Scott Delp, Ph.D. - US grants

This NYI award will fund research in the area of computer simulation of human movement. The research will focus on methods to scale nominal models of the lower limbs to arrive at accurate estimates of the moment arms of various sized individuals. The predictions will be verified using cadaver measurements. Similar scaling studies for children will be based on MRI measurements and also verified through cadaver studies. These scaling studies are important since movement simulations require accurate estimates of moment arms. The model also will be used to simulate a number of surgeries including tendon transfers, osteotomies, and joint replacements. The NYI award also will help the investigator create a teaching laboratory that focuses on biodynamic simulations. Three courses based on the simulation laboratory; namely, Biomechanics of Movement, Biodynamics Simulation Laboratory, and Advanced Biodynamics, will be developed.

DESCRIPTION (Adapted from the Applicant's Abstract): The long-term goal of this work is to provide a scientific basis for treating crouch gait, the most common movement abnormality among persons with spastic diplegia. Crouch gait is characterized by persistent flexion of the knee, which is usually accompanied by excessive flexion, adduction and internal rotation of the hip. It is an extremely inefficient means of locomotion; if not corrected it often leads to joint degeneration. Although surgeries such as: (i) lengthening of the hamstrings; (ii) derotation of the femur; and (iii) transfer of the rectus femoris are frequently performed in an attempt to improve musculoskeletal function, the outcomes of these surgeries are unpredictable and often unsatisfactory, leaving patients with serious, life-long physical limitations. The purpose of this work is to develop biomechanical models that explain the causes of crouch gait and predict the functional consequences of commonly used surgical treatments. Aim #1 will examine the causes of excessive knee flexion. Based on preliminary data, it is hypothesized that many persons with crouch gait have hamstrings that are of adequate length for normal walking. This hypothesis will be tested by estimating the lengths of the hamstrings during normal and crouch gait using detailed musculoskeletal models in conjunction with measured three-dimensional joint kinematics. This analysis may enable a more rational basis to be developed for deciding who should, and should not, have hamstring lengthening. Aim #2 will examine the causes and corrections of excessive internal rotation of the hip. The rotational moment arms of the muscles crossing the hip will be determined based on quantitative anatomical studies and three dimensional computer models constructed from magnetic resonance images. The computer models will be used to analyze the effects of femoral deformities and derotational osteotomies on the rotational function of the muscles. This analysis is intended to reveal the best means to restore normal limb rotation. Aim #3 will compare measurements taken from subjects after transfer of the rectus femoris to a computer simulation of the surgery. This study will also determine if subjects use the rectus femoris as a knee flexor after it is transferred behind the knee. It is suggested by the applicant that although multijoint movement abnormalities such as crouch gait are exceptionally complex, the development of quantitative descriptions of muscle function in normal, deformed, and surgically altered limbs, as proposed here, is an important and necessary first step toward designing more effective treatments.

The goal of this work is to provide a scientific basis for treating stiff-knee gait, a common movement abnormality among children with cerebral palsy. The rectus femoris transfer surgery is frequently performed to treat stiff-knee gait. In this surgery, the distal tendon of the rectus femoris is detached from the patella and reattached to one of several sites posterior to the knee. This surgery is thought to convert the muscle from knee extensor to a knee flexor thereby allowing the muscle to assist knee flexion during walking. However, the surgical outcomes are inconsistent, and the in vivo function of the transferred rectus femoris is unknown. We hypothesize that remodeling of the rectus femoris after surgery results in adhesions to surrounding tissues in some patients, which dramatically alters the postoperative function of the muscle and degrades the outcome of the surgery. This study will use biomechanical models, magnetic resonance imaging techniques, and experimental measurements on human subjects to evaluate the function of the rectus femoris following tendon transfer. Aim 1 will assess the potential of the rectus femoris to flex the knee after transfer. Biomechanical models, constructed from MR images, will provide quantitative descriptions of musculoskeletal geometry for 8-12 patients undergoing rectus femoris transfers to different surgical sites. This work will determine the differences in the knee flexion moment arm of the rectus femoris after transfer to different sites. Aim 2 will determine if forces at the proximal and distal tendons of the rectus femoris are equal after transfer, or if force at the distal tendon is disrupted after the surgery. Hip and knee moments, generated by selective stimulation of the rectus femoris, will be measured in subjects with stiff-knee gait. Tendon forces will be estimated by dividing the measured moments by the corresponding moment arms obtained from the MRI-based models. Aim 3 will use cine phase-contrast MR imaging to analyze and visualize the relative motions of the rectus femoris and the surrounding muscles in vivo. The extent of scar formation following rectus femoris transfer will be investigated, and the effects of adhesions on the mechanics of the muscle will be assessed. Aim 4 will examine how postoperative function of the rectus femoris influences knee motion during gait. The success of this work will result in better, more predictable treatment outcomes.

DESCRIPTION: (provided by applicant) The goal of this work is to establish a scientific basis for treating crouch gait, one of the most common movement abnormalities among children with cerebral palsy. Crouch gait is characterized by persistent flexion of the knee, which is usually accompanied by excessive flexion, adduction and internal rotation of the hip. It is a highly inefficient means of locomotion; if not corrected it often leads to bone deformities, osteoarthritis, and senous, life-long physical limitations. Musculoskeletal surgeries are frequently performed in an effort to improve knee extension. However, it is extremely difficult to predict which patients will benefit from these procedures, in part, because the factors that cause excessive knee flexion are not known. This study will result in dynamic simulations of crouch gait that can identify the underlying biomechanical sources of patients' movement abnormalities and predict the functional consequences of common interventions. Aims 1-3 will rigorously examine several hypothesized causes of crouch gait, including spasticity of the hamstrings and psoas muscles, weakness of the hip, knee, and ankle extensors, and deformities of the femur and tibia. Forward dynamic simulations will be created that reproduce experimentally measured movement kinematics and kinetics of 45 subjects with cerebral palsy. A series of simulations of varying complexity will be analyzed to determine which of the hypothesized causes of crouch gait contribute to each subject's persistent knee flexion. Aim 4 will evaluate the utility of our dynamic analyses for guiding treatment decisions by determining whether subjects have good (poor) outcomes when interventions are performed that agree (disagree) with the results of our simulations. This work will provide improved guidelines for deciding which patients should undergo surgical lengthening of the hamstrings and/or psoas muscles to correct crouch gait, and which patients are likely to benefit more from other treatments, such as strengthening exercises or bracing. Although multijoint movement abnormalities such as crouch gait are exceptionally complex, the development of dynamic simulations that elucidate how muscles contribute to movements in normal, impaired, and surgically altered limbs, as proposed here, is an important and necessary next step toward designing more effective treatments.

[unreadable] DESCRIPTION (provided by applicant): The goal of this work is to establish a scientific basis for treating stiff-knee gait in persons with cerebral palsy. Stiff-knee gait is a prevalent, debilitating movement abnormality that is characterized by inadequate knee flexion during the swing phase. The reputed cause of stiff-knee gait is over-activity of the rectus femoris muscle; hence, individuals with stiff-knee gait frequently undergo rectus femoris transfer surgery. Unfortunately the outcomes of these procedures are inconsistent. The PIs hypothesize that several factors, in addition to over-activity of the rectus femoris, contribute to the diminished knee flexion that is commonly observed. If these factors were identified, and if methods were developed to determine which of these factors contribute to an individual's abnormal gait, then treatments for stiff-knee gait could be designed more effectively. [unreadable] [unreadable] Muscle-actuated, forward dynamic simulations will be created that reveal the causes of diminished knee flexion in subjects with stiff-knee gait and explain the functional consequences of rectus femoris transfer surgery. Aim 1 will identify the factors that can cause stiff-knee gait, and will generate an algorithm to uncover which of these factors contribute to diminished knee flexion in patients with a range of neuromusculoskeletal impairments. Aim 2 will determine why some individuals walk with diminished swing-phase knee flexion following surgeries performed to correct other aspects of their abnormal gait. Aim 3 will examine the potential of the rectus femoris to augment knee flexion after tendon transfer surgery. Aim 4 will test the validity and utility of the simulation-based analyses for guiding treatment decisions by determining whether patients have better outcomes when interventions are performed that address the biomechanical causes of their stiff-knee gait, as determined using the simulations. This work will provide new guidelines deciding which patients should undergo rectus femoris transfer surgery, and which patients are likely to benefit more from other treatments, such as bracing, strengthening exercises, injections of neuromuscular blocking agents, or other surgeries. [unreadable] [unreadable] The success of this project will result in the first (rigorously tested) simulation-based system to aid treatment planning for stiff-knee gait, and will hopefully result in better, more predictable surgical outcomes. Although multi-joint movement abnormalities such as stiff-knee gait are exceptionally complex, the development of dynamic simulations that elucidate the causes of patients' movement abnormalities is an important and necessary next step toward designing more effective treatments. [unreadable] [unreadable]

The goal of this work is to establish a scientific basis for treating crouch gait, one of the most common movement abnormalities among children with cerebral palsy. Crouch gait is characterized by persistent flexion of the knee. It is an inefficient means of locomotion;if not corrected it often leads to bone deformities and serious, life-long physical limitations. We will develop predictive tools to determine the probability that each of the common treatments for crouch gait will improve a subject's excess knee flexion. For each treatment, we will identify a set of predictive variables. We will then identify all subjects from a large database of patients who received the surgery and use these subjects'outcome data to choose a weighting coefficient for each of the biomechanics based predictive variables. We will assess the predictive accuracy when applied to a different set of subjects using cross validation. These tools will predict whether or not a particular surgery will improve a patient's excessive knee flexion. Our preliminary work suggests that 80% prediction accuracy is feasible The success of this project will result in rigorously tested methods to aid treatment planning for crouch gait, and will hopefully produce better, more predictable treatment outcomes. Although multi-joint movement abnormalities such as crouch gait are exceptionally complex, the development of statistical models that predict which patients will benefit from specific surgical treatments is an important and necessary step toward designing more effective treatments.

DESCRIPTION (provided by applicant): Computer simulation has revolutionized science and engineering. The PIs propose to establish a National Center for Simulation in Rehabilitation Research to bring the power of simulation to medical rehabilitation. The Center will provide robust tools for simulating human motion, enabling investigators to answer clinical questions that cannot be solved with experimental studies alone. Hundreds of scientists affiliated with the Center will focus on critical areas of rehabilitation, including stroke, spinal cord injury, cerebral palsy, osteoarthritis, prosthetics, orthotics, and sports medicine. The PIs will pursue the following specific aims. 1. Develop and disseminate advanced technology for the simulation of musculoskeletal dynamics and accelerate its appropriate use in rehabilitation research. 2. Award seed grants to innovative and meritorious pilot projects that employ simulation tools to identify the mechanisms underlying movement disorders and improve the efficacy of rehabilitation. 3. Attract and train 20 talented scholars from computer science, biomechanics, physical therapy and other fields to become experts in simulation and the needs of individuals with disabilities. 4. Teach over 300 rehabilitation scientists to create and test biomechanical simulations and correctly interpret their results during intensive multi-day workshops. 5. Introduce over 1000 rehabilitation specialists to the value and limitations of musculoskeletal simulation through hands-on training at national and international conferences. 6. Create web-based tutorials, lectures, and a formal textbook to promote the appropriate use of simulation in rehabilitation research. The development and dissemination of open source software for the rehabilitation community addresses a critical barrier to progress in the field. The software will be designed in close partnership with rehabilitation scientists and physicians to address critical, clinically motivated questions across the spectrum of medical rehabilitation. There is a rapidly growing community of rehabilitation scientists eager to engage in this project; thus, the timing for establishing this core resource is excellent.

CORE 4: TRAINING BIOCOMPUTATIONAL SCIENTISTS Simbios has a strong track record in training graduate students and post-doctoral fellows. We have access to training grant support and training programs across all the disciplines contributing to this Center. We have a five-year track record of training students and post-docs as part of Simbios (Table ES.1). We equip biomedical computation students and post-doctoral fellows with a broad set of tools, so that they are prepared to approach unanticipated research challenges that arise in the future. For students, we have engaged 25 of them (including 11 women. Table 4.1, Section 4) in the core Simbios mission, and supplemented their experience with new classes, workshops, and seminars. We are thus well positioned to continue as a leader in the education of biomedical computation scientists. For post-docs, we have created a community of junior scholars devoted to physics-based simulation called the "Simbios Distinguished Post-doctoral Program" which has supported 15 fellows, some of whom have already begun independent faculty positions in the field and continue to be Simbios collaborators

The user community for SIMTK is large. The DBPs during our first five years demonstrate the breadth of problems for which physics-based modeling and simulation are critical. The RNA folding and protein folding/misfolding DBPs included structural biologists, physical chemists, physicists, molecular biologists, physicians and biochemists. The myosin dynamics DBP included biochemists, biomechanical engineers, geneticists, chemists, biophysicists, and structural biologists. The neuromuscular dynamics DBP included orthopedic surgeons, biomechanical engineers, neurologists, and physical therapists. The cardiovascular fluid dynamics DBP included vascular surgeons, cardiologists, vascular biologists, and bioengineers. The three new DBPs, summarized under Core 2 similarly engage a broad spectrum of scientists, including structural biologists, neurologists, electrical engineers, biochemists, pharmaceutical chemists, physicians, computer scientists and biomedical engineers. These users will again provide appropriate "pull" on SIMTK to ensure that it has relevant and useful capabilities. We have disseminated the fruits of Simbios research and development through traditional publications (over 150 papers), presentations at conferences, workshops, and via the materials on our simtk.org website.

DESCRIPTION (provided by applicant): Neurological disorders and injuries frequently result in spasticity and involuntary muscle contractions that interfere with speech, movement and activities of daily living. Current approaches to reduce spasticity, such as selective dorsal rhizotomy, oral medications, or injections of botulinum toxin, have important limitations. There is currently no treatment for spasticity that provides targeted, tunable and rapidly reversible inhibition of unwanted muscle activity. This proposal describes a novel strategy to achieve precise, tunable, and reversible inhibition of motor neuron and muscle activity in vivo. Our approach is made possible by the recent discovery of optogenetics, a technique that enables the use of light-sensitive ion channels to facilitate optical excitation or inhibition of mammalian neurons. Our laboratory has recently reported the first use of optogenetics to excite sciatic nerve motor neurons in transgenic mice. However, it is unknown if a similar approach can be used to inhibit motor neuron activity using halorhodopsin (NpHR), a light sensitive ion channel that hyperpolarizes axons in response to light and impedes action potential propagation along a nerve. Our first aim is to apply light to the sciatic nerve of transgenic mice that express NpHR in their motor neuron axons. We will ask whether the application of light can block action potentials induced by electric nerve stimulation in a tunable and reversible manner and thereby prevent muscle contraction. Our preliminary results in anesthetized animals indicate that such inhibition is not only possible, but is repeatable and robust. After we characterize the light delivery properties required for effective inhibition, we will build an implantable light delivery device an use it to achieve motor neuron inhibition in freely moving transgenic mice. Finally, as a necessary prelude to future clinical use of this technology, we will translate this work to non-transgenic animals. For this purpose we will use state of the art gene therapy techniques to selectively deliver the NpHR gene to targeted motor neuron pools in a manner that is readily translated to humans. This project will lay the foundation for optogenetic modulation of activity i motor neurons and other peripheral nerves. Successful demonstration of optical inhibition will provide a proof-of-principle for a novel treatment for spasticity. Various components of this project, including techniques for opsin delivery to target nerves and the development of an implantable light delivery device, are needed to enable optogenetics-based treatments for a variety of other disorders.

Physics-based simulation provides a powerful framework for understanding biological form and function. Simulations help biomedical researchers understand the physical constraints on biological systems as they engineer novel drugs, new diagnostics, synthetic tissues, medical devices, and surgical interventions. Unfortunately, after individual investigators publish their work, the software, underlying models and simulation results are often inaccessible, limiting progress. In 2005, we launched Simtk.org, a website to develop and share biosimulation tools, models, and data, to address this issue. Simtk.org now supports over 23,000 researchers globally and over 600 projects. Members use Simtk.org to develop and build communities around their simulation tools, fulfill the data sharing responsibilities for their grants, and create new types of collaborations. Thus, Simtk.org is a model for how national infrastructure can be created for scientific subdisciplines. Our users have helped us identify key opportunities to further increase our impact: facilitating the faithful reproduction of simulation results across investigators, capturing the full nuances of a simulation study, and creating incentives for researchers to provide access to their data, models and software. Thus, we propose a plan to extend Simtk.org and build new ways for scientific data sharing. First, we will create prototype tools that enable others to easily reproduce and extend biosimulation results. Virtual machines and cloud computing services allow researchers to capture and share a snapshot of their complete computing environment, enabling unprecedented fidelity in sharing. We will explore how these could be used within Simtk.org to reproduce biosimulation studies. Second, we will dramatically increase the integration of simulation-based scientific publications with their supporting data. Working with the editors of major journals for biosimulation publications, we will extend Simtk.org to directly link published articles to the resources needed to understand, examine and replicate their results. Third, we will improve the robustness and usability of Simtk.org, so that the growing community who rely on the site can continue their work with full confidence in the availability and stability of the resource. Fourth, we will develop techniques using social networking to motivate and reward researchers who actively participate in the Simtk.org community. Simtk.org is a middle- sized social network with the key advantage of a specific focus and purpose. As such, it serves as a model for how to build a scientific resource for sharing and community building. The proposed enhancements to Simtk.org will significantly accelerate research in biosimulation and offer a new model for interactions between scientific researchers.

The Simbios National Center for Biomedical Computing is singularly focused on the application of physics-based simulation to problems in biology and medicine. Physics-based simulation provides a powerful framework for understanding biological form and function. Simulations are used by biologists to study macromolecular assemblies and by clinicians to analyze disease mechanisms and therapeutic options. Simulations help biomedical researchers understand the physical constraints on these systems as they engineer novel drugs, drug delivery mechanisms, synthetic tissues, medical devices, or surgical interventions. In the previous grant period, we have created a simulation toolkit (SimTK) that enables users to create and visualize accurate models and simulations of biological structures at all scales, from molecules to organisms. SimTK is an extensible, open source, freely available software package. Domain specific application software packages (built using SimTK) are distributed on our webportal, simtk.org, which has more than 8000 users and 300 software or data projects, and has enabled scientific impact in RNA biology, myosin biomechanics, protein folding, cardiovascular fluid dynamics, and neuromuscular biomechanics. SimTK applications have been developed and tested in close collaboration with biomedical scientists to ensure its utility and accuracy. In this proposal, we outline a plan to introduce three exciting new driving biological problems focusing on (1) the dynamics of neural prostheses, (2) the dynamics of cell shape, and (3) the dynamics of drug target macromolecules. We have identified the computational research challenges critical to these fields, and have assembled a strong team of researchers in modeling, simulation and visualization of biological structures that will address these challenges. The software engineering effort is lead by experienced professionals, who have previously developed and delivered complex software packages to thousands of users. Our dissemination plan includes workshops that will move online, a nationally recognized magazine, and technologies for community-based user support. Our initial efforts have established the vision, facilities, training environment, administrative organization, and collaborative relationships required for the success of Simbios. In the context of other centers focusing on complementary elements of biomedicine, our center is focused on the physical reality of biological structures. It thus provides a critical piece of a national biomedical computing infrastructure.

Mobility is essential for human health. Regular physical activity helps prevent heart disease and stroke, relieves symptoms of depression, and promotes weight loss. Unfortunately, many conditions, such as cerebral palsy, osteoarthritis, and obesity, limit mobility at an enormous personal and societal cost. While vast amounts of data are available from hundreds of research labs and millions of smartphones, there is a dearth of methods for analyzing this massive, heterogeneous dataset. We propose to establish the National Center for Mobility Data Integration to Insight (the Mobilize Center) to overcome the data science challenges facing mobility big data and biomedical big data in general. Our preliminary work identified four bottlenecks in data science, which drive four Data Science Research Cores. The Cores include Biomechanical Modeling, Statistical Learning, Behavioral and Social Modeling, and Integrative Modeling and Prediction. Our Cores will produce novel methods to integrate diverse modeling modalities and gain insight from noisy, sparse, heterogeneous, and time-varying big data. Our data-sharing consortia, with clinical, research, and industry partners, will provide mobility data for over ten million people. Three Driving Biomedical Problems will focus and validate our data science research. The Mobilize Center will disseminate our novel data science tools to thousands of researchers and create a sustainable data-sharing consortia. We will train tens of thousands of scientists to use data science methods in biomedicine through our in-person and online educational programs. We will establish a cohesive, vibrant, and sustainable National Center through the leadership of an experienced executive team and will help unify the BD2K consortia through our Biomedical Computation Review publication and the Simtk.org resource portal. The Mobilize Center will lay the groundwork for the next generation of data science systems and revolutionize diagnosis and treatment for millions of people affected by limited mobility.

DESCRIPTION (provided by applicant): Mobility is essential for human health. Regular physical activity helps prevent heart disease and stroke, relieves symptoms of depression, and promotes weight loss. Unfortunately, many conditions, such as cerebral palsy, osteoarthritis, and obesity, limit mobility at an enormous personal and societal cost. While vast amounts of data are available from hundreds of research labs and millions of smartphones, there is a dearth of methods for analyzing this massive, heterogeneous dataset. We propose to establish the National Center for Mobility Data Integration to Insight (the Mobilize Center) to overcome the data science challenges facing mobility big data and biomedical big data in general. Our preliminary work identified four bottlenecks in data science, which drive four Data Science Research Cores. The Cores include Biomechanical Modeling, Statistical Learning, Behavioral and Social Modeling, and Integrative Modeling and Prediction. Our Cores will produce novel methods to integrate diverse modeling modalities and gain insight from noisy, sparse, heterogeneous, and time-varying big data. Our data-sharing consortia, with clinical, research, and industry partners, will provide mobility data for over ten million people. Three Driving Biomedical Problems will focus and validate our data science research. The Mobilize Center will disseminate our novel data science tools to thousands of researchers and create a sustainable data-sharing consortium. We will train tens of thousands of scientists to use data science methods in biomedicine through our in-person and online educational programs. We will establish a cohesive, vibrant, and sustainable National Center through the leadership of an experienced executive team and will help unify the BD2K consortia through our Biomedical Computation Review publication and the Simtk.org resource portal. The Mobilize Center will lay the groundwork for the next generation of data science systems and revolutionize diagnosis and treatment for millions of people affected by limited mobility.

Our data science research is tied to three Driving Biomedical Problems that we will use to focus, test, and validate the data science methods. These problems represent major opportunities to improve human mobility and health. We propose the following specific aims: 1. Data Science Cores: Develop and disseminate data science tools to overcome several of the major challenges in exploiting big data in biomedical research. In particular, we will: a. Develop robust, flexible, and automated optimization tools for generating personalized biomechanical models and simulations from diverse experimental movement data. b. Create techniques to make predictions and classifications and identify insightful correlations from large sets of noisy, sparse, and complex data, whether discrete or time-varying. c. Develop tools to model the role of behavioral and social dynamics in human health based on information collected with smartphones and wearable activity monitors. d. Establish machine learning systems that integrate diverse data sources and modeling approaches to aid clinical decision-making and transparently communicate with clinicians

ADMINISTRATION The success of the Center's efforts will require effective administration and management (Figure 1.3). We have developed a carefully tuned administrative plan to maintain cohesion of the investigators, students, and staff within Stanford and among our clinical, research, and industry partners and the broader biomedical research community. An eight-member Executive Committee, led by PI Scott Delp, will set the vision of the Center, manage its operation, and ensure that milestones are met. The members of the Committee have worked together extensively in running the successful Simbios and NCSRR National Centers. They bring expertise in mobility and data science research, as well as in training, dissemination, and software development. The Center includes several additional investigators, all members of the Stanford faculty, who will help lead the research in the Cores and DBPs. An experienced professional staff will ensure that training and dissemination aims are met. Our consultants will help focus our driving questions and serve as beta testers for our software and apps. Postdoctoral fellows and graduate students, who will be co-advised by the Core and DBP investigators, will conduct research and serve as additional glue to create a cohesive Center. Finally, we will establish an eight-member External Advisory Committee to evaluate and guide the Center. The Center will conduct weekly Executive Committee and developer meetings, quarterly consortia meetings for the key players in our DBPs, and a yearly combined all-hands meeting and symposium. We will also hold a weekly seminar series for all Center affiliates. Our administrative plan will ensure that the Center has the computing resources and data infrastructure required by the Cores, DBPs, and data consortia.

Mobility is essential for human health. Regular physical activity helps prevent heart disease and stroke, relieves symptoms of depression, and promotes weight loss. Unfortunately, many conditions, such as cerebral palsy, osteoarthritis, and obesity, limit mobility at an enormous personal and societal cost. While vast amounts of data are available from hundreds of research labs and millions of smartphones, there is a dearth of methods for analyzing this massive, heterogeneous dataset. We propose to establish the National Center for Mobility Data Integration to Insight (the Mobilize Center) to overcome the data science challenges facing mobility big data and biomedical big data in general. Our preliminary work identified four bottlenecks in data science, which drive four Data Science Research Cores. The Cores include Biomechanical Modeling, Statistical Learning, Behavioral and Social Modeling, and Integrative Modeling and Prediction. Our Cores will produce novel methods to integrate diverse modeling modalities and gain insight from noisy, sparse, heterogeneous, and time-varying big data. Our data-sharing consortia, with clinical, research, and industry partners, will provide mobility data for over ten million people. Three Driving Biomedical Problems will focus and validate our data science research. The Mobilize Center will disseminate our novel data science tools to thousands of researchers and create a sustainable data-sharing consortia. We will train tens of thousands of scientists to use data science methods in biomedicine through our in-person and online educational programs. We will establish a cohesive, vibrant, and sustainable National Center through the leadership of an experienced executive team and will help unify the BD2K consortia through our Biomedical Computation Review publication and the Simtk.org resource portal. The Mobilize Center will lay the groundwork for the next generation of data science systems and revolutionize diagnosis and treatment for millions of people affected by limited mobility.

The National Center for Simulation in Rehabilitation Research (NCSRR) is organized to enable the medical rehabilitation research community to optimally benefit from OpenSim, an advanced software system that allows researchers to create patient-specific dynamic simulations of movement. Our director, Dr. Scott Delp, has a decade?s worth of experience successfully running NIH National Centers. Dr. Delp will lead the professional staff, external advisors, collaborators, awardees, and interactions with NIH staff to achieve the following specific aims: 1. Implement an effective administrative plan to manage the Center?s diverse activities. 2. Communicate with and integrate input from our Scientific Advisory Board, our consultants, and NIH staff. 3. Manage the selection and progress of pilot projects and visiting scholars. 4. Set goals and assess the Center?s progress towards achieving these goals. 5. Evaluate the quality and utility of the Center?s programs. 6. Create and maintain a vibrant, sustainable community of experts in rehabilitation research and modeling and simulation. 7. Promote the Center?s resources and activities. 8. Collaborate with other medical rehabilitation research centers funded under this FOA. Excellent administration and management teams will be essential for coordinating the many diverse activities of the NCSRR. The extensive and diverse experience of our leadership team, and our track record of administrative successes, will ensure the success of the interdisciplinary NCSRR.

Our overall goal is to develop and disseminate the world?s most advanced technology for the simulation of musculoskeletal dynamics. Over the past five years, the National Center for Simulation in Rehabilitation Research (NCSRR) has equipped the rehabilitation research community with OpenSim, powerful open source software for simulating human movement. Our user base includes thousands of researchers who are using the software to address fundamental questions in movement science and rehabilitation related to stroke, spinal cord injury, cerebral palsy, prosthetics, sports injury, osteoarthritis, and many other areas. To ensure that this research core is optimally responsive to the needs of the research community, we have performed extensive surveys and assessments. The aims we propose are based on these needs, including a set of innovations designed to accelerate the progress of rehabilitation clinicians and engineers in developing more effective interventions. In particular, our specific aims include: 1. Develop tools for predictive simulation. Predictive simulation will enable researchers to simulate motions for which experimental data are not available (e.g., predicting the effects of a treatment). 2. Enable simulation of wearable robotic systems. Design of wearable robotic systems is greatly enhanced by simulating the interactions between robotic systems and human neuromuscular dynamics. 3. Create resources for the validation of models and simulations. The NCSRR will establish guidelines for verification and validation of biomechanical models and simulations that researchers, clinicians, reviewers, and others can adopt to evaluate the accuracy and credibility of modeling studies. We will measure whether we achieve our overall goal by rigorously comparing the performance and features of OpenSim with other software; tracking the number, location, and application areas of users with an instrumented website (www.simtk.org); and testing the usability of the software by the rehabilitation community.

The National Center for Simulation in Rehabilitation Research (NCSRR) has developed a highly effective set of programs for promoting the use of engineering simulations in rehabilitation research. Our software and educational programs have introduced thousands of movement scientists, rehabilitation specialists, physicians, and surgeons to the use of simulations within rehabilitation research. We have increased the level of expertise among our users and now have a small cadre of experts who are independently promoting and teaching others how to use biomechanical simulation and modeling tools. We will continue to support and refine our dissemination and education programs to meet the needs of our active and growing community of researchers and clinicians. We have the following specific aims: 1. Train 500 rehabilitation scientists in the use of simulations via intensive multiday advanced user workshops. 2. Introduce over 1000 movement scientists, rehabilitation specialists, physicians, and surgeons to the strengths and limitations of musculoskeletal simulation through hands- on tutorials at conferences. 3. Disseminate the latest research on biomechanical simulations within rehabilitation research to over 2000 individuals through symposia and our webinar program. 4. Curate a collection of biomechanical models and simulations on the community-building website Simtk.org. 5. Enhance our diverse set of online resources to support new technology developments and the expanding rehabilitation research community. 6. Create a series of 20 training videos to promote the appropriate use of simulations in movement science. We have designed a multi-faceted promotion and dissemination plan that combines online resources and in-person training, and includes resources for users of all types, from beginners to advanced software developers. Our plan exploits state-of-the art information technologies to engage and respond to our extensive user community, and to foster new collaborations.

To maximize the impact of simulations on rehabilitation research, the National Center for Simulation in Rehabilitation Research (NCSRR) will attract and engage researchers from multiple areas and accelerate the research of early-career scientists. Our Pilot Studies program will initiate and sustain research projects in complementary rehabilitation areas with scientists from a wide range of institutions. We will build on our success so far in using the Pilot Studies to seed larger proposals (e.g., NIH R01s, NSF CAREER Awards), attract new researchers to rehabilitation science, and support ongoing collaborations. Our specific aims are as follows: 1. Award seed grants to innovative and meritorious Pilot Studies to accelerate the use of simulations in rehabilitation research and to advance medical rehabilitation. 2. Mentor and support Pilot Project winners during and after the one-year term of their award. 3. Support high caliber applicants with Outstanding Researcher and Travel Awards. 4. Evaluate the impact of the Pilot Projects and Outstanding Researcher and Travel Awards. Through the Pilot Studies and the Outstanding Researcher and Travel Awards, we anticipate that over the next five years we will be able to initiate and advance at least 65 new rehabilitation research projects that benefit from biomechanical modeling and simulation. These projects will serve as examples of what is possible to achieve with biomechanical simulations and provide a foundation for the use of simulations in future research in those areas, while simultaneously fostering ideas for successful future NIH grant applications. Our program is designed to be much more than just a distribution of seed funds to investigators. It is an integral component for building a sustainable network of experts across all of rehabilitation medicine. Through mentoring and on-going interactions with our awardees, we will continue to develop their expertise and encourage them to share their knowledge to produce a national network of experts that supports the mission of NCSRR.

To achieve the mission of the National Center for Simulation in Rehabilitation Research (NCSRR), we will engage a broad array of researchers in our activities. We have designed two programs?the Visiting Scholars program and the OpenSim Fellows program?to attract and engage researchers outside of the Stanford community who offer complementary expertise in a variety of rehabilitation areas. They will extend our impact in promoting the use of simulations in rehabilitation research. Our aims are as follows: 1. Attract talented scholars from computer science, biomechanics, robotics, physical therapy, rehabilitation and other fields to become experts in biomechanical simulation and the needs of the rehabilitation community through a Visiting Scholars program. 2. Mentor and support Visiting Scholars before, during, and after their eight-week visit. 3. Build an international community of simulation experts who are actively involved in expanding the impact of NCSRR through our OpenSim Fellows Program. 4. Evaluate the impact of the Visiting Scholar and OpenSim Fellows programs. The Visiting Scholars program provides an extended training and networking opportunity for researchers seeking to gain expertise in applying simulations to rehabilitation-related research, while the OpenSim Fellows program offers modeling and simulation experts the opportunity to help shape and expand the field. Together, these programs will create a pipeline for training and engaging researchers in the application of biomechanical simulations to rehabilitation research. The Visiting Scholars program will initiate and advance at least 20 new rehabilitation research projects that benefit from biomechanical simulations, training a new generation of simulation experts. The OpenSim Fellows program will recognize and incentivize 50 experts in the field to engage in the advancement and promotion of simulations in rehabilitation research. The result is a robust, dynamic community of experts that will sustain and grow the Center?s software, training resources, and rehabilitation research applications.

Physics-based simulations provide a powerful framework for understanding biological form and function. They harmonize heterogeneous experimental data with real-world physical constraints, helping researchers understand biological systems as they engineer novel drugs, new diagnostics, medical devices, and surgical interventions. The rise in new sensors and simulation tools is generating an increasing amount of data, but this data is often inaccessible, preventing reuse and limiting scientific progress. In 2005, we launched SimTK, a website to develop and share biosimulation tools, models, and data, to address these issues. SimTK now supports 62,000+ researchers globally and 950+ projects. Members use it to meet their grants? data sharing responsibilities; experiment with new ways of collaborating; and build communities around their datasets and tools. However, challenges remain: many researchers still do not share their digital assets due to the time needed to prepare, document, and maintain those assets, and since SimTK hosts a growing number of diverse digital assets, the site now also faces the challenge of making these assets discoverable and reusable. Thus, we propose a plan to extend SimTK and implement new solutions to promote scientific data sharing and reuse. First, we will maintain the reliable, user-friendly foundation upon which SimTK is built, continuing to provide the excellent support our members expect and supporting the site?s existing features for sharing and building communities. Second, we will implement methods to establish a culture of model and data sharing in the biomechanics community. We will encourage researchers to adopt new habits, making sharing part of their workflow, by enabling the software and systems they use to automatically upload models and data to SimTK via an application programming interface (API) and by recruiting leading researchers in the community to serve as beta testers and role models. Third, we will create tools to easily replicate and extend biomechanics simulations. Containers and cloud computing services allow researchers to capture and share a snapshot of their computing environment, enabling unprecedented fidelity in sharing. We will integrate these technologies into SimTK and provide custom, easy-to-use interfaces to replicate and extend simulation studies. Lastly, we will develop a metadata standard for models and data for the biomechanics community, increasing reusability and discoverability of the rich set of resources shared on SimTK. We will use the new standard on SimTK and fill in the metadata fields automatically using natural language processing and machine learning, minimizing the burden and inaccuracies of manual metadata entry. We will evaluate our success in achieving these aims by tracking the number of assets shared and the frequency they are used as a springboard to new research. These changes will accelerate biomechanics research and provide new tools to increase the reusability and impact of shared resources. By lowering barriers to data sharing in the biosimulation community, SimTK will continue to serve as a model for how to create national infrastructure for scientific subdisciplines.

Mobile technology is poised to revolutionize rehabilitation research, but the infrastructure and training to support researchers in designing effective studies, collecting and analyzing data, and translating findings to improve care has not kept pace with the mobile technology industry. Our Center for Reliable Sensor Technology-Based Outcomes for Rehabilitation (RESTORE Center) will establish vital research infrastructure and training that enables rehabilitation scientists to use mobile sensors to monitor a diverse set of real-world outcomes. We will accomplish this by integrating expertise from bioengineering, statistics, computer science, mobile health, and clinical rehabilitation. Our mission is to launch a world-wide collaboration involving hundreds of researcher teams to collect and share real world data on rehabilitation outcomes. To achieve this, we will: 1. Provide state-of-the-art software to convert wearable sensor data into meaningful outcome metrics, create a data sharing repository with a vast set of movement and outcome data, and develop advanced data science tools to gain insight from real-world rehabilitation datasets. 2. Train thousands of rehabilitation scientists to use mobile technology for research through bootcamps, conference-based tutorials, an online knowledgebase, and massive open online courses. 3. Attract and train talented scholars from physical therapy, physiatry, computer science, biomechanics, and other fields to become experts in mobile technology and the needs of the rehabilitation community. 4. Award 65 seed grants to innovative and meritorious projects to accelerate the use of mobile technology in rehabilitation research and advance patient care. 5. Encourage the appropriate use of mobile technology in rehabilitation research and foster interdisciplinary collaborations through a multi-faceted promotion effort. Our broad outreach program will expand the group of over 14,000 researchers who are currently using our resources. 6. Establish a cohesive, vibrant, and sustainable Medical Rehabilitation Research Resource Center through the leadership of an experienced executive team that will manage the Center?s activities. By providing high-quality, in-demand, and open-source software tools, our Center will enable a collaboration of unprecedented scale between bioengineers, physical therapists, computer scientists, patients, physicians, and others focused on rehabilitation. Our training efforts will create a new generation of rehabilitation scientists who are fluent in the strengths and challenges of mobile technology. Our Center will be run by a tightly integrated clinical and engineering team, enabling us to appreciate the needs and goals of patients, recruit participants to our studies, and rapidly create valuable new technology. Together with the RESTORE Center community, we will achieve the potential of mobile technology to monitor real world function and improve care for conditions including stroke, Parkinson?s disease, osteoarthritis, frailty, cerebral palsy, and low back pain.

Mobile technology is poised to revolutionize rehabilitation research, but the infrastructure and training to support researchers in designing effective studies, collecting and analyzing data, and translating findings to improve care has not kept pace with the mobile technology industry. Our Center for Reliable Sensor Technology-Based Outcomes for Rehabilitation (RESTORE Center) will establish vital research infrastructure and training that enables rehabilitation scientists to use mobile sensors to monitor a diverse set of real-world outcomes. We will accomplish this by integrating expertise from bioengineering, statistics, computer science, mobile health, and clinical rehabilitation. Our mission is to launch a world-wide collaboration involving hundreds of researcher teams to collect and share real world data on rehabilitation outcomes. To achieve this, we will: 1. Provide state-of-the-art software to convert wearable sensor data into meaningful outcome metrics, create a data sharing repository with a vast set of movement and outcome data, and develop advanced data science tools to gain insight from real-world rehabilitation datasets. 2. Train thousands of rehabilitation scientists to use mobile technology for research through bootcamps, conference-based tutorials, an online knowledgebase, and massive open online courses. 3. Attract and train talented scholars from physical therapy, physiatry, computer science, biomechanics, and other fields to become experts in mobile technology and the needs of the rehabilitation community. 4. Award 65 seed grants to innovative and meritorious projects to accelerate the use of mobile technology in rehabilitation research and advance patient care. 5. Encourage the appropriate use of mobile technology in rehabilitation research and foster interdisciplinary collaborations through a multi-faceted promotion effort. Our broad outreach program will expand the group of over 14,000 researchers who are currently using our resources. 6. Establish a cohesive, vibrant, and sustainable Medical Rehabilitation Research Resource Center through the leadership of an experienced executive team that will manage the Center?s activities. By providing high-quality, in-demand, and open-source software tools, our Center will enable a collaboration of unprecedented scale between bioengineers, physical therapists, computer scientists, patients, physicians, and others focused on rehabilitation. Our training efforts will create a new generation of rehabilitation scientists who are fluent in the strengths and challenges of mobile technology. Our Center will be run by a tightly integrated clinical and engineering team, enabling us to appreciate the needs and goals of patients, recruit participants to our studies, and rapidly create valuable new technology. Together with the RESTORE Center community, we will achieve the potential of mobile technology to monitor real world function and improve care for conditions including stroke, Parkinson?s disease, osteoarthritis, frailty, cerebral palsy, and low back pain.

Limited mobility due to conditions like osteoarthritis (OA), cerebral palsy, and Parkinson?s disease affects millions of individuals, at enormous personal and societal cost. Rehabilitation can dramatically improve mobility and function, but current rehabilitation practice requires in-person guidance by a skilled clinician, increasing expense and limiting access. Mobile sensing technologies are now ubiquitous and have the potential to measure patient function and guide treatment outside the clinic, but they currently fail to capture the characteristics of motion required to accurately monitor function and customize treatment. Millions of low-cost mobile sensors are generating terabytes of data that could be analyzed in combination with other data, such as images, clinical records, and video, to enable studies of unprecedented scale, but machine learning models for analyzing these large-scale, heterogeneous, time-varying data are lacking. To address these challenges, we will establish a Biomedical Technology Resource Center ?The Mobilize Center. Through the leadership of an experienced scientific team, we will create and disseminate innovative tools to quantify movement biomechanics with mobile sensors. Specifically, we will: 1. Push the bounds of what we can measure via wearable sensors using models that compute muscle and joint forces and metabolic cost of locomotion. These models, based on biomechanical and machine learning models, will be disseminated via our newly created OpenSense software, which will be used by thousands of researchers to gain new insights into patient biomechanics using mobile sensors. 2. Meet the need for tools that analyze data about movement dynamics and develop machine learning models to analyze and generate insights from unstructured, high-dimensional data, including time- series (e.g., from mobile sensors), images (e.g., MRI), and video (e.g., smartphone video of a patient?s gait). 3. Provide tools needed to intervene in the real-world. We will develop algorithms to accurately quantify kinematics outside the lab for long durations using data from inertial measurement units (IMUs). We will also build behavioral models to adapt and personalize goal setting, drawing on movement records from 6 million individuals, as well as health goals and exercise for 1.7 million people. Through intensive interactions with our Collaborative Projects, we will focus on improving rehabilitation outcomes for individuals with limited mobility due to osteoarthritis, obesity, Parkinson?s disease, and cerebral palsy. The Center?s tools and services will enable researchers to revolutionize how we diagnose, monitor, and treat mobility disorders, providing tools needed to deliver precision rehabilitation at low cost and on a massive scale in the future.

Limited mobility due to conditions like osteoarthritis (OA), cerebral palsy, and Parkinson?s disease affects millions of individuals, at enormous personal and societal cost. Rehabilitation can dramatically improve mobility and function, but current rehabilitation practice requires in-person guidance by a skilled clinician, increasing expense and limiting access. Mobile sensing technologies are now ubiquitous and have the potential to measure patient function and guide treatment outside the clinic, but they currently fail to capture the characteristics of motion required to accurately monitor function and customize treatment. Millions of low-cost mobile sensors are generating terabytes of data that could be analyzed in combination with other data, such as images, clinical records, and video, to enable studies of unprecedented scale, but machine learning models for analyzing these large-scale, heterogeneous, time-varying data are lacking. To address these challenges, we will establish a Biomedical Technology Resource Center ?The Mobilize Center. Through the leadership of an experienced scientific team, we will create and disseminate innovative tools to quantify movement biomechanics with mobile sensors. Specifically, we will: 1. Push the bounds of what we can measure via wearable sensors using models that compute muscle and joint forces and metabolic cost of locomotion. These models, based on biomechanical and machine learning models, will be disseminated via our newly created OpenSense software, which will be used by thousands of researchers to gain new insights into patient biomechanics using mobile sensors. 2. Meet the need for tools that analyze data about movement dynamics and develop machine learning models to analyze and generate insights from unstructured, high-dimensional data, including time- series (e.g., from mobile sensors), images (e.g., MRI), and video (e.g., smartphone video of a patient?s gait). 3. Provide tools needed to intervene in the real-world. We will develop algorithms to accurately quantify kinematics outside the lab for long durations using data from inertial measurement units (IMUs). We will also build behavioral models to adapt and personalize goal setting, drawing on movement records from 6 million individuals, as well as health goals and exercise for 1.7 million people. Through intensive interactions with our Collaborative Projects, we will focus on improving rehabilitation outcomes for individuals with limited mobility due to osteoarthritis, obesity, Parkinson?s disease, and cerebral palsy. The Center?s tools and services will enable researchers to revolutionize how we diagnose, monitor, and treat mobility disorders, providing tools needed to deliver precision rehabilitation at low cost and on a massive scale in the future.

Mobile technology is poised to revolutionize rehabilitation research, but the infrastructure and training to support researchers in designing effective studies, collecting and analyzing data, and translating findings to improve care has not kept pace with the mobile technology industry. Our Center for Reliable Sensor Technology-Based Outcomes for Rehabilitation (RESTORE Center) will establish vital research infrastructure and training that enables rehabilitation scientists to use mobile sensors to monitor a diverse set of real-world outcomes. We will accomplish this by integrating expertise from bioengineering, statistics, computer science, mobile health, and clinical rehabilitation. Our mission is to launch a world-wide collaboration involving hundreds of researcher teams to collect and share real world data on rehabilitation outcomes. To achieve this, we will: 1. Provide state-of-the-art software to convert wearable sensor data into meaningful outcome metrics, create a data sharing repository with a vast set of movement and outcome data, and develop advanced data science tools to gain insight from real-world rehabilitation datasets. 2. Train thousands of rehabilitation scientists to use mobile technology for research through bootcamps, conference-based tutorials, an online knowledgebase, and massive open online courses. 3. Attract and train talented scholars from physical therapy, physiatry, computer science, biomechanics, and other fields to become experts in mobile technology and the needs of the rehabilitation community. 4. Award 65 seed grants to innovative and meritorious projects to accelerate the use of mobile technology in rehabilitation research and advance patient care. 5. Encourage the appropriate use of mobile technology in rehabilitation research and foster interdisciplinary collaborations through a multi-faceted promotion effort. Our broad outreach program will expand the group of over 14,000 researchers who are currently using our resources. 6. Establish a cohesive, vibrant, and sustainable Medical Rehabilitation Research Resource Center through the leadership of an experienced executive team that will manage the Center?s activities. By providing high-quality, in-demand, and open-source software tools, our Center will enable a collaboration of unprecedented scale between bioengineers, physical therapists, computer scientists, patients, physicians, and others focused on rehabilitation. Our training efforts will create a new generation of rehabilitation scientists who are fluent in the strengths and challenges of mobile technology. Our Center will be run by a tightly integrated clinical and engineering team, enabling us to appreciate the needs and goals of patients, recruit participants to our studies, and rapidly create valuable new technology. Together with the RESTORE Center community, we will achieve the potential of mobile technology to monitor real world function and improve care for conditions including stroke, Parkinson?s disease, osteoarthritis, frailty, cerebral palsy, and low back pain.

Limited mobility due to conditions like osteoarthritis (OA), cerebral palsy, and Parkinson?s disease affects millions of individuals, at enormous personal and societal cost. Rehabilitation can dramatically improve mobility and function, but current rehabilitation practice requires in-person guidance by a skilled clinician, increasing expense and limiting access. Mobile sensing technologies are now ubiquitous and have the potential to measure patient function and guide treatment outside the clinic, but they currently fail to capture the characteristics of motion required to accurately monitor function and customize treatment. Millions of low-cost mobile sensors are generating terabytes of data that could be analyzed in combination with other data, such as images, clinical records, and video, to enable studies of unprecedented scale, but machine learning models for analyzing these large-scale, heterogeneous, time-varying data are lacking. To address these challenges, we will establish a Biomedical Technology Resource Center ?The Mobilize Center. Through the leadership of an experienced scientific team, we will create and disseminate innovative tools to quantify movement biomechanics with mobile sensors. Specifically, we will: 1. Push the bounds of what we can measure via wearable sensors using models that compute muscle and joint forces and metabolic cost of locomotion. These models, based on biomechanical and machine learning models, will be disseminated via our newly created OpenSense software, which will be used by thousands of researchers to gain new insights into patient biomechanics using mobile sensors. 2. Meet the need for tools that analyze data about movement dynamics and develop machine learning models to analyze and generate insights from unstructured, high-dimensional data, including time- series (e.g., from mobile sensors), images (e.g., MRI), and video (e.g., smartphone video of a patient?s gait). 3. Provide tools needed to intervene in the real-world. We will develop algorithms to accurately quantify kinematics outside the lab for long durations using data from inertial measurement units (IMUs). We will also build behavioral models to adapt and personalize goal setting, drawing on movement records from 6 million individuals, as well as health goals and exercise for 1.7 million people. Through intensive interactions with our Collaborative Projects, we will focus on improving rehabilitation outcomes for individuals with limited mobility due to osteoarthritis, obesity, Parkinson?s disease, and cerebral palsy. The Center?s tools and services will enable researchers to revolutionize how we diagnose, monitor, and treat mobility disorders, providing tools needed to deliver precision rehabilitation at low cost and on a massive scale in the future.

Limited mobility due to conditions like osteoarthritis (OA), cerebral palsy, and Parkinson?s disease affects millions of individuals, at enormous personal and societal cost. Rehabilitation can dramatically improve mobility and function, but current rehabilitation practice requires in-person guidance by a skilled clinician, increasing expense and limiting access. Mobile sensing technologies are now ubiquitous and have the potential to measure patient function and guide treatment outside the clinic, but they currently fail to capture the characteristics of motion required to accurately monitor function and customize treatment. Millions of low-cost mobile sensors are generating terabytes of data that could be analyzed in combination with other data, such as images, clinical records, and video, to enable studies of unprecedented scale, but machine learning models for analyzing these large-scale, heterogeneous, time-varying data are lacking. To address these challenges, we will establish a Biomedical Technology Resource Center ?The Mobilize Center. Through the leadership of an experienced scientific team, we will create and disseminate innovative tools to quantify movement biomechanics with mobile sensors. Specifically, we will: 1. Push the bounds of what we can measure via wearable sensors using models that compute muscle and joint forces and metabolic cost of locomotion. These models, based on biomechanical and machine learning models, will be disseminated via our newly created OpenSense software, which will be used by thousands of researchers to gain new insights into patient biomechanics using mobile sensors. 2. Meet the need for tools that analyze data about movement dynamics and develop machine learning models to analyze and generate insights from unstructured, high-dimensional data, including time- series (e.g., from mobile sensors), images (e.g., MRI), and video (e.g., smartphone video of a patient?s gait). 3. Provide tools needed to intervene in the real-world. We will develop algorithms to accurately quantify kinematics outside the lab for long durations using data from inertial measurement units (IMUs). We will also build behavioral models to adapt and personalize goal setting, drawing on movement records from 6 million individuals, as well as health goals and exercise for 1.7 million people. Through intensive interactions with our Collaborative Projects, we will focus on improving rehabilitation outcomes for individuals with limited mobility due to osteoarthritis, obesity, Parkinson?s disease, and cerebral palsy. The Center?s tools and services will enable researchers to revolutionize how we diagnose, monitor, and treat mobility disorders, providing tools needed to deliver precision rehabilitation at low cost and on a massive scale in the future.