2015 |
Inan, Omer Tolga Klein, Liviu |
R56Activity Code Description: To provide limited interim research support based on the merit of a pending R01 application while applicant gathers additional data to revise a new or competing renewal application. This grant will underwrite highly meritorious applications that if given the opportunity to revise their application could meet IC recommended standards and would be missed opportunities if not funded. Interim funded ends when the applicant succeeds in obtaining an R01 or other competing award built on the R56 grant. These awards are not renewable. |
Wearable Cardiomechanics Monitor to Decrease Heart Failure Readmissions @ Georgia Institute of Technology
? DESCRIPTION (provided by applicant): Heart failure (HF) is one of the most major challenges faced by society today, claiming hundreds of thousands of American lives each year and costing more than 30 billion Medicare dollars annually. The ultimate goal of this research is to create a non-invasive and unobtrusive system for monitoring HF patients at home, automatically assessing their risk of experiencing an exacerbation, and providing feedback to caregivers and the patients themselves. This will enable proactive management of HF at home, with patients receiving tailored therapies that can adapt continuously to meet their changing needs. The hypothesis is that by (1) measuring a combination of hemodynamics, activity, and cardiovascular response to stressors (e.g. exercise) at home, and (2) combining these heterogeneous measures with modern data analytics, prediction of HF exacerbation at home can be achieved with a predictive window of greater than 7 days before impending hospitalization. To examine this hypothesis, the following four specific aims are proposed: (1) to collect longitudinal hemodynamic (ballistocardiogram, BCG) and activity data unobtrusively at home for the first time in a population of elderly HF patients; (2) to adapt existing algorithms fo predicting an impending HF exacerbation based on BCG, ECG, and activity time series data; (3) to develop wearable hardware based on BCG to be used at home for continuous hemodynamic and activity recording from elderly HF patients; and (4) to develop innovative sensing strategies and algorithms for improving the robustness of wearable BCG measurements. A previously demonstrated and verified weighing scale for center-of-mass (COM) BCG measurement will be scaled-up and deployed in the home of 200 total patients over the course of the project. Simultaneously, the hardware and analytics efforts will build on our prior data and existing prototypes. For the first 25 participants in the take-home BCG study, we will also conduct a direct physiologic study to quantify the underlying mechanisms contributing to both the scale-based (COM) and wearable BCG measurements, and to develop computational techniques for converting between the two domains (COM versus surface vibrations of the chest). These techniques, in combination with iterative and experimental efforts to improve the robustness of wearable BCG measurements, will then be applied to optimizing the wearable system for BCG and activity monitoring. This system will then be scaled-up and deployed in the home for the final 50 participants (of 200) in the take-home study. While we anticipate that the wearable will provide the best solution, the project risk is mitigated through the validation efforts with the existing scale-based system. Successful completion of this project could ultimately reduce HF related hospitalizations, and thus both improve quality of life for elderly Americans, and reduce overall healthcare costs.
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0.958 |
2016 — 2020 |
Inan, Omer Tolga Klein, Liviu |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Non-Invasive Biosensors to Detect Cardiovascular Changes in Heart Failure @ Georgia Institute of Technology
? DESCRIPTION (provided by applicant): Heart failure (HF) is one of the most major challenges faced by society today, claiming hundreds of thousands of American lives each year and costing more than 30 billion Medicare dollars annually. The ultimate goal of this research is to create an unobtrusive wearable system for continuously monitoring HF patients in naturalistic settings, automatically assessing their risk of experiencing an exacerbation, and providing feedback to caregivers and the patients themselves. The central innovation that will support these efforts is the proposed measurement of hemodynamic responses to stressors experienced in normal activities of daily living (e.g., walking, climbing stairs). The measurement of such hemodynamic responses will be enabled by wearable ballistocardiography (BCG). The following four specific aims are proposed for the research: (1) to elucidate the underlying mechanisms involved in the genesis of wearable ballistocardiogram (BCG) signals; (2) to develop novel predictive analytics algorithms for BCG signals measured from HF patients at home; (3) to design and implement a wearable sensing system for estimating cardiac output (CO), blood pressure (BP), and indirect calorimetry from ambulant subjects; and (4) to evaluate the wearable sensing system with healthy and HF patients during cardiopulmonary stress testing, and to pilot the new system at home for a small population of patients. The first aim will build a strong foundation for better understanding the wearable BCG signal - a measurement of body vibrations in response to the heartbeat - and will inform the placement and modality of the sensor for optimizing the sensing. Furthermore, the evaluation of this wearable prototype will include usability testing to assess comfort and robustness to practical challenges (e.g., motion artifacts, the device rubbing on clothing), and based on the results the design will be refined and improved. While we anticipate that the wearable will provide the best solution, the project risk is mitigated through the more mature, existing scale- based system. Successful completion of this project could ultimately reduce HF related hospitalizations, and thus both improve quality of life for elderly Americans, and reduce overall healthcare costs.
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0.958 |
2016 — 2020 |
Inan, Omer Tolga |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Sch: Int: Wearable Knee Joint Health Sensing Using Acoustical Emissions @ Georgia Institute of Technology
Every year, millions of Americans present at the hospital with knee injuries, such as meniscus tears or anterior cruciate ligament (AGL) sprains. Moreover, knee injuries are one of the most common causes of missed workdays. The current paradigm of treating knee injuries initially involves frequent physical therapy visits, subjective evaluations by experts, and possibly surgery; following these initial steps the patient continues to participate in physical therapy, periodically - and typically infrequently - returns to the clinic for follow-up subjective evaluations, and bases his I her joint health rehabilitation status mainly on symptoms and pain. There is no technology available currently to provide patients with knee injuries frequent, objective, and in-depth information regarding the status of their joint rehabilitation. The hypothesis for this project is that the sounds of the joints measured using sensors embedded in a wearable wrap can provide a clinically-relevant biomarker for joint health rehabilitation assessment, and can ultimately allow patients to tune their rehabilitation exercises dynamically based on objective feedback. This could potentially accelerate rehabilitation, reduce the risk of re-injury, and empower patients to be in control of their rehabilitation. This project proposes to study these sounds, and their measurement, with an integrative program including the following specific aims: (1) Design and implement an ultra-low noise, high-bandwidth, wafer-level-packaged micro-accelerometer chip for contact measurement of joint sounds from the skin surface with high fidelity; (2) Elucidate the origin of the sounds and how they change with injury using a cadaver model; (3) Develop algorithms for extracting salient features from the joint sound signals that can be used to assess joint health; (4) Evaluate the sensors and analytics in a population of 20 subjects with meniscus tears, before and after surgery, and twice during rehabilitation several months following surgery.
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0.958 |