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High-probability grants
According to our matching algorithm, Carlo J. De Luca is the likely recipient of the following grants.
| Years |
Recipients |
Code |
Title / Keywords |
Matching
score |
| 2000 — 2011 |
De Luca, Carlo J |
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. R24Activity Code Description:
Undocumented code - click on the grant title for more information. |
Harnessing Motoneuron Activity: From Lab to Clinic
DESCRIPTION (Adapted from the applicant's abstract): This study will develop an automatic system for decomposing the electromyographic signal into the constituent action potentials corresponding to the firing of individual motor units activated by motorneurons. The system will be an enhancement of a current system used over the past 20 years in many studies carried out by the Neuromuscular Research Lab at Boston University. Although the current system has been a valuable research tool, it has never been useful as a clinical tool due to limitations in processing time, accuracy and portability. Proposed enhancements will be introduced by redesigning the hardware and rewriting the decomposition software using a knowledge-based artificial intelligence language (IPUS), which has recently been developed by the team. As part of this application the enhanced system will be used in two laboratory studies and two clinical studies. The laboratory studies will investigate the modifications that occur in the firing of motor units as a function of ageing and will quantify the benefits that can be restored by exercise. The system will also be used to investigate the phenomena of motor unit substitution. The clinical studies will address the use of the device in quantifying the degree of denervation in paralyzed laryngeal muscles and in studying the effect of acute ataxia on the firing characteristics of the motorneurons in cerebellar stroke. As well as testing specific hypotheses, these studies will be used to test and improve the evolving design of the new decomposition system.
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1 |
| 2006 — 2010 |
De Luca, Carlo J |
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. |
Wearable-Sensor System For Monitoring Motor Function @ Boston University (Charles River Campus)
DESCRIPTION (provided by applicant): We propose to develop a wearable Personal Status Monitor for improving the medication management of Parkinson's Disease patients by monitoring the effects of the medication continuously during the day. New outcome measures are needed to supplement the self-reports currently in use that are ineffective in managing the complex and unpredictable nature of movement disorders in this population. The device we propose to develop can be worn unobtrusively by patients in their home to automatically provide the following clinically significant information: 1) the presence and severity of specific primary and secondary movement disorders associated with the disease, 2) the status of On-Off motor fluctuation in response to anti-Parkinson's medication, and 3) the mobility status of the patient. The patient will be monitored by specially designed electromyographic (EMG) and accelerometric (ACC) body-worn sensors. Their signals will be analyzed by a novel Artificial Intelligence knowledge-based signal processing method developed by our group specifically for this purpose. Success of the system will be based on classification accuracy compared to observation by experts. The proposal is composed of two projects. The first and dominant project will develop the underlying technological requirements for the PSM system's application to Parkinson's disease. The aims will include acquisition hardware development in the form of hybrid EMG/ACC sensors. However, the emphasis will be on the development of the knowledge-based algorithms and their software implementation. The second project is designed to acquire the knowledge base needed in Project 1 through data collection experiments from control subjects and patients with Parkinson's disease. The experiments are designed to advance the algorithm development in a hierarchical manner starting from highly standardized activities to free-form activities which approximate real-world conditions. The development of the system will be constructed so that future versions can be adapted to other movement disorders. The successful development of this technology will be transferred for commercial development with the financial assistance of the Massachusetts Community Technology Foundation.
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1 |