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The purpose of this study is to evaluate the feasibility and efficacy of a smart airbag system that detects and mitigates fall-related impact in individuals with high fall risk.
The purpose of this study is to evaluate the feasibility and efficacy of a smart airbag system that detects and mitigates fall-related impact in individuals with high fall risk.
The specific aims of this study are:
The investigators hypothesize that a soft, smart airbag system that uses advanced machine learning algorithms can accurately detect and mitigate falls, deploying appropriately to reduce hip fractures due to falls. The investigators also expect that wearing this device will decrease fear of falling and thus increase community mobility and social interaction.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Airbag Belt Fall Protection System | Device | Both versions of Airbags features different number of IMU sensors. Participant's will be randomly assigned one of the two versions. The algorithms developed in this project will help the researcher to identify the optimal performance (sensitivity and specificity values for detecting falls). Based on this information the research team will be able to choose a version for home/community deployment portion of the study. Based on the performance of the airbags in detecting true positives (falls) and true negatives (non-falls) accurately one of the airbags will be used in community deployment phase of the study. |
| Measure | Description | Time Frame |
|---|---|---|
| Pre-fall classification performance | Derivation(s) from a confusion matrix | 1 year |
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INCLUSION AND EXCLUSION CRITERIA
All potential subjects will be evaluated by research staff in order to match them to the inclusion and exclusion criteria that has been established - see below:
Inclusion Criteria - Able bodied Subjects:
Exlusion Criteria - Able bodied Subjects:
Inclusion Criteria - Fall Risk Subjects:
Exclusion Criteria - Fall Risk Subjects
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| Name | Affiliation | Role |
|---|---|---|
| Arun Jayaraman, PT, PhD | Shirley Ryan AbilityLab | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Shirley Ryan AbilityLab | Chicago | Illinois | 60611 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 35715823 | Derived | Botonis OK, Harari Y, Embry KR, Mummidisetty CK, Riopelle D, Giffhorn M, Albert MV, Heike V, Jayaraman A. Wearable airbag technology and machine learned models to mitigate falls after stroke. J Neuroeng Rehabil. 2022 Jun 17;19(1):60. doi: 10.1186/s12984-022-01040-4. |
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| ID | Term |
|---|---|
| D020521 | Stroke |
| D010300 | Parkinson Disease |
| ID | Term |
|---|---|
| D002561 | Cerebrovascular Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
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This device feasibility enrollment number of 200 is a larger sample in order to create a machine learning algorithm
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| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |
| D020734 | Parkinsonian Disorders |
| D001480 | Basal Ganglia Diseases |
| D009069 | Movement Disorders |
| D000080874 | Synucleinopathies |
| D019636 | Neurodegenerative Diseases |