Wearable Airbag Technology to Mitigate Falls in Individuals With High Fall Risk

Study Overview
=================
Brief Summary
-----------------
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.

Detailed Description
-----------------
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: To evaluate and optimize pre-fall detection algorithms and the usability of the smart airbag system for fall mitigation in individuals with high fall risk. To evaluate the efficacy of the smart airbag system in mitigating real-world falls and its effect on community mobility in individuals with high fall risk. 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.

Official Title
-----------------
Wearable Airbag Technology to Mitigate Falls in Individuals With High Fall Risk

Conditions
-----------------
Falling, Stroke, Parkinson Disease, Lower Limb Amputation Knee, Fall Injury, Fall Patients

Intervention / Treatment
-----------------
* Device: Airbag Belt Fall Protection System


Participation Criteria
=================
Eligibility Criteria
-----------------
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: Healthy, able-bodied subject Age constrained from 18-70 years old No injury to either upper or lower extremity or history of back pain English speaking. Exlusion Criteria - Able bodied Subjects: Waist circumference greater than 125 cm Pregnant women (status determined by self-reporting) Co-morbidity that interferes with the study (e.g. significant arthritis or joint problems, history of back injury, neuromuscular disorders, stroke, epilepsy, etc.) Individuals currently on anti-coagulants. Inactive, physically unfit Severe Osteoporosis (status determined by self-reporting) Non-English speaking Cognitive deficits or visual impairments (MMSE score <17) that would impair their ability to give informed consent or impair their ability to follow simple instructions during the experiments Inclusion Criteria - Fall Risk Subjects: Ages between 18-85 years old Individuals diagnosed with a Stroke (> 6 months post), Parkinson's disease, aging elderly (ages 60-85)or lower-limb amputee with at least one self-reported fall in the last six months. Able to sit unsupported, walk at least with an assistive device and be able to follow a three-step command. For individuals with Parkinson's disease, Scoring 1 or higher on questions in Section II (Activities of Daily Living) and Section III (Motor examinations) on the Unified PD Rating Scale (UPDRS), be able to walk at least with an assistive device and be able to follow a three-step command. Waist circumference between 90 and 125 cm Either homebound or community ambulators. Willing to carry and use a smartphone and Airbag device. Willing to wear the airbag system as directed by the research personnel. English speaking Able and willing to give written consent and comply with study procedures. Exclusion Criteria - Fall Risk Subjects Serious cardiac conditions, any musculoskeletal disorder, or other comorbidities that would interfere with participation in this minimal risk study. Non-healing ulcers of a lower extremity, Renal dialysis or end-stage liver disease, Legal blindness or severe visual impairment, a history of significant psychiatric illness. Subjects reporting a head injury from exposure to a blast/concussion injury with one or more of the following symptoms: dizziness, vertigo, headache, migraine, oscillopsia, movement induced vertigo, imbalance. Individuals who use a wheelchair for mobility both outdoors and indoors. Waist circumference greater than 125 cm Non-English speaking individuals Severe Osteoporosis (status determined by self-reporting,medical records) The subject is pregnant, nursing or planning a pregnancy. Individuals currently on anti-coagulants. Cognitive deficits or visual impairments (MMSE score < 17) that would impair their ability to give informed consent or impair their ability to follow simple instructions during the experiments

Ages Eligible for Study
-----------------
Minimum Age: 18 Years
Maximum Age: 85 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
Accepts Healthy Volunteers

Study Plan
=================
How is the study designed?
-----------------

Design Details

Primary Purpose: Device Feasibility
Intervention Model: Single Group Assignment
Interventional Model Description: This device feasibility enrollment number of 200 is a larger sample in order to create a machine learning algorithm
Masking: None (Open Label)


Arms and Interventions

| Intervention/Treatment |
| --- |
|Device: Airbag Belt Fall Protection System|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.|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Pre-fall classification performance | Derivation(s) from a confusion matrix | 1 year |