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

NCT05076565 | Active Not Recruiting

• 1 other identifier: STU00209246
• Study Type: interventional
• Target: 200
• Locations: 1 country
• Sites: 1

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.

Conditions

Stroke; Parkinson Disease; Lower Limb Amputation Knee; Fall Injury; Fall Patients; Falling

Outcome Measures

Primary Outcomes (1)

• Pre-fall classification performance

  Derivation(s) from a confusion matrix

  1 year

Interventions

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.

Eligibility Criteria

• Age: 18 Years - 85 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may not qualify if:

• 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
• 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.

Sponsors & Collaborators

• Shirley Ryan AbilityLab: lead

Study Sites (1)

Shirley Ryan AbilityLab

Chicago, Illinois, 60611, United States

Location

Related Publications (1)

• 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.

  PMID: 35715823 DERIVED

MeSH Terms

Conditions

Stroke; Parkinson Disease

Condition Hierarchy (Ancestors)

Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases; Cardiovascular Diseases; Parkinsonian Disorders; Basal Ganglia Diseases; Movement Disorders; Synucleinopathies; Neurodegenerative Diseases

Study Officials

• Arun Jayaraman, PT, PhD

  Shirley Ryan AbilityLab

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DEVICE FEASIBILITY
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Model Details: This device feasibility enrollment number of 200 is a larger sample in order to create a machine learning algorithm

Study Record Dates

• First Submitted: June 11, 2021
• First Posted: October 13, 2021
• Study Start: January 14, 2018
• Primary Completion (Estimated): December 14, 2026
• Study Completion (Estimated): December 14, 2026
• Last Updated: December 29, 2025

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)