Wearable Airbag Technology to Mitigate Falls in Individuals With High Fall Risk
NCT ID: NCT05076565
Last Updated: 2025-12-29
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
ACTIVE_NOT_RECRUITING
NA
200 participants
INTERVENTIONAL
2018-01-14
2026-12-14
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
The specific aims of this study are:
1. 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.
2. 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.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
NA
SINGLE_GROUP
DEVICE_FEASIBILITY
NONE
Interventions
Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.
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.
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
* 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.
* 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
* 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
18 Years
85 Years
ALL
Yes
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
Shirley Ryan AbilityLab
OTHER
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Arun Jayaraman, PT, PhD
Principal Investigator
Principal Investigators
Learn about the lead researchers overseeing the trial and their institutional affiliations.
Arun Jayaraman, PT, PhD
Role: PRINCIPAL_INVESTIGATOR
Shirley Ryan AbilityLab
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
Shirley Ryan AbilityLab
Chicago, Illinois, United States
Countries
Review the countries where the study has at least one active or historical site.
References
Explore related publications, articles, or registry entries linked to this study.
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.
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
STU00209246
Identifier Type: -
Identifier Source: org_study_id