Journal article
Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics
Sensors (Basel, Switzerland), Vol.21(10), p.3481
05/01/2021
DOI: 10.3390/s21103481
PMCID: PMC8156094
PMID: 34067644
Abstract
Falls among the elderly population cause detrimental physical, mental, financial problems and, in the worst case, death. The increasing number of people entering the higher risk age-range has increased clinicians' attention to intervene. Clinical tools, e.g., the Timed Up and Go (TUG) test, have been created for aiding clinicians in fall-risk assessment. Often simple to evaluate, these assessments are subject to a clinician's judgment. Wearable sensor data with machine learning algorithms were introduced as an alternative to precisely quantify ambulatory kinematics and predict prospective falls. However, they require a long-term evaluation of large samples of subjects' locomotion and complex feature engineering of sensor kinematics. Therefore, it is critical to build an objective fall-risk detection model that can efficiently measure biometric risk factors with minimal costs. We built and studied a sensor data-driven convolutional neural network model to predict older adults' fall-risk status with relatively high sensitivity to geriatrician's expert assessment. The sample in this study is representative of older patients with multiple co-morbidity seen in daily medical practice. Three non-intrusive wearable sensors were used to measure participants' gait kinematics during the TUG test. This data collection ensured convenient capture of various gait impairment aspects at different body locations.
Details
- Title: Subtitle
- Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics
- Creators
- Venous Roshdibenam - University of IowaGerald J. Jogerst - University of IowaNicholas R. Butler - University of IowaStephen Baek - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Sensors (Basel, Switzerland), Vol.21(10), p.3481
- DOI
- 10.3390/s21103481
- PMID
- 34067644
- PMCID
- PMC8156094
- NLM abbreviation
- Sensors (Basel)
- ISSN
- 1424-8220
- eISSN
- 1424-8220
- Publisher
- Mdpi
- Number of pages
- 18
- Grant note
- R49 CE002108-05 / National Center for Injury Prevention and Control/CDC; United States Department of Health & Human Services; Centers for Disease Control & Prevention - USA
- Language
- English
- Date published
- 05/01/2021
- Academic Unit
- Family and Community Medicine; Radiation Oncology
- Record Identifier
- 9984297337702771
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