Fall risk assessment in the wild: A critical examination of wearable sensors use in free-living conditions

Falls are a significant threat to the health and independence of elderly people and represent an enormous burden on the healthcare system. Successfully predicting falls could be of great help, yet this requires a timely and accurate fall risk assessment. Gait abnormalities are one of the best predictive signs of underlying locomotion conditions and precursors of falls. The advent of wearable sensors and wrist-worn devices provides new opportunities for continuous and unobtrusive monitoring of gait during daily activities, including the identification of unexpected changes in gait. To this end, we present in this paper a novel method for determining gait abnormalities based on a wrist-worn device and a deep neural network. It integrates convolutional and bidirectional long short-term memory layers for successful learning of spatiotemporal features from multiple sensor signals. The proposed method was evaluated using data from 18 subjects, who recorded their normal gait and simulated abnormal gait while wearing impairment glasses. The data consist of inertial measurement unit (IMU) sensor signals obtained from smartwatches that the subjects wore on both wrists. Numerous experiments showed that the proposed method provides better results than the compared methods, achieving 88.9% accuracy, 90.6% sensitivity, and 86.2% specificity in the detection of abnormal walking patterns using data from an accelerometer, gyroscope, and rotation vector sensor. These results indicate that reliable fall risk assessment is possible based on the detection of walking abnormalities with the use of wearable sensors on a wrist.

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Instrumented timed up and go: Fall risk assessment based on inertial wearable sensors

2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA) ◽

10.1109/memea.2016.7533778 ◽

2016 ◽

Cited By ~ 8

Author(s):

Joana Silva ◽

Ines Sousa

Keyword(s):

Risk Assessment ◽

Fall Risk ◽

Wearable Sensors ◽

Timed Up And Go ◽

Fall Risk Assessment

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Promises and Challenges in the Use of Wearable Sensors and Nonlinear Signal Analysis for Balance and Fall Risk Assessment in Older Adults

IFMBE Proceedings - Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices ◽

10.1007/978-3-030-30636-6_40 ◽

2019 ◽

pp. 288-295

Author(s):

Luis Montesinos ◽

Rossana Castaldo ◽

Leandro Pecchia

Keyword(s):

Older Adults ◽

Risk Assessment ◽

Signal Analysis ◽

Fall Risk ◽

Wearable Sensors ◽

Fall Risk Assessment ◽

Nonlinear Signal ◽

Nonlinear Signal Analysis

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Sensor-based fall risk assessment in older adults with or without cognitive impairment: a systematic review

European Review of Aging and Physical Activity ◽

10.1186/s11556-021-00266-w ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Jelena Bezold ◽

Janina Krell-Roesch ◽

Tobias Eckert ◽

Darko Jekauc ◽

Alexander Woll

Keyword(s):

Systematic Review ◽

Older Adults ◽

Risk Assessment ◽

Cognitive Impairment ◽

Fall Risk ◽

Wearable Sensors ◽

Sensor Location ◽

Risk Of Falling ◽

Fall Risk Assessment ◽

Fall Risk Factors

Abstract Background Higher age and cognitive impairment are associated with a higher risk of falling. Wearable sensor technology may be useful in objectively assessing motor fall risk factors to improve physical exercise interventions for fall prevention. This systematic review aims at providing an updated overview of the current research on wearable sensors for fall risk assessment in older adults with or without cognitive impairment. Therefore, we addressed two specific research questions: 1) Can wearable sensors provide accurate data on motor performance that may be used to assess risk of falling, e.g., by distinguishing between faller and non-faller in a sample of older adults with or without cognitive impairment?; and 2) Which practical recommendations can be given for the application of sensor-based fall risk assessment in individuals with CI? A systematic literature search (July 2019, update July 2020) was conducted using PubMed, Scopus and Web of Science databases. Community-based studies or studies conducted in a geriatric setting that examine fall risk factors in older adults (aged ≥60 years) with or without cognitive impairment were included. Predefined inclusion criteria yielded 16 cross-sectional, 10 prospective and 2 studies with a mixed design. Results Overall, sensor-based data was mainly collected during walking tests in a lab setting. The main sensor location was the lower back to provide wearing comfort and avoid disturbance of participants. The most accurate fall risk classification model included data from sit-to-walk and walk-to-sit transitions collected over three days of daily life (mean accuracy = 88.0%). Nine out of 28 included studies revealed information about sensor use in older adults with possible cognitive impairment, but classification models performed slightly worse than those for older adults without cognitive impairment (mean accuracy = 79.0%). Conclusion Fall risk assessment using wearable sensors is feasible in older adults regardless of their cognitive status. Accuracy may vary depending on sensor location, sensor attachment and type of assessment chosen for the recording of sensor data. More research on the use of sensors for objective fall risk assessment in older adults is needed, particularly in older adults with cognitive impairment. Trial registration This systematic review is registered in PROSPERO (CRD42020171118).

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