The evolution of trends and technology in wearable sensors used to detect falls in people with neurodegenerative diseases: a systematic review.
Systematic review of 89 studies (predominantly in Parkinson's disease) characterizing wearable sensor types, placements (ankle common), and machine-learning algorithms for fall detection and calling for standardized, real-world validation.
What the AI sees
Systematic review of 89 studies (predominantly in Parkinson's disease) characterizing wearable sensor types, placements (ankle common), and machine-learning algorithms for fall detection and calling for standardized, real-world validation.
Research significance
Although it doesn't address molecular or therapeutic mechanisms, the paper is useful for Parkinson's drug development because robust, validated wearable fall-detection tools can supply objective real-world endpoints, safety monitoring, and patient stratification to improve clinical trials and care.
Source abstract
BACKGROUND: Neurodegenerative diseases (NDs) are a significant threat to human health. Numerous research demonstrated that patients with NDs might present with decreased balance, which is responsible for an increased risk of falling. As an emerging technology, wearable devices can detect falls and prevent privacy breaches. OBJECTIVE: To access the evolution of trends and technology in wearable devices to detect falls among patients with NDs. METHODS: We screened PubMed and Web of Science (February 2023) to summarize the pathway of fall detection with any body-worn sensor. Included articles were required to be full-text and published in English. Documents were excluded if they; (1) only used wearable devices for fall cueing, (2) did not offer sufficient information for data extraction, (3) did not use patients with NDs, (4) only used non-wearable sensors or devices. RESULTS: The review identified 89 articles at the end of the procedure for data extraction. A wide variety existed in participant sample size (1-131), sensor types, placement and algorithms. 97.75% of papers (n = 87) used patients with Parkinson's disease as experimental subjects. 21.45% of studies attached devices on the ankle (n = 19), with a clear preference for using multiple types of sensors (58.43% of studies, n = 52). As the most commonly used inertial measurement unit (IMU), 21 articles utilized accelerometers and gyroscopes to assess falls. 39.33% of studies (n = 35) choose data set to verify the effectiveness of their algorithm. Machine learning algorithms have become prevalent since 2019, and the most commonly used algorithm was support vector machine (SVM) (n = 17). CONCLUSION: These results show that an increasing number of researchers examine the validation performance of their systems in non-real-time. The ankle was the preferred location among researchers, and there is a clear preference to use multiple types of sensors and machine learning algorithms to improve accuracy and immediacy. Future work should focus on other NDs instead of limiting to Parkinson's disease and consider an adequately studied population. A consensus on walking tasks and accuracy measurements is urgently needed. Performing studies in a simulated free-living environment for a specified time frame is advisable, with continuous real-time monitoring and assessment. SYSTEMATIC REVIEW REGISTRATION: PROSPERO, identifier (CRD42023405952).