Patricia Bet1, Paula C Castro2, Moacir A Ponti3. 1. DGero - Universidade Federal de São Carlos, São Carlos, SP, Brazil. Electronic address: patriciabet95@gmail.com. 2. DGero - Universidade Federal de São Carlos, São Carlos, SP, Brazil. 3. ICMC - Universidade de São Paulo, São Carlos, 13566-590, SP, Brazil.
Abstract
BACKGROUND: wearable sensors are often used to acquire data for gait analysis as a strategy to study fall events, due to greater availability of acquisition platforms, and advances in computational intelligence. However, there are no review papers addressing the three most common types of applications related to fall using sensors, namely: fall detection, fallers classification and fall risk screening. OBJECTIVE: To identify the state of art of fall-related events detection in older person using wearable sensors, as well as the main characteristics of the studies in the literature, pointing gaps for future studies. METHODS: A systematic review design was used to search peer-reviewed literature on fall detection and risk in elderly through inertial sensors, published in English, Portuguese, Spanish or French between August 2002 and June 2019. The following questions are investigated: the type of sensors and their sampling rate, the type of signal and data processing employed, the scales and tests used in the study and the type of application. RESULTS: We identified 608 studies, from which 29 were included. The accelerometer, with sampling rate 50 or 100 Hz, allocated in the waist or lumbar was the most used sensor setting. Methods comparing features or variables extracted from the accelerometry signal are the most common, and fall risk screening the most observed application. CONCLUSION: This review identifies the main elements to be addressed in studies on the detection of events related to falls in the elderly and may help in future studies on the subject. However, some aspects are still no reach consensus in the literature such as the size of the sample to be studied, the population under study and how to acquire data for each application.
BACKGROUND: wearable sensors are often used to acquire data for gait analysis as a strategy to study fall events, due to greater availability of acquisition platforms, and advances in computational intelligence. However, there are no review papers addressing the three most common types of applications related to fall using sensors, namely: fall detection, fallers classification and fall risk screening. OBJECTIVE: To identify the state of art of fall-related events detection in older person using wearable sensors, as well as the main characteristics of the studies in the literature, pointing gaps for future studies. METHODS: A systematic review design was used to search peer-reviewed literature on fall detection and risk in elderly through inertial sensors, published in English, Portuguese, Spanish or French between August 2002 and June 2019. The following questions are investigated: the type of sensors and their sampling rate, the type of signal and data processing employed, the scales and tests used in the study and the type of application. RESULTS: We identified 608 studies, from which 29 were included. The accelerometer, with sampling rate 50 or 100 Hz, allocated in the waist or lumbar was the most used sensor setting. Methods comparing features or variables extracted from the accelerometry signal are the most common, and fall risk screening the most observed application. CONCLUSION: This review identifies the main elements to be addressed in studies on the detection of events related to falls in the elderly and may help in future studies on the subject. However, some aspects are still no reach consensus in the literature such as the size of the sample to be studied, the population under study and how to acquire data for each application.
Authors: Richard D Boyce; Olga V Kravchenko; Subashan Perera; Jordan F Karp; Sandra L Kane-Gill; Charles F Reynolds; Steven M Albert; Steven M Handler Journal: J Am Med Inform Assoc Date: 2022-08-16 Impact factor: 7.942
Authors: Brett M Meyer; Lindsey J Tulipani; Reed D Gurchiek; Dakota A Allen; Lukas Adamowicz; Dale Larie; Andrew J Solomon; Nick Cheney; Ryan S McGinnis Journal: IEEE J Biomed Health Inform Date: 2021-05-11 Impact factor: 5.772