Fall Risk Assessment Using Wearable Sensors: A Narrative Review

Abstract

Recently, fall risk assessment has been a main focus in fall-related research. Wearable sensors have been used to increase the objectivity of this assessment, building on the traditional use of oversimplified questionnaires. However, it is necessary to define standard procedures that will us enable to acknowledge the multifactorial causes behind fall events while tackling the heterogeneity of the currently developed systems. Thus, it is necessary to identify the different specifications and demands of each fall risk assessment method. Hence, this manuscript provides a narrative review on the fall risk assessment methods performed in the scientific literature using wearable sensors. For each identified method, a comprehensive analysis has been carried out in order to find trends regarding the most used sensors and its characteristics, activities performed in the experimental protocol, and algorithms used to classify the fall risk. We also verified how studies performed the validation process of the developed fall risk assessment systems. The identification of trends for each fall risk assessment method would help researchers in the design of standard innovative solutions and enhance the reliability of this assessment towards a homogeneous benchmark solution.

Provoking Artificial Slips and Trips towards Perturbation-Based Balance Training: A Narrative Review

“…They were the only type of biosensor used among the 48 studies collected. EMG data may provide useful information about muscles activated during imminent fall-risk situations [ 81 ]. Accordingly, Sawers et al [ 22 , 45 ] and Nazifi et al [ 54 ] explored muscle synergies, which represent groups of muscles that coactivate to produce a biomechanical function required to perform a certain motor task [ 82 ], during perturbation trials.…”

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall

“…Also, personal devices, such as smartphones, are beginning to be used for implementing fall systems because they are carried by the users most of the day. Ferreira et al [ 27 ] review showed that IMUs were generally placed in the upper body and that machine learning models were preferably adopted to classify the subject’s risk of fall. However, they noted that the number of participants enrolled in the studies they reviewed was often reduced and sometimes did not include elderly participants.…”