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.

Keywords: fall prediction; fall risk assessment; wearable sensors.

Figure 1

PRISMA flow diagram.

Figure 2

(a) Number of studies from each fall risk assessment methods identified. (b) Fall risk assessment method adopted by each study. Saadeh [4], Leone [5], Rivolta [8], Tang [9], Parvaneh [12], Annese [13], Rivolta [25], Shahzad [26], Saporito [27], Rescio [28], Leone [29], Buisseret [30], Yang [31], Selvaraj [32], Vieira [33], and Dzhagaryan [34].

Figure 3

Overview of the sensor characteristics from clinical scale-based fall risk assessment studies. (a) Anterior and posterior views of the human body depicting sensor location, where: (i) [8,25,27,33,34], (ii) [9], (iii) [9,31], and (iv) [26,30]. (b) Adopted sensor specifications, where: S = sensors, N = number, fs = sampling frequency, Acc = accelerometer, Gyro = gyroscope, Mag = magnetometer, Press = pressure sensors, Bar = barometer, Dist = distance sensors, $N\backslash A$ = Not Available. Rivolta [8], Tang [9], Rivolta [25], Shahzad [26], Saporito [27], Buisseret [30], Yang [31], Vieira [33], and Dzhagaryan [34].