Review of fall risk assessment in geriatric populations using inertial sensors

Abstract

BackgroundFalls are a prevalent issue in the geriatric population and can result in damaging physical and psychological consequences. Fall risk assessment can provide information to enable appropriate interventions for those at risk of falling. Wearable inertial-sensor-based systems can provide quantitative measures indicative of fall risk in the geriatric population.MethodsForty studies that used inertial sensors to evaluate geriatric fall risk were reviewed and pertinent methodological features were extracted; including, sensor placement, derived parameters used to assess fall risk, fall risk classification method, and fall risk classification model outcomes.ResultsInertial sensors were placed only on the lower back in the majority of papers (65%). One hundred and thirty distinct variables were assessed, which were categorized as position and angle (7.7%), angular velocity (11.5%), linear acceleration (20%), spatial (3.8%), temporal (23.1%), energy (3.8%), frequency (15.4%), and other (14.6%). Fallers were classified using retrospective fall history (30%), prospective fall occurrence (15%), and clinical assessment (32.5%), with 22.5% using a combination of retrospective fall occurrence and clinical assessments. Half of the studies derived models for fall risk prediction, which reached high levels of accuracy (62-100%), specificity (35-100%), and sensitivity (55-99%).ConclusionsInertial sensors are promising sensors for fall risk assessment. Future studies should identify fallers using prospective techniques and focus on determining the most promising sensor sites, in conjunction with determination of optimally predictive variables. Further research should also attempt to link predictive variables to specific fall risk factors and investigate disease populations that are at high risk of falls.

Fall-Risk Classification of the Timed Up-And-Go Test with Principle Component Analysis

“…A previous study showed that frail elderly subjects exhibited significantly decreased smoothness in the sit-stand transition pattern [8,21,23,[29][30][31][32][33].…”

“…The stability and variability of walking can be identified by a simple control theory such as the Lyapunov exponent [22]. A review by Howcroft et al [8] suggested that intelligent computing methods such as neural networks and Bayesian classifiers might be more appropriate for fall risk classification than regression techniques [8]. However, no studies have identified the parameters that play the most important roles in the risk of falls.…”

“…Sway and standing balance have been estimated using accelerometry and gyro-sensors. Several other gait performance measures, including the TUG test, have also been evaluated [8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Various parameters have been measured and collected to classify the risk of falling among the elderly.…”

209 Quantitative Analysis of the Timed Up and Go in Elderly Patients Post Hip Fracture in a Sub-Acute Rehabilitation Setting

“…Inertial sensors consist of accelerometers and gyroscopes and directly measure angular velocity and linear acceleration of body segments, from which temporal and spatial gait parameters can 7 be measured (Howcroft et al, 2013). There were some disadvantages identified with the use of sensors, as they need to be routinely charged, and need to be wellpositioned and oriented or they can suffer from noise or drift.…”

“…Inertial sensors have the potential to provide a quantitative, objective, and reliable indication of falls risk in the geriatric population (Howcroft et al, 2013). The concept of this falls risk measurement involves the analysis of signals collected during movement via body-worn inertial sensors and the use of algorithms to predict falls risk (Shany et al, 2012).…”