Review of fall risk assessment in geriatric populations using inertial sensors

Jennifer Howcroft,Edward D Lemaire,Jonathan Kofman

doi:10.1186/1743-0003-10-91

Jennifer Howcroft, Edward D Lemaire + Show 1 more

Open Access

https://doi.org/10.1186/1743-0003-10-91

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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.

Highlights

One third of people over 65 years of age will fall each year [1,2], with the fall rate increasing with age [3,4] and for those in long-term care [5]
Fall risk classification Three main methods were used to classify subjects into faller and non-faller categories: retrospective fall history (30%), prospective fall occurrence (15%), and scores on clinical assessments (32.5%)
Inertial sensors Two different inertial sensors were used in the reviewed papers: gyroscopes and accelerometers [76]

Summary

Introduction

One third of people over 65 years of age will fall each year [1,2], with the fall rate increasing with age [3,4] and for those in long-term care [5]. The direct-care costs of fall related injuries could reach $32.4 billion per year by 2020 [7]. The first uses physical-monitoring devices to detect falls and signal for immediate care. This approach can only reduce consequence severity. The second approach prevents fall occurrence through interventions such as exercise [15,16], improved footwear [15], assistive devices [16], adaptation or modification of the home environment [15,16], review and modification of medication [15], and increased surveillance and care by caregivers [16]. Falls are a prevalent issue in the geriatric population and can result in damaging physical and psychological consequences. Wearable inertial-sensor-based systems can provide quantitative measures indicative of fall risk in the geriatric population

Results

Discussion

Conclusion