Elsevier

Gait & Posture

Volume 66, October 2018, Pages 114-117
Gait & Posture

Full length article
Validation of an accelerometer for measurement of activity in frail older people

https://doi.org/10.1016/j.gaitpost.2018.08.024Get rights and content

Highlights

  • Accelerometer validation for activity & gait detection-frail older people (>75 years).

  • Study carried out in Free-living/out-of-clinical lab conditions.

  • Data is inertial signals from a single waist-worn wearable device.

  • Method - Comparison between processed accelerometer data and annotated video.

  • 92.8% and 95.1% of walking episodes detected for unscripted and scripted activities.

Abstract

Background

Specific gait parameters are associated with falls and injury. It is important to identify walking episodes in order to determine the associated gait parameters. Frail older people have a greater risk of falling due to increased probability of inactivity. Therefore, detection and analysis of their physical activities becomes significant. Furthermore, ascertainment of gait parameters and non-sedentary activities for frail older group is difficult in free living environments – an area which hasn’t been explored much.

Methods

Participants were 23 older people residing in independent-living retirement homes. Data was inertial sensor signals, attached to the L5 vertebral area using a belt, from scripted activities (a timed up and go, and sit to stand activities) and unscripted activities of daily living collected in a free-living environment. An algorithm designed to identify walking, standing/sitting and lying is applied to the uSense wearable accelerometer data which has been analysed by processing the raw data with a gait detection algorithm and the results were compared against annotated videos which served as the gold standard. Validity of gait assessment was based on the percentage of agreement between the analysed accelerometer data and the corresponding reference video with 100Hz sampling frequency and 0.01 frames/second.

Results

The median overall agreement between the processed accelerometer data and the annotated video was a match of approximately 92.8% and 95.1% for walking episodes for unscripted and scripted activities respectively.

Significance

The tri-axial accelerometer with a sampling frequency of 100 Hz provides a valid measure of gait detection in frail older people aged above 75 years. Since a limited number of studies have reported the use of accelerometers for older people in a free-living context, performance evaluation and establishing the validity of body worn sensors for physical activity and gait recognition is the key goal achieved.

Introduction

Physical activity is essential for independence and wellbeing in older people in all settings and at all levels of functional capacity [[1], [2], [3]]. Activity is even more important for frail older people as their absolute risk of functional decline from inactivity is highest [4,5]. Furthermore, sedentary time has been linked to health outcomes [6]. Self-report assessment of activity, the commonest assessment measure, can be limited by cognitive problems and recall bias when used in older people [7]. Therefore, objective measures of activity are needed to assess the amount and type of activity for the purposes of: self-management; estimation of risk of falls [8], functional decline [9]; assessment of the impact of activity programmes [10,11] and for guidance in rehabilitation and maintenance of function. eHealth initiatives, particularly body-worn sensors present the potential for precise measurement of activity [12,13].

In frail older people the identification of walking is still limited due to slower gait speed than younger age groups leading to less pronounced gait signal [15,16].

Non-sedentary activity is pretty important for older people in care homes [6]. Several devices are available but there is always a need for more, and adequate validation is needed [14].

However, only a limited number of studies have reported the use of accelerometers in older people, particularly those over age 75 years. Although there are many devices already existing for accurate detection of gait parameters, measurement has been limited to in-lab facilities. There hasn’t been much investigation of the activities outside the laboratory setup. Advance development of wearable devices and related signal processing algorithms will enable to extend analysis to be carried out in any setting outside of the lab facilities in retirement care homes etc.

uSense was developed with an intention to facilitate better reliable detection of gait and physical activities for frail older people specifically for out-of-lab and free-living environment conditions [14].

This paper uses uSense, a tri-axial accelerometer with an algorithm which would work on several devices.

Therefore the main objective of this study is to validate the performance of uSense in detecting non-sedentary activities, differentiating walking and non-walking episodes for frail older people aged 75 years and above in free-living environment.

Section snippets

Methods and procedure

Older people living in retirement villages were invited to participate in the study through residents’ meetings and letter drops. Those interested were assessed for eligibility, fully informed about the study and gave written informed consent. Inclusion criteria were age of 75 years or over and the ability walk independently with or without a walking aid for a minimum of 20 m. Exclusion criteria were any significant medical, orthopaedic or neurological conditions that would contraindicate

Results

Twenty-nine older people were assessed in their own home. Most participants completed both scripted and unscripted activities. Three participants had unusable videos, and three did not complete both activities. The complete data for 23 older people have been analysed and presented here. Average age was 80.5 years (range: 75–92 years) and 17 were women (74%). TUG measured by the observer was on average x minutes (SD).

During scripted activity the mean non-walking time was 80.0 (SD = 25.1) seconds

Key results

In order to validate the signal processing algorithm for uSense accelerometer, a comparison between the spatial inertial accelerometer signals and the video frames was performed where an overall match of 92.8% and 95.1% for walking episodes for unscripted and scripted activities respectively were attained. In particular, for scripted activity, 97.2% agreement was achieved between the video and the algorithm with a mismatch of 2.8% for non-walking activity. The differentiation of 91.4% walking

Conclusion

The placement of a single waist worn sensor on older people not only reduced discomfort but also maintained good accuracy levels in detecting walking and non-walking episodes. The performance of uSense and the signal processing algorithm have been successfully validated for non-sedentary activity recognition and gait detection in frail older people aged above 75 years in free-living environment. Establishing the validity of body worn sensor for physical activity and gait recognition is the main

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