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