Validation of an accelerometer for measurement of activity in frail older people

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.

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.

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.

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.