TY - GEN
T1 - Monitoring walking and cycling of middle-aged to older community dwellers using wireless wearable accelerometers
AU - Zhang, Yuting
AU - Beenakker, Karel G.M.
AU - Butala, Pankil M.
AU - Lin, Cheng Chieh
AU - Little, Thomas D.C.
AU - Maier, Andrea B.
AU - Stijntjes, Marjon
AU - Vartanian, Richard
AU - Wagenaar, Robert C.
PY - 2012/11/12
Y1 - 2012/11/12
N2 - Changes in gait parameters have been shown to be an important indicator of several age-related cognitive and physical declines of older adults. In this paper we propose a method to monitor and analyze walking and cycling activities based on a triaxial accelerometer worn on one ankle. We use an algorithm that can (1) distinguish between static and dynamic functional activities, (2) detect walking and cycling events, (3) identify gait parameters, including step frequency, number of steps, number of walking periods, and total walking duration per day, and (4) evaluate cycling parameters, including cycling frequency, number of cycling periods, and total cycling duration. Our algorithm is evaluated against the triaxial accelerometer data obtained from a group of 297 middle-aged to older adults wearing an activity monitor on the right ankle for approximately one week while performing unconstrained daily activities in the home and community setting. The correlation coefficients between each of detected gait and cycling parameters on two weekdays are all statistically significant, ranging from 0.668 to 0.873. These results demonstrate good test-retest reliability of our method in monitoring walking and cycling activities and analyzing gait and cycling parameters. This algorithm is efficient and causal in time and thus implementable for real-time monitoring and feedback.
AB - Changes in gait parameters have been shown to be an important indicator of several age-related cognitive and physical declines of older adults. In this paper we propose a method to monitor and analyze walking and cycling activities based on a triaxial accelerometer worn on one ankle. We use an algorithm that can (1) distinguish between static and dynamic functional activities, (2) detect walking and cycling events, (3) identify gait parameters, including step frequency, number of steps, number of walking periods, and total walking duration per day, and (4) evaluate cycling parameters, including cycling frequency, number of cycling periods, and total cycling duration. Our algorithm is evaluated against the triaxial accelerometer data obtained from a group of 297 middle-aged to older adults wearing an activity monitor on the right ankle for approximately one week while performing unconstrained daily activities in the home and community setting. The correlation coefficients between each of detected gait and cycling parameters on two weekdays are all statistically significant, ranging from 0.668 to 0.873. These results demonstrate good test-retest reliability of our method in monitoring walking and cycling activities and analyzing gait and cycling parameters. This algorithm is efficient and causal in time and thus implementable for real-time monitoring and feedback.
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U2 - 10.1109/EMBC.2012.6345895
DO - 10.1109/EMBC.2012.6345895
M3 - Conference contribution
C2 - 23365856
AN - SCOPUS:84881061059
SN - 9781424441198
T3 - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
SP - 158
EP - 161
BT - 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
PB - IEEE
T2 - 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Y2 - 28 August 2012 through 1 September 2012
ER -