OBJECTIVE: The aim of this study was to develop models for the detection of type, duration, and intensity of human physical activity using one triaxial accelerometer. METHODS: Twenty subjects (age = 29 +/- 6 yr, BMI = 23.6 +/- 3.2 kg.m) performed 20 selected activities, including walking, running, and cycling, wearing one triaxial accelerometer mounted on the lower back. Identification of activity type was based on a decision tree. The decision tree evaluated attributes (features) of the acceleration signal. The features were measured in intervals of defined duration (segments). Segment size determined the time resolution of the decision tree to assess activity duration. Decision trees with a time resolution of 0.4, 0.8, 1.6, 3.2, 6.4, and 12.8 s were developed, and the respective classification performances were evaluated. Multiple linear regression was used to estimate speed of walking, running, and cycling based on acceleration features. RESULTS: Maximal accuracy for the classification of activity type (93%) was reached when the segment size of analysis was 6.4 or 12.8 s. The smaller the segment size, the lower the classification accuracy achieved. Segments of 6.4 s gave the highest time resolution for measuring activity duration without decreasing the classification accuracy. The developed models estimated walking, running, and cycling speeds with a standard error of 0.20, 1.26, and 1.36 km.h, respectively. CONCLUSIONS: This study demonstrated the ability of a triaxial accelerometer in detecting type, duration, and intensity of physical activity using models based on acceleration features. Future studies are needed to validate the presented models in free-living conditions.
OBJECTIVE: The aim of this study was to develop models for the detection of type, duration, and intensity of human physical activity using one triaxial accelerometer. METHODS: Twenty subjects (age = 29 +/- 6 yr, BMI = 23.6 +/- 3.2 kg.m) performed 20 selected activities, including walking, running, and cycling, wearing one triaxial accelerometer mounted on the lower back. Identification of activity type was based on a decision tree. The decision tree evaluated attributes (features) of the acceleration signal. The features were measured in intervals of defined duration (segments). Segment size determined the time resolution of the decision tree to assess activity duration. Decision trees with a time resolution of 0.4, 0.8, 1.6, 3.2, 6.4, and 12.8 s were developed, and the respective classification performances were evaluated. Multiple linear regression was used to estimate speed of walking, running, and cycling based on acceleration features. RESULTS: Maximal accuracy for the classification of activity type (93%) was reached when the segment size of analysis was 6.4 or 12.8 s. The smaller the segment size, the lower the classification accuracy achieved. Segments of 6.4 s gave the highest time resolution for measuring activity duration without decreasing the classification accuracy. The developed models estimated walking, running, and cycling speeds with a standard error of 0.20, 1.26, and 1.36 km.h, respectively. CONCLUSIONS: This study demonstrated the ability of a triaxial accelerometer in detecting type, duration, and intensity of physical activity using models based on acceleration features. Future studies are needed to validate the presented models in free-living conditions.
Authors: B Adar Emken; Ming Li; Gautam Thatte; Sangwon Lee; Murali Annavaram; Urbashi Mitra; Shrikanth Narayanan; Donna Spruijt-Metz Journal: J Phys Act Health Date: 2011-05-11
Authors: Genevieve N Healy; Bronwyn K Clark; Elisabeth A H Winkler; Paul A Gardiner; Wendy J Brown; Charles E Matthews Journal: Am J Prev Med Date: 2011-08 Impact factor: 5.043