Comparison of Activity Type Classification Accuracy from Accelerometers Worn on the Hip,Wrists, and Thigh in Young,Apparently Healthy Adults |
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| Authors: | Alexander H K Montoye James M Pivarnik Lanay M Mudd Subir Biswas Karin A Pfeiffer |
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| Institution: | 1. Clinical Exercise Physiology Program, Ball State University, Muncie, Indiana;2. Department of Integrative Physiology and Health Science, Alma College, Alma, Michigan;3. Department of Kinesiology, Michigan State University, East Lansing, Michigan;4. Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan |
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| Abstract: | The purpose of this article is to compare accuracy of activity type prediction models for accelerometers worn on the hip, wrists, and thigh. Forty-four adults performed sedentary, ambulatory, lifestyle, and exercise activities (14 total, 10 categories) for 3–10 minutes each in a 90-minute semi-structured laboratory protocol. Artificial neural networks (ANNs) were developed for four accelerometers (right hip, both wrists, and right thigh,) to predict individual activities and activity categories, with direct observation (DO) as criterion. The wrist-mounted accelerometers achieved the highest accuracy for individual activities (80.9%–81.1%) and activity categories (86.6%–86.7%); accuracy was not different between wrists. The hip-mounted accelerometer had the lowest accuracy (66.2% individual activities, 72.5% activity categories); thigh-mounted accelerometer accuracy (71.4% individual activities, 84.0% activity categories) fell between the wrist- and hip-mounted accelerometers. ANNs developed for accelerometers worn on the wrists and thigh provided high accuracy for activity type prediction and represent a potential approach to physical activity (PA) assessment. |
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| Keywords: | activity monitor activity recognition artificial neural network machine learning physical activity |
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