TY - GEN
T1 - Accelerometry-based motion pattern analysis for physical activity recognition and activity level assessment
AU - Liu, Kai Chun
AU - Liu, Chung Tse
AU - Chen, Chao Wei
AU - Lin, Chih Ching
AU - Chan, Chia Tai
PY - 2014
Y1 - 2014
N2 - Physical inactivity is becoming a major public health concern and lead to a variety of chronic diseases. Since adequate moderate or vigorous activity can reduce the incidence of chronic diseases, noncommunicable disease and obesity. The evidence is supporting the importance of physical activity on health and well-being. However, many people nowadays live without adequate physical activity, and do not aware whether their daily activity is enough or not. The activity recognition and activity level can be used to survey the effectiveness and achievement of goals aimed at increasing physical activity. Physical activity monitoring has become a more proactive healthcare service that should build on the real-time reminding offered by healthcare solutions. Therefore, physical activity monitoring and activity level assessment are critical to maintain adequate physical activity and improve health. In this work, we present a motion patterns analysis for physical activity recognition and activity level assessment by using a wearable sensor. The proposed mechanism uses triaxial accelerometer as a sensing device. The sensor node is mounted in the right front waist, sensing and transmitting sensing data to server. The time series of raw data will be preprocessed through the aggregation technique of jumping window. The raw data will be divided into small segments and separated to gravity signal and body acceleration by filter. Through feature extraction and proposed classifier, motion pattern analysis is achieved. The classifier consists of activity recognition and activity level assessment algorithms. The results have demonstrated that the proposed methods can achieve 94.7%, 87.0% accuracy of activity recognition and activity level estimation respectively.
AB - Physical inactivity is becoming a major public health concern and lead to a variety of chronic diseases. Since adequate moderate or vigorous activity can reduce the incidence of chronic diseases, noncommunicable disease and obesity. The evidence is supporting the importance of physical activity on health and well-being. However, many people nowadays live without adequate physical activity, and do not aware whether their daily activity is enough or not. The activity recognition and activity level can be used to survey the effectiveness and achievement of goals aimed at increasing physical activity. Physical activity monitoring has become a more proactive healthcare service that should build on the real-time reminding offered by healthcare solutions. Therefore, physical activity monitoring and activity level assessment are critical to maintain adequate physical activity and improve health. In this work, we present a motion patterns analysis for physical activity recognition and activity level assessment by using a wearable sensor. The proposed mechanism uses triaxial accelerometer as a sensing device. The sensor node is mounted in the right front waist, sensing and transmitting sensing data to server. The time series of raw data will be preprocessed through the aggregation technique of jumping window. The raw data will be divided into small segments and separated to gravity signal and body acceleration by filter. Through feature extraction and proposed classifier, motion pattern analysis is achieved. The classifier consists of activity recognition and activity level assessment algorithms. The results have demonstrated that the proposed methods can achieve 94.7%, 87.0% accuracy of activity recognition and activity level estimation respectively.
KW - Accelerometer
KW - Activity level assessment
KW - Activity recognition
KW - Wearable sensor
UR - http://www.scopus.com/inward/record.url?scp=84891056594&partnerID=8YFLogxK
U2 - 10.4028/www.scientific.net/AMM.479-480.818
DO - 10.4028/www.scientific.net/AMM.479-480.818
M3 - Conference contribution
AN - SCOPUS:84891056594
SN - 9783037859476
T3 - Applied Mechanics and Materials
SP - 818
EP - 822
BT - Applied Science and Precision Engineering Innovation
T2 - International Applied Science and Precision Engineering Conference 2013, ASPEC 2013
Y2 - 18 October 2013 through 22 October 2013
ER -