TY - GEN
T1 - A two-layer hierarchical framework for activity sequence recognition by wearable sensors
AU - Chan, Guo Jing
AU - Lin, Dong Hung
AU - Ik, Tsi-Ui
AU - Tseng, Chien-Chao
N1 - Publisher Copyright:
© 2016 IEICE.
PY - 2016/11/7
Y1 - 2016/11/7
N2 - As the aging population grows, the elderly care service has become an important part of the service industry in the aging population era. Activity monitoring is one of the most important services in the field of the elderly care service. In this paper, we proposed a wearable solution to provide an activity monitoring service on elders for caregivers. This service monitors restroom activities, such as washing hands, urinating and defecation. In the proposed solution, wireless motion sensors are wore on elder's wrist and waist to measure their body movement. The measured motion data are processed to statistical features and aggregated to cloud servers through gateways. A two-layer hierarchical framework is used for the activity recognition. In the first layer, a preliminary recognition is performed by a supervised Reduced Error Pruning (REP) Tree classifier to detect the transition of the activity. In the second layer, a Variable Order Hidden Markov Model (VOHMM) is proposed to determine the sequence of the activities. The experiment results show that the recognition accuracy is 70 percent. We developed a prototype service App to provide a life log for the recording of the activity sequence. The caregivers can make use of this information to take necessary actions accordingly.
AB - As the aging population grows, the elderly care service has become an important part of the service industry in the aging population era. Activity monitoring is one of the most important services in the field of the elderly care service. In this paper, we proposed a wearable solution to provide an activity monitoring service on elders for caregivers. This service monitors restroom activities, such as washing hands, urinating and defecation. In the proposed solution, wireless motion sensors are wore on elder's wrist and waist to measure their body movement. The measured motion data are processed to statistical features and aggregated to cloud servers through gateways. A two-layer hierarchical framework is used for the activity recognition. In the first layer, a preliminary recognition is performed by a supervised Reduced Error Pruning (REP) Tree classifier to detect the transition of the activity. In the second layer, a Variable Order Hidden Markov Model (VOHMM) is proposed to determine the sequence of the activities. The experiment results show that the recognition accuracy is 70 percent. We developed a prototype service App to provide a life log for the recording of the activity sequence. The caregivers can make use of this information to take necessary actions accordingly.
KW - activity monitoring
KW - activity sequence
KW - activity transition
KW - variable order hidden Markov model
UR - http://www.scopus.com/inward/record.url?scp=85006412394&partnerID=8YFLogxK
U2 - 10.1109/APNOMS.2016.7737259
DO - 10.1109/APNOMS.2016.7737259
M3 - Conference contribution
AN - SCOPUS:85006412394
T3 - 18th Asia-Pacific Network Operations and Management Symposium, APNOMS 2016: Management of Softwarized Infrastructure - Proceedings
BT - 18th Asia-Pacific Network Operations and Management Symposium, APNOMS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th Asia-Pacific Network Operations and Management Symposium, APNOMS 2016
Y2 - 5 October 2016 through 7 October 2016
ER -