TY - GEN
T1 - Wearable sensor-based activity recognition for housekeeping task
AU - Liu, Kai Chun
AU - Yen, Chien Yi
AU - Chang, Li Han
AU - Hsieh, Chia Yeh
AU - Chan, Chia Tai
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/30
Y1 - 2017/5/30
N2 - In order to improve healthcare services and support clinical professionals, it is important to develop the unobstructive and automatic ADLs monitoring system for healthcare applications. Currently, various works have been developed for the monitoring of daily activities, such as ambulation, kitchen task, food and fluid intake, dressing, and medication intake while only few works paid attention to the housekeeping task. Housekeeping activity is a complex task, generally important for the several clinical assessment tools. In this work, we design and develop a wearable sensor-based activity recognition system recognize housekeeping tasks and classify the activity level. The proposed system achieves 90.67% accuracy for housekeeping tasks recognition, and 94.35% accuracy for activity level classification, respectively. The results of the experiment demonstrate that the system is reliable and fulfills the requirements of the unobstructive, objective, and long-Term monitoring system.
AB - In order to improve healthcare services and support clinical professionals, it is important to develop the unobstructive and automatic ADLs monitoring system for healthcare applications. Currently, various works have been developed for the monitoring of daily activities, such as ambulation, kitchen task, food and fluid intake, dressing, and medication intake while only few works paid attention to the housekeeping task. Housekeeping activity is a complex task, generally important for the several clinical assessment tools. In this work, we design and develop a wearable sensor-based activity recognition system recognize housekeeping tasks and classify the activity level. The proposed system achieves 90.67% accuracy for housekeeping tasks recognition, and 94.35% accuracy for activity level classification, respectively. The results of the experiment demonstrate that the system is reliable and fulfills the requirements of the unobstructive, objective, and long-Term monitoring system.
UR - http://www.scopus.com/inward/record.url?scp=85025466428&partnerID=8YFLogxK
U2 - 10.1109/BSN.2017.7936009
DO - 10.1109/BSN.2017.7936009
M3 - Conference contribution
AN - SCOPUS:85025466428
T3 - 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2017
SP - 67
EP - 70
BT - 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2017
Y2 - 9 May 2017 through 12 May 2017
ER -
