TY - GEN
T1 - ALPAS
T2 - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017
AU - Kashimoto, Yukitoshi
AU - Fujiwara, Masashi
AU - Fujimoto, Manato
AU - Suwa, Hirohiko
AU - Arakawa, Yutaka
AU - Yasumoto, Keiichi
PY - 2017/5/5
Y1 - 2017/5/5
N2 - These days, smart home applications such as a concierge service for residents, home appliance control and so on are attracting attention. In order to realize these applications, we strongly believe that we need a system which recognizes the various human activities accurately with a low cost device. There are many studies which work on the activity recognition in the smarthome. Moreover, we also have proposed the activity recognition technique in the smarthome by utilizing the digital-output-PIR sensor, door sensor, watt meter. However, the study has the challenge: we cannot distinguish between the similar tiny activities at the same place: 'eating' and 'reading' with sitting on a sofa. In order to cope with this challenge, we introduce ALPAS: analog-output-PIR-sensor-based activity recognition technique which recognizes the detailed activities of the user. Our technique recognizes the activity of the user by utilizing the machine learning. We evaluated the proposed technique in a smarthome which belongs to the authors' university. In the evaluation, three subjects performed four different activities with sitting on a sofa. As a result, we achieved F-Measure: 57.0%.
AB - These days, smart home applications such as a concierge service for residents, home appliance control and so on are attracting attention. In order to realize these applications, we strongly believe that we need a system which recognizes the various human activities accurately with a low cost device. There are many studies which work on the activity recognition in the smarthome. Moreover, we also have proposed the activity recognition technique in the smarthome by utilizing the digital-output-PIR sensor, door sensor, watt meter. However, the study has the challenge: we cannot distinguish between the similar tiny activities at the same place: 'eating' and 'reading' with sitting on a sofa. In order to cope with this challenge, we introduce ALPAS: analog-output-PIR-sensor-based activity recognition technique which recognizes the detailed activities of the user. Our technique recognizes the activity of the user by utilizing the machine learning. We evaluated the proposed technique in a smarthome which belongs to the authors' university. In the evaluation, three subjects performed four different activities with sitting on a sofa. As a result, we achieved F-Measure: 57.0%.
UR - http://www.scopus.com/inward/record.url?scp=85019733237&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019733237&partnerID=8YFLogxK
U2 - 10.1109/AINA.2017.33
DO - 10.1109/AINA.2017.33
M3 - Conference contribution
AN - SCOPUS:85019733237
T3 - Proceedings - International Conference on Advanced Information Networking and Applications, AINA
SP - 880
EP - 885
BT - Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017
A2 - Enokido, Tomoya
A2 - Hsu, Hui-Huang
A2 - Lin, Chi-Yi
A2 - Takizawa, Makoto
A2 - Barolli, Leonard
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 March 2017 through 29 March 2017
ER -