TY - GEN
T1 - Daily living activity recognition with ECHONET Lite appliances and motion sensors
AU - Moriya, Kazuki
AU - Nakagawa, Eri
AU - Fujimoto, Manato
AU - Suwa, Hirohiko
AU - Arakawa, Yutaka
AU - Kimura, Aki
AU - Miki, Satoko
AU - Yasumoto, Keiichi
PY - 2017/5/2
Y1 - 2017/5/2
N2 - Recently, IoT (Internet of Things) technologies have been attracting increasing attention. Among many applications of IoT, homes can be the most promising target. One of the purposes to deploy IoT in homes is automatic recognition of activities of daily living (ADLs). It is expected that ADL recognition in homes enables many new services such as elderly people monitoring and low energy appliance control. In existing studies on ADL recognition, however, it is hard to build a system to acquire data for ADL recognition in terms of installation cost. In this paper, we propose a method that reduces costs of the ADL recognition system by using ECHONET Lite-ready appliances which are expected to be widely spread in the future. ECHONET Lite is a communication protocol for control and sensor networks in smart-homes and standardized as ISO/IEC-4-3. The proposed method utilizes information (e.g., on/off state) from appliances and motion sensors attached to them as features and recognizes ADLs through machine learning. To evaluate the proposed method, we collected data in our smart-home testbed while several participants are living there. As a result, the proposed method achieved about 68% classification accuracy for 9 different activities.
AB - Recently, IoT (Internet of Things) technologies have been attracting increasing attention. Among many applications of IoT, homes can be the most promising target. One of the purposes to deploy IoT in homes is automatic recognition of activities of daily living (ADLs). It is expected that ADL recognition in homes enables many new services such as elderly people monitoring and low energy appliance control. In existing studies on ADL recognition, however, it is hard to build a system to acquire data for ADL recognition in terms of installation cost. In this paper, we propose a method that reduces costs of the ADL recognition system by using ECHONET Lite-ready appliances which are expected to be widely spread in the future. ECHONET Lite is a communication protocol for control and sensor networks in smart-homes and standardized as ISO/IEC-4-3. The proposed method utilizes information (e.g., on/off state) from appliances and motion sensors attached to them as features and recognizes ADLs through machine learning. To evaluate the proposed method, we collected data in our smart-home testbed while several participants are living there. As a result, the proposed method achieved about 68% classification accuracy for 9 different activities.
UR - http://www.scopus.com/inward/record.url?scp=85019974448&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019974448&partnerID=8YFLogxK
U2 - 10.1109/PERCOMW.2017.7917603
DO - 10.1109/PERCOMW.2017.7917603
M3 - Conference contribution
AN - SCOPUS:85019974448
T3 - 2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017
SP - 437
EP - 442
BT - 2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017
Y2 - 13 March 2017 through 17 March 2017
ER -