Daily living activity recognition with ECHONET Lite appliances and motion sensors

Kazuki Moriya, Eri Nakagawa, Manato Fujimoto, Hirohiko Suwa, Yutaka Arakawa, Aki Kimura, Satoko Miki, Keiichi Yasumoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages437-442
Number of pages6
ISBN (Electronic)9781509043385
DOIs
Publication statusPublished - May 2 2017
Externally publishedYes
Event2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017 - Kona, Big Island, United States
Duration: Mar 13 2017Mar 17 2017

Publication series

Name2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017

Conference

Conference2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017
CountryUnited States
CityKona, Big Island
Period3/13/173/17/17

Fingerprint

Sensors
Testbeds
Sensor networks
Learning systems
Costs
Network protocols
Monitoring
Internet of things

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications

Cite this

Moriya, K., Nakagawa, E., Fujimoto, M., Suwa, H., Arakawa, Y., Kimura, A., ... Yasumoto, K. (2017). Daily living activity recognition with ECHONET Lite appliances and motion sensors. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017 (pp. 437-442). [7917603] (2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PERCOMW.2017.7917603

Daily living activity recognition with ECHONET Lite appliances and motion sensors. / Moriya, Kazuki; Nakagawa, Eri; Fujimoto, Manato; Suwa, Hirohiko; Arakawa, Yutaka; Kimura, Aki; Miki, Satoko; Yasumoto, Keiichi.

2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 437-442 7917603 (2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Moriya, K, Nakagawa, E, Fujimoto, M, Suwa, H, Arakawa, Y, Kimura, A, Miki, S & Yasumoto, K 2017, Daily living activity recognition with ECHONET Lite appliances and motion sensors. in 2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017., 7917603, 2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017, Institute of Electrical and Electronics Engineers Inc., pp. 437-442, 2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017, Kona, Big Island, United States, 3/13/17. https://doi.org/10.1109/PERCOMW.2017.7917603
Moriya K, Nakagawa E, Fujimoto M, Suwa H, Arakawa Y, Kimura A et al. Daily living activity recognition with ECHONET Lite appliances and motion sensors. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 437-442. 7917603. (2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017). https://doi.org/10.1109/PERCOMW.2017.7917603
Moriya, Kazuki ; Nakagawa, Eri ; Fujimoto, Manato ; Suwa, Hirohiko ; Arakawa, Yutaka ; Kimura, Aki ; Miki, Satoko ; Yasumoto, Keiichi. / Daily living activity recognition with ECHONET Lite appliances and motion sensors. 2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 437-442 (2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017).
@inproceedings{4824a2381f104df99dc3218385246701,
title = "Daily living activity recognition with ECHONET Lite appliances and motion sensors",
abstract = "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.",
author = "Kazuki Moriya and Eri Nakagawa and Manato Fujimoto and Hirohiko Suwa and Yutaka Arakawa and Aki Kimura and Satoko Miki and Keiichi Yasumoto",
year = "2017",
month = "5",
day = "2",
doi = "10.1109/PERCOMW.2017.7917603",
language = "English",
series = "2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "437--442",
booktitle = "2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017",
address = "United States",

}

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

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.

ER -