TY - GEN
T1 - BLE Beacon-based Activity Monitoring System toward Automatic Generation of Daily Report
AU - Morita, Tatsuya
AU - Taki, Kenta
AU - Fujimoto, Manato
AU - Suwa, Hirohiko
AU - Arakawa, Yutaka
AU - Yasumoto, Keiichi
N1 - Funding Information:
ACKNOWLEDGMENTS This work is partly supported by the Japanese Government Monbukagakusho: JSPS KAKENHI Grant Number 16H01721, 26700007, 16K00126. Also, we would like to
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/2
Y1 - 2018/10/2
N2 - As the world's population of senior citizens continues to grow, the burdens on the professionals who care for them (carers) are also increasing. In nursing homes, carers need to make a daily report for each resident aiming to improve his/her quality of life. However, in the present understaffed situation, it is difficult and burdensome for carers to record the resident's activity in detail since each carer needs to take care of several residents at the same time. In this paper, we propose an automatic daily report generation system which can monitor the activity of multiple residents in nursing homes. Knowing that important activities such as toilet, bathing, rehabilitation and so on take place in specific areas in a nursing home, it is possible to record residents' activities by tracking their stay areas and movement between the areas within the day. Our proposed system estimates stay areas of multiple residents by machine learning for RSSI values that are sent from BLE beacons attached to residents and received at BLE scanners deployed over multiple areas, and records activities of the residents determined based on their estimated stay areas. The proposed system can also output a daily report of each resident based on the recorded data. We carried out a five-day experiment with four elderly participants in a nursing home and evaluated activity estimation accuracy by leave-one-person-out cross-validation. As a result, our proposed system achieved the weighted average F-measure of 81.6%.
AB - As the world's population of senior citizens continues to grow, the burdens on the professionals who care for them (carers) are also increasing. In nursing homes, carers need to make a daily report for each resident aiming to improve his/her quality of life. However, in the present understaffed situation, it is difficult and burdensome for carers to record the resident's activity in detail since each carer needs to take care of several residents at the same time. In this paper, we propose an automatic daily report generation system which can monitor the activity of multiple residents in nursing homes. Knowing that important activities such as toilet, bathing, rehabilitation and so on take place in specific areas in a nursing home, it is possible to record residents' activities by tracking their stay areas and movement between the areas within the day. Our proposed system estimates stay areas of multiple residents by machine learning for RSSI values that are sent from BLE beacons attached to residents and received at BLE scanners deployed over multiple areas, and records activities of the residents determined based on their estimated stay areas. The proposed system can also output a daily report of each resident based on the recorded data. We carried out a five-day experiment with four elderly participants in a nursing home and evaluated activity estimation accuracy by leave-one-person-out cross-validation. As a result, our proposed system achieved the weighted average F-measure of 81.6%.
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U2 - 10.1109/PERCOMW.2018.8480348
DO - 10.1109/PERCOMW.2018.8480348
M3 - Conference contribution
AN - SCOPUS:85056448035
T3 - 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
SP - 788
EP - 793
BT - 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
Y2 - 19 March 2018 through 23 March 2018
ER -