TY - GEN
T1 - Indoor Occupancy Estimation via Location-Aware HMM
T2 - 19th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2018
AU - Yoshida, Masahiro
AU - Kleisarchaki, Sofia
AU - Gtirgen, Levent
AU - Nishi, Hiroaki
N1 - Funding Information:
This work was partially supported by the EU-Activage project Grant No 732679 and MEXT/JSPS KAKENHI Grant (B) Number JP16H04455 and JP17H01739 and by Technology Foundation of the R&D project ”Design of Information and Communication Platform for Future Smart Community Services” by the Ministry of Internal Affairs and Communications of Japan.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/28
Y1 - 2018/8/28
N2 - Indoor occupancy estimation is a critical analytical task for several applications (e.g., social isolation of elderlies). The proliferation of Internet of Things (IoT) devices enabled the occupancy estimation, as it provided access to a mass amount of data. Several works have been proposed exploiting the IoT Passive Inference (PIR) or environmental (e.g., CO2) features. These works however are traditionally selecting the feature space at the learning phase and passively using it over time. Hence, they ignore the dynamics of indoor occupancy, such as the location of the occupant or his motion patterns, leading to a decreasing accuracy over time. In this paper, we study those dynamics and show that motion patterns, along with environmental features favor the occupancy estimation. We design a Location-Aware Hidden Markov Model (HMM), which dynamically adapts the feature space based on the occupant's location. Our experiments on real data show that Location-Aware HMM can reach up to 10% better accuracy than Conventional HMM.
AB - Indoor occupancy estimation is a critical analytical task for several applications (e.g., social isolation of elderlies). The proliferation of Internet of Things (IoT) devices enabled the occupancy estimation, as it provided access to a mass amount of data. Several works have been proposed exploiting the IoT Passive Inference (PIR) or environmental (e.g., CO2) features. These works however are traditionally selecting the feature space at the learning phase and passively using it over time. Hence, they ignore the dynamics of indoor occupancy, such as the location of the occupant or his motion patterns, leading to a decreasing accuracy over time. In this paper, we study those dynamics and show that motion patterns, along with environmental features favor the occupancy estimation. We design a Location-Aware Hidden Markov Model (HMM), which dynamically adapts the feature space based on the occupant's location. Our experiments on real data show that Location-Aware HMM can reach up to 10% better accuracy than Conventional HMM.
KW - Internet of Things (IoT)
KW - Location-Aware Hidden Markov Model
KW - Occupancy Estimation
UR - http://www.scopus.com/inward/record.url?scp=85053771895&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053771895&partnerID=8YFLogxK
U2 - 10.1109/WoWMoM.2018.8449765
DO - 10.1109/WoWMoM.2018.8449765
M3 - Conference contribution
AN - SCOPUS:85053771895
SN - 9781538647257
T3 - 19th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2018
BT - 19th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 June 2018 through 15 June 2018
ER -