TY - GEN
T1 - Detection of Anomaly Health Data by Specifying Latent Factors with SEM and Estimating Hidden States with HMM
AU - Tago, Kiichi
AU - Jin, Qun
N1 - Funding Information:
The work was partly supported by 2016–2018 Masaru Ibuka Foundation Research Project on Oriental Medicine.
Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/12/26
Y1 - 2018/12/26
N2 - Nowadays, it has become convenient to record data related to an individual using a wearable device. However, it is difficult to utilize the data according to the individual, especially to anomaly detection. Anomaly detection is very important for healthcare, e.g., early detecting of illness. In our previous study, we proposed an approach to specifying latent factors using Structural Equation Modeling (SEM). In this paper, we propose an improved approach for anomaly detection taking account of personal status based on latent factors. To estimate the states, we adopt Hidden Markov Model (HMM). Moreover, we use Hotelling's theory to detect abnormal data statistically. By using our approach, even if states can not be explicitly obtained from a device, hidden states can be estimated to perform anomaly detection in more details.
AB - Nowadays, it has become convenient to record data related to an individual using a wearable device. However, it is difficult to utilize the data according to the individual, especially to anomaly detection. Anomaly detection is very important for healthcare, e.g., early detecting of illness. In our previous study, we proposed an approach to specifying latent factors using Structural Equation Modeling (SEM). In this paper, we propose an improved approach for anomaly detection taking account of personal status based on latent factors. To estimate the states, we adopt Hidden Markov Model (HMM). Moreover, we use Hotelling's theory to detect abnormal data statistically. By using our approach, even if states can not be explicitly obtained from a device, hidden states can be estimated to perform anomaly detection in more details.
KW - Anomaly detection
KW - Health data
KW - Hidden Markov Model (HMM)
KW - Structural Equation Modeling (SEM)
UR - http://www.scopus.com/inward/record.url?scp=85061313155&partnerID=8YFLogxK
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U2 - 10.1109/ITME.2018.00040
DO - 10.1109/ITME.2018.00040
M3 - Conference contribution
AN - SCOPUS:85061313155
T3 - Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018
SP - 137
EP - 141
BT - Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Information Technology in Medicine and Education, ITME 2018
Y2 - 19 October 2018 through 21 October 2018
ER -