TY - GEN
T1 - Operation assistance for the Bio-Remote environmental control system using a Bayesian Network-based prediction model
AU - Shibanoki, Taro
AU - Nakamura, Go
AU - Shima, Keisuke
AU - Chin, Takaaki
AU - Tsuji, Toshio
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/4
Y1 - 2015/11/4
N2 - This paper proposes a Bayesian Network (BN) based prediction model for a layer-based selection and its application to an operation assistance for the environmental control system Bio-Remote (BR). In the proposed method, each node of the BN model is involved in the layer-based selection function, which corresponds to an individual operation command, appliance, etc., and previous logs of operation commands and time division are used as input factors to predict the user's intended operation. The prediction results are displayed on the layer-based selection for the BR, and the number of times of operations and time taken for the operations can be reduced with the proposed prediction model. In the experiments, life-logs were collected from a cervical spinal injury patient who used the BR in daily life, and the proposed model was trained based on these recorded life-logs. The prediction accuracy for control devices of the BR system using the proposed model was 84.3 ± 6.5 %. The results indicated that the proposed prediction model could be useful for the operation assistance of the BR system.
AB - This paper proposes a Bayesian Network (BN) based prediction model for a layer-based selection and its application to an operation assistance for the environmental control system Bio-Remote (BR). In the proposed method, each node of the BN model is involved in the layer-based selection function, which corresponds to an individual operation command, appliance, etc., and previous logs of operation commands and time division are used as input factors to predict the user's intended operation. The prediction results are displayed on the layer-based selection for the BR, and the number of times of operations and time taken for the operations can be reduced with the proposed prediction model. In the experiments, life-logs were collected from a cervical spinal injury patient who used the BR in daily life, and the proposed model was trained based on these recorded life-logs. The prediction accuracy for control devices of the BR system using the proposed model was 84.3 ± 6.5 %. The results indicated that the proposed prediction model could be useful for the operation assistance of the BR system.
UR - http://www.scopus.com/inward/record.url?scp=84953275136&partnerID=8YFLogxK
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U2 - 10.1109/EMBC.2015.7318572
DO - 10.1109/EMBC.2015.7318572
M3 - Conference contribution
C2 - 26736472
AN - SCOPUS:84953275136
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1160
EP - 1163
BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Y2 - 25 August 2015 through 29 August 2015
ER -