{"title":"Classification of State Transition by Using a Microwave Doppler Sensor for Wandering Detection ","authors":"K. Shiba, T. Kaburagi, Y. Kurihara","volume":132,"journal":"International Journal of Biomedical and Biological Engineering","pagesStart":679,"pagesEnd":685,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10008353","abstract":"With global aging, people who require care, such as people with dementia (PwD), are increasing within many developed countries. And PwDs may wander and unconsciously set foot outdoors, it may lead serious accidents, such as, traffic accidents. Here, round-the-clock monitoring by caregivers is necessary, which can be a burden for the caregivers. Therefore, an automatic wandering detection system is required when an elderly person wanders outdoors, in which case the detection system transmits a ‘moving’ followed by an ‘absence’ state. In this paper, we focus on the transition from the ‘resting’ to the ‘absence’ state, via the ‘moving’ state as one of the wandering transitions. To capture the transition of the three states, our method based on the hidden Markov model (HMM) is built. Using our method, the restraint where the ‘resting’ state and ‘absence’ state cannot be transmitted to each other is applied. To validate our method, we conducted the experiment with 10 subjects. Our results show that the method can classify three states with 0.92 accuracy.","references":"[1]\t\u201cWorld Population Ageing,\u201d United Nations, Department of Economic and Social Affairs, Population Division, New York, USA, ST\/ESA\/SER.A\/390, p. 2 2015.\r\n[2]\tJ. Heinik: \u201cFamilies\u2019 and professional caregivers\u2019 R. Laudau, G.K. Auslander, S. Werner, N. Shoval, and views of using advanced technology to track people with dementia\u201d, Psychogeriatrics, Vol. 1, No. 2, pp.20-35, 2010.\r\n[3]\tG. Cipriani, C. Lucetti, A. Nuti, and S. Danti, \u201cWandering and Dementia\u201d, Psychogeriatrics, Vol.14, No. 2, pp. 135-142, 2014\r\n[4]\tA. Solanas, E. Batista, F. Borras, A. M. Balleste, and C. Patsakis, \u201cWandering analysis with mobile phones: On the relation between randomness and wandering\u201d, International Conference on Pervasive and Embedded Computing and Communication Systems, pp. 168-173, 2015\r\n[5]\tC.-Y. Ko, F.-Y. Leu, and I.-T. Lin, \u201cA Wandering Path Tracking and Fall Detection System for People with Dementia\u201d, Broadband and Wireless Computing, Communication and Applications, pp.306-311, 2014.DOI:10.1109\/BWCCA.2014.127\r\n[6]\tS. Elloumi, S. cosar, G. Pusiol, F. Bremond and M. Thonnat, \u201cUnsupervised discovery of human activities from long-time videos\u201d, Institution of Engineering and Technology, vol. 9, no. 4, pp. 522-530, 2015.\r\n[7]\tJ. Lu, T. Zhang, F. Hu & Q. Hao, \"Preprocessing Design in Pyroelectric Infrared Sensor-Based Human-Tracking System: On Sensor Selection and Calibration\", IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 2, pp. 263-275, 2017.\r\n[8]\tR. Ma, F. Hu, and Q. Hao, \u201cActive Compressive Sensing via Pyroelectric Infrared Sensor for Human Situation Recognition\u201d, IEEE Transactions on Man, and Cybernetics: Systems, DOI: 10.1109\/TSMC.2016.2578465, 2017.\r\n[9]\tM. Sekine, K. Maeno, and T. Kamakura, \u201cHuman Detection Algorithm for Doppler Radar Using Prediction Error in Autoregressive Model\u201d, IEEE International Symposium on Instrumentation and Control Technology, pp. 37\u201340, 2012.\r\n[10]\tD. Okuya, M. Hiramoto, and K. Maeno: Human Sensing Technique Using Microwave Doppler Radar Based on Higher-order Local Autocorrelation Features, IEICE Technical Report, Vol. 113, No. 28, pp. 13\u201318, 2013.\r\n[11]\tK. Shiba, T. Kaburagi, T. Nakamura, K. Ozaki and Y. Kurihara, \u201cFeature Selection Guideline for Presence\/Absence classification Using a Microwave Doppler Sensor\u201d , SICE journal of Control, Measurement, and System Integration, vol. 10, no. 5, pp. 418-425, 2017.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 132, 2017"}