Commenced in January 2007
Paper Count: 32131
Classification of State Transition by Using a Microwave Doppler Sensor for Wandering Detection
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1314851Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 726
 “World Population Ageing,” United Nations, Department of Economic and Social Affairs, Population Division, New York, USA, ST/ESA/SER.A/390, p. 2 2015.
 J. Heinik: “Families’ and professional caregivers’ R. Laudau, G.K. Auslander, S. Werner, N. Shoval, and views of using advanced technology to track people with dementia”, Psychogeriatrics, Vol. 1, No. 2, pp.20-35, 2010.
 G. Cipriani, C. Lucetti, A. Nuti, and S. Danti, “Wandering and Dementia”, Psychogeriatrics, Vol.14, No. 2, pp. 135-142, 2014
 A. Solanas, E. Batista, F. Borras, A. M. Balleste, and C. Patsakis, “Wandering analysis with mobile phones: On the relation between randomness and wandering”, International Conference on Pervasive and Embedded Computing and Communication Systems, pp. 168-173, 2015
 C.-Y. Ko, F.-Y. Leu, and I.-T. Lin, “A Wandering Path Tracking and Fall Detection System for People with Dementia”, Broadband and Wireless Computing, Communication and Applications, pp.306-311, 2014.DOI:10.1109/BWCCA.2014.127
 S. Elloumi, S. cosar, G. Pusiol, F. Bremond and M. Thonnat, “Unsupervised discovery of human activities from long-time videos”, Institution of Engineering and Technology, vol. 9, no. 4, pp. 522-530, 2015.
 J. 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. Ma, F. Hu, and Q. Hao, “Active Compressive Sensing via Pyroelectric Infrared Sensor for Human Situation Recognition”, IEEE Transactions on Man, and Cybernetics: Systems, DOI: 10.1109/TSMC.2016.2578465, 2017.
 M. Sekine, K. Maeno, and T. Kamakura, “Human Detection Algorithm for Doppler Radar Using Prediction Error in Autoregressive Model”, IEEE International Symposium on Instrumentation and Control Technology, pp. 37–40, 2012.
 D. 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. 13–18, 2013.
 K. Shiba, T. Kaburagi, T. Nakamura, K. Ozaki and Y. Kurihara, “Feature Selection Guideline for Presence/Absence classification Using a Microwave Doppler Sensor” , SICE journal of Control, Measurement, and System Integration, vol. 10, no. 5, pp. 418-425, 2017.