Volume XLI-B2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 505-507, 2016
https://doi.org/10.5194/isprs-archives-XLI-B2-505-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 505-507, 2016
https://doi.org/10.5194/isprs-archives-XLI-B2-505-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  08 Jun 2016

08 Jun 2016

MINING SPATIOTEMPORAL PATTERNS OF THE ELDER’S DAILY MOVEMENT

C. R. Chen1, C. F. Chen1, M. E. Liu2, S. J. Tsai2,3, N. T. Son1, and L. V. Kinh1 C. R. Chen et al.
  • 1Center for Space and Remote Sensing Research, National Central University, Taoyuan, Taiwan 32001
  • 2Department of Psychiatry, Taipei Veteran General Hospital, Taipei, Taiwan 11217
  • 3Psychiatric Division, School of Medicine, National Yang-Ming University, Taipei, Taiwan 11221

Keywords: Geographic Information System (GIS), wearable device, daily movement pattern

Abstract. With rapid developments in wearable device technology, a vast amount of spatiotemporal data, such as people’s movement and physical activities, are generated. Information derived from the data reveals important knowledge that can contribute a long-term care and psychological assessment of the elders’ living condition especially in long-term care institutions. This study aims to develop a method to investigate the spatial-temporal movement patterns of the elders with their outdoor trajectory information. To achieve the goal, GPS based location data of the elderly subjects from long-term care institutions are collected and analysed with geographic information system (GIS). A GIS statistical model is developed to mine the elderly subjects’ spatiotemporal patterns with the location data and represent their daily movement pattern at particular time. The proposed method first finds the meaningful trajectory and extracts the frequent patterns from the time-stamp location data. Then, a density-based clustering method is used to identify the major moving range and the gather/stay hotspot in both spatial and temporal dimensions. The preliminary results indicate that the major moving area of the elderly people encompasses their dorm and has a short moving distance who often stay in the same site. Subjects’ outdoor appearance are corresponded to their life routine. The results can be useful for understanding elders’ social network construction, risky area identification and medical care monitoring.