TY - GEN
T1 - New monitoring scheme for persons with dementia through monitoring-area adaptation according to stage of disease
AU - Kamada, Shigeki
AU - Matsuo, Yuji
AU - Hara, Sunao
AU - Abe, Masanobu
N1 - Funding Information:
This work was supported by JSPS KAKENHI 15K00128
Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/11/7
Y1 - 2017/11/7
N2 - In this paper, we propose a new monitoring scheme for a person with dementia (PwD). The novel aspect of this monitoring scheme is that the size of the monitoring area changes for different stages of dementia, and the monitoring area is automatically generated using global positioning system (GPS) data collected by the PwD. The GPS data are quantized using the GeoHex code, which breaks down the map of the entire world into regular hexagons. The monitoring area is defined as a set of GeoHex codes, and the size of the monitoring area is controlled by the granularity of hexagons in the GeoHex code. The stages of dementia are estimated by analyzing the monitoring area to determine how frequently the PwD wanders. In this paper, we also examined two aspects of the implementation of the proposed scheme. First, we proposed an algorithm to estimate the monitoring area and evaluate its performance. The experimental results showed that the proposed algorithm can estimate the monitoring area with a precision of 0.82 and recall of 0.86 compared with the ground truth. Second, to investigate privacy considerations, we showed that different persons have different preferences for the granularity of the hexagons in the monitoring systems.1The results indicate that the size of the monitoring area also should be changed for PwDs.
AB - In this paper, we propose a new monitoring scheme for a person with dementia (PwD). The novel aspect of this monitoring scheme is that the size of the monitoring area changes for different stages of dementia, and the monitoring area is automatically generated using global positioning system (GPS) data collected by the PwD. The GPS data are quantized using the GeoHex code, which breaks down the map of the entire world into regular hexagons. The monitoring area is defined as a set of GeoHex codes, and the size of the monitoring area is controlled by the granularity of hexagons in the GeoHex code. The stages of dementia are estimated by analyzing the monitoring area to determine how frequently the PwD wanders. In this paper, we also examined two aspects of the implementation of the proposed scheme. First, we proposed an algorithm to estimate the monitoring area and evaluate its performance. The experimental results showed that the proposed algorithm can estimate the monitoring area with a precision of 0.82 and recall of 0.86 compared with the ground truth. Second, to investigate privacy considerations, we showed that different persons have different preferences for the granularity of the hexagons in the monitoring systems.1The results indicate that the size of the monitoring area also should be changed for PwDs.
KW - Dementia
KW - GPS
KW - Living area
KW - Location-based services
KW - Monitoring system
KW - Privacy
UR - http://www.scopus.com/inward/record.url?scp=85052895576&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052895576&partnerID=8YFLogxK
U2 - 10.1145/3148150.3148151
DO - 10.1145/3148150.3148151
M3 - Conference contribution
AN - SCOPUS:85052895576
SN - 9781450354998
T3 - LocalRec 2017 - Proceedings of the 1st ACM SIGSPATIAL International Workshop on Recommendations for Location-Based Services and Social Networks
BT - LocalRec 2017 - Proceedings of the 1st ACM SIGSPATIAL International Workshop on Recommendations for Location-Based Services and Social Networks
A2 - Bouros, Panagiotis
A2 - Renz, Matthias
A2 - Sacharidis, Dimitris
PB - Association for Computing Machinery, Inc
T2 - 1st ACM SIGSPATIAL International Workshop on Recommendations for Location-Based Services and Social Networks, LocalRec 2017, in conjunction with the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2017
Y2 - 7 November 2017
ER -