Algorithm to automatically detect abnormally long periods of inactivity in a home

An algorithm has been developed to automatically construct individual models of normal activity within a home using motion sensor data. Alerts can be generated when a period of inactivity exceeds a normal length for a particular residence. Alerting frequency has been optimized on a total of 650 days of real data from four homes of seniors who live independently. Results suggest that an inexpensive system that does not require the occupant to push any buttons or wear any devices could nonetheless alert within hours if a senior is unusually inactive. Further, such algorithms may facilitate widespread deployment of smart home technology to persons with different behavior patterns and home layouts by using automatic learning in place of potentially tedious manual configuration.

[1]  Karen Zita Haigh,et al.  Learning Models of Human Behaviour with Sequential Patterns , 2002 .

[2]  Kathleen E Krichbaum,et al.  Automation as caregiver , 2001 .

[3]  G. Williams,et al.  A smart fall and activity monitor for telecare applications , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[4]  O. Wilder‐Smith,et al.  How dangerous are falls in old people at home? , 1981, British medical journal.

[5]  Elizabeth D. Mynatt,et al.  Increasing the opportunities for aging in place , 2000, CUU '00.

[6]  M. Tinetti,et al.  Predictors and prognosis of inability to get up after falls among elderly persons. , 1993, JAMA.

[7]  Trent Apted,et al.  Successful Aging , 2004, IEEE Pervasive Comput..