Rhythm of daily living and detection of atypical days for elderly people living alone as determined with a monitoring system.

We have developed a system for monitoring the health of elderly people living at home. Infrared and other sensor outputs are collected using a monitoring program installed on a personal computer (PC) in the home at a sampling rate of 1 Hz. Once each day, the data are transferred to a server through the Internet using a cable television (TV) connection. An elderly subject was monitored for a 12-day baseline period and completed a daily questionnaire about her activities. This enabled us to identify the rhythm of daily living (sleeping, 23:00-04:59; getting up/breakfast, 05:00-08:59; indoor activities/going out, 09:00-16:59; and dinner/going to bed, 17:00-22:59) and the average outputs from the sensors in the rooms. The subject was then monitored for a further six months. By identifying sensor output counts outside the limits of mean +/- 3SD, we were able to detect atypical days. During the six-month monitoring period, 29 atypical days were detected. We suggest that the monitoring system may be effective in tele-rehabilitation.

Monitoring daily living activities of elderly people in a nursing home using an infrared motion-detection system.

We examined whether we could identify activity patterns of elderly people in a nursing home from sensor outputs of an infrared monitoring system. The subjects consisted of three elderly people. A single passive infrared sensor installed on the ceiling of each subject's usual dwelling room provided digital output whenever the subject moved. The subjects' actual daily activities were established from questionnaires with which patients documented their living patterns for each of 7 days. Activities were classed as sleeping, getting up/breakfast, indoor activities/going out, and dinner/going to bed. The mean +/- 2 standard deviations (SDs) of the sensor outputs on each day for each period of indoor activity was used to distinguish between normal and aberrant activities. Days on which sensor outputs exceeded the means +/- 2 SDs were regarded as atypical and were identified for each subject over a 28-day period. We were unable to determine the physical condition of the subjects on these atypical days. We were able to identify the pattern of daily indoor living activities and the duration of each class of activity using sensor outputs and a questionnaire. Days were assumed to be atypical when sensor outputs deviated from the normal pattern.

Analysis of activities of daily living in elderly people living alone: single-subject feasibility study.

We have developed an automatic remote system for use in monitoring the health of independent elderly people living in conventional homes. In this study, a monitoring system was installed and evaluated in the house of one subject to test the feasibility of determining activities from sensor outputs. Over a period of 6 months, it was found that total counts from all sensor outputs matched the concurrently acquired data on physical activity obtained from a pedometer. Performance of each basic activity of daily living could be confirmed from the sensor output records.