In maintaining the health of elderly people, it can be useful to monitor their health status through their daily routines in their own home. This paper reports on the remote monitoring of the daily routine behavior of elderly patients in their domestic houses. We attempted to monitor the daily behavior of two elderly ladies who live in Mizusawa, Japan. A 74-year-old woman, who lived alone, was monitored for about two years, and another 72-year-old woman who lived alone was monitored for about a year. Several sensors were installed, including infrared sensors to detect human movement, magnetic switches to detect the opening and closing of doors, wattmeters embedded in wall sockets to detect the use of household appliances, a flame detector to detect the use of a cooking stove and a CO, sensor to detect the presence of a subject in a room by monitoring the carbon dioxide expired. An industrial networking system was introduced into each house to combine the sensors. The sensor outputs were recorded on a personal computer located in each house. The data were automatically transferred daily to another site via the Internet using CATV. With the sensors, a network and data system, the monitoring was fully automatic and did not require the placement of any sensors on the subjects (e.g. electrodes and cuff sensors) or any operations by subjects. Information on several daily behavior patterns, such as the number of door openings, the length of sleep, absences from the house, use of a cooking stove, and time spent watching television were clearly identified either from a single sensor output, or by combining several sensor outputs. Examination of the data allowed some daily behavior, such as worship (in the form of Japanese Buddhism) and the tending of planters to be evaluated. Such monitoring can contribute to the maintenance of health.

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.

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.

In maintaining the health of elderly people, it can be useful to monitor their health status through their daily routines in their own home. This paper reports on the remote monitoring of the daily routine behavior of elderly patients in their domestic houses. We attempted to monitor the daily behavior of two elderly ladies (a 74-year-old woman and a 72year-old woman) who live alone in Mizusawa, Japan, over a period of one year. Several sensors were installed, including infrared sensors to detect human movement, magnetic switches to detect the opening and closing of doors, wattmeters embedded in wall sockets to detect the use of household appliances, a flame detector to detect the use of a cooking stove, and a CO, sensor to detect the presence of a subject in a room. An industrial networking system was introduced into each house to combine the sensors. The sensor outputs were recorded on a personal computer located in each house. The data were automatically transferred daily to another site via the Internet using CATV. With our system, the monitoring procedure was fully automated and did not require the placement of any sensors on the subjects or require any operations by the subjects. Information on several daily behavior patterns, such as the number of door openings, the length of sleep, absences from the house, use of a cooking stove, and the time spent watching television were clearly identifiable from the obtained data. Such monitoring techniques can contribute to maintaining the health of selected patients.

No abstract