A portable heat flux sensor.

Nowadays monitoring physiological signals in real situations is essential to get the best diagnosis on patients. In this study we focus on the heat flux generated by the human body. We are developing a portable heat flux sensor using specific thermal materials.

Evaluation under real-life conditions of a stand-alone fall detector for the elderly subjects.

BACKGROUND AND OBJECTIVES: Elderly patients unable to get up after a fall or to activate an alarm mechanism are particularly at risk of complications and need to be monitored with extreme care. The different risk factors have fostered the development of stand-alone devices facilitating early detection of falls. We aimed at assessing performance of the Vigi'Fall(®) system, a cutting edge fall detector associating a "passive release" mechanism attached to the patient and including external sensors; in the event of a fall, the system automatically triggers an alarm, and it also incorporates embedded confirmation software. We have put it to the test under real-life conditions so as to evaluate not only its efficacy, but also and more particularly its acceptability and tolerability in elderly subjects. METHOD: The study ran from March 2007 through December 2008 in a geriatric ward with 10 subjects over 75 years of age, all of whom presented with a risk of falling. RESULTS: For eight patients wearing an accelerometric sensor, eight "falling" events and 30 "alarm release" events were recorded. Sensitivity and specificity of the device came to 62.5 and 99.5% respectively. For the two patients wearing the complete device, no events were detected. Not a single adverse occurrence was noted. Local tolerance was excellent in all but one of the subjects. CONCLUSION: Our results clearly show that the device may be worn by patients without discomfort over prolonged periods of time, and also demonstrate that the verification component will help to increase sensitivity in real-life conditions to a level comparable to the level attained in our laboratory studies.

Identification of inactivity behavior in smart home.

To help elderly people live independently at home, the TIMC-IMAG laboratory developed Health Smart Homes called 'HIS'. These smart Homes are composed of several sensors to monitor the activities of daily living of the patients. Volunteers have accepted to be monitored during 2 years in their own flats. During one year, we carried out our survey on one elderly patient. Thanks to this experimentation, we will access to relevant information like physiological, environmental and activity. This paper focuses on daily living activity. We will introduce an original data splitting method based on the relationship between the frame of time and the location in the flat. Moreover we will present two different methods to determine a threshold of critical inactivity and eventually we will discuss their possible utilities.