Marc Mertens

Semi-automated Video-based In-home Fall Risk Assessment

The development of an in-home fall risk assessment tool is under in- vestigation. Several fall risk screening tests such as the Timed-Get-Up-and-Go-test (TGUG) only provide a snapshot taken at a given time and place, where automated in-home fall risk assessment tools can assess the fall risk of a person on a contin- uous basis. During this study we monitored four older people in their own home for a period of three months and automatically assessed fall risk parameters. We selected a subset of fixed walking sequences from the resulting real-life video for analysis of the time needed to perform these sequences. The results show a sig- nificant diurnal and health-related variance in the time needed to cross the same distance. These results also suggest that trends in the transfer time can be detected with the presented system.

Three Ways to Improve the Performance of Real-Life Camera-Based Fall Detection Systems

More than thirty percent of persons over 65 years fall at least once a year and are often not able to get up again. Camera-based fall detection systems can help by triggering an alarm when falls occur. Previously we showed that real-life data poses significant challenges, resulting in high false alarm rates. Here, we show three ways to tackle this. First, using a particle filter combined with a person detector increases the robustness of our foreground segmentation, reducing the number of false alarms by 50%. Second, selecting only nonoccluded falls for training further decreases the false alarm rate on average from 31.4 to 26 falls per day. But, most importantly, this improvement is also shown by the doubling of the AUC of the precision-recall curve compared to using all falls. Third, personalizing the detector by adding several days containing only normal activities, no fall incidents, of the monitored person to the training data further increases the robustness of our fall detection system. In one case, this reduced the number of false alarms by a factor of 7 while in another one the sensitivity increased by 17% for an increase of the false alarms of 11%.

Automated in-home gait transfer time analysis using video cameras

Previous studies have shown that gait speed is an important measure of functional ability in the elderly. Continuous monitoring of the gait speed of older adults in their home environment may therefore allow the detection of changes in gait speed which could be predictive of health changes of the monitored person. In this study, a system consisting of multiple wall-mounted cameras that can automatically measure the time an older adult needs to cross a predefined transfer zone in the home environment is presented. The purpose of this study is the preliminary validation of the algorithm of the camera system which consists of several preprocessing steps and the automatic measurement of the transfer times. This validation is done through data collection in the homes of four older adults for periods varying from eight to twelve weeks. Trends in the measured transfer times are visualised and subsequently compared with the results of clinical assessments obtained during the acquisition period such as Timed-Get-Up-and-Go tests. The results indicate that it is possible to identify long-term trends in transfer times which can be indicative of adverse health-related events.