Evaluation of a Monitoring System for Event Recognition of Older People

International audiencePopulation aging has been motivating academic research and industry to develop technologies for the improvement of older people's quality of life, medical diagnosis, and support on frailty cases. Most of available research prototypes for older people monitoring focus on fall detection or gait analysis and rely on wearable, environmental, or video sensors. We present an evaluation of a research prototype of a video monitoring system for event recognition of older people. The prototype accuracy is evaluated for the recognition of physical tasks (e.g., Up and Go test) and instrumental activities of daily living (e.g., watching TV, writing a check) of participants of a clinical protocol for Alzheimer's disease study (29 participants). The prototype uses as input a 2D RGB camera, and its performance is compared to the use of a RGB-D camera. The experimentation results show the proposed approach has a competitive performance to the use of a RGB-D camera, even outperforming it on event recognition precision. The use of a 2D-camera is advantageous, as the camera field of view can be much larger and cover an entire room where at least a couple of RGB-D cameras would be necessary

A Hierarchical Description-based Video Monitoring System for Elderly

The increase in the number of elderly motivates academic researchers to develop technologies that can ensure self- sufficiency in their lives. In this research, prototype of an inexpensive video monitoring system for the elderly using a single RGB camera proposed. In the process is divided into two, namely vision and event recognition module. For event recognition, we use a hierarchical description-based approach with three attributes, namely posture (e.g., stand, sit and lie), location (e.g., walking zone, relaxing zone and toilet zone) and duration (e.g., short and long). Output this system is description activity recognized in the text. The experiment result shows our system can provide the effectiveness of the context description

BEHAVE - Behavioral analysis of visual events for assisted living scenarios

International audienceThis paper proposes BEHAVE, a person-centered pipeline for probabilistic event recognition. The proposed pipeline firstly detects the set of people in a video frame, then it searches for correspondences between people in the current and previous frames (i.e., people tracking). Finally, event recognition is carried for each person using proba-bilistic logic models (PLMs, ProbLog2 language). PLMs represent interactions among people, home appliances and semantic regions. They also enable one to assess the probability of an event given noisy observations of the real world. BEHAVE was evaluated on the task of online (non-clipped videos) and open-set event recognition (e.g., target events plus none class) on video recordings of seniors carrying out daily tasks. Results have shown that BEHAVE improves event recognition accuracy by handling missed and partially satisfied logic models. Future work will investigate how to extend PLMs to represent temporal relations among events

Development of a smart post-hospitalization facility for older people by using domotics, robotics, and automated tele-monitoring

Recent studies showed that about the 8% of beds are occupied by patients who experience a delayed hospital discharge (DHD). This is attributed to a delay in the arrangement of home-care assistance or in admission to long-term care facilities. Recently a lot of technologies have been developed to improve caring and monitoring of older people. The aim of this study is to design, implement and test a prototype of a technology based post-hospitalization facility for older people at risk of DHD by using domotics, robotics and wearable sensors for tele-monitoring. A sensorised posthospitalization facility has been built inside the hospital. Thirty-five healthy volunteers aged from 20 to 82 years were recruited. Clinical and functional assessment, i.e. motility index (MI), and human-robot interaction satisfaction were measured. A significant correlation was observed between automatic MI and the Gait Speed, the time sit-to-stand, and the Timed Up and Go test. Domotics, robotics and technology-based telemonitoring may represent a new way to assess patient’s autonomy and functional and clinical conditions in an ecological way, reproducing as much as possible a real life at home

Intelligent fall detection system for eldercare

Fall among elders is a main reason to cause accidental death among the population over the age 65 in United States. The fall detection methods have been brought into scene by implemented on different fall monitoring devices. For the advantages in privacy protection and non-invasive, independent of light, I design the fall detection system based on Doppler radar sensor. This dissertation explores different Doppler radar sensor configurations and positioning in both of the lab and real senior home environment, signal processing and machine learning algorithms. Firstly, I design the system based on the data collected with three configurations: two floor radars, one ceiling and one wall radars, one ceiling and one floor radars in lab. The performance of the sensor positioning and features are evaluated with classifiers: support vector machine, nearest neighbor, naïve Bayes, hidden Markov model. In the real senior home, I investigate the system by evaluating the detection variances caused by training dataset due to the variable subjects and environment settings. Moreover, I adjust the automatic fall detection system for the actual retired community apartment. I examine different features: Mel-frequency cepstral coefficients (MFCCs), local binary patterns (LBP) and the combined version of features with RELIEF algorithm. I also improve the detection performance with both pre-screener and features selection. I fuse the radar fall detection system with motion sensors. I develop a standalone fall detection system and generate a result to display on a designed webpage