Among elderly populations over the world, a high percentage of individuals are affected by physical or mental diseases, greatly influencing their quality of life. As it is a known fact that they wish to remain in their own home for as long as possible, solutions must be designed to detect these diseases automatically, limiting the reliance on human resources. To this end, our team developed a sensors platform based on infrared proximity sensors to accurately recognize basic bathroom activities such as going to the toilet and showering. This work is based on the body of scientific literature which establish evidences that activities relative to corporal hygiene are strongly correlated to health status and can be important signs of the development of eventual disorders. The system is built to be simple, affordable and highly reliable. Our experiments have shown that it can yield an F-Score of 96.94~\%. Also, the durations collected by our kit are approximately 6 seconds apart from the real ones; those results confirm the reliability of our kit.

Many people in the world are affected by muscle wasting, especially the population hits by myotonic dystrophy type 1 (DM1). Those people are usually given a program of multiple physical exercises to do. While DM1 and many other people have difficulties attending commercial centers to realize their program, a solution is to develop such a program completable at home. To this end, we developed a portable system that patients could bring home. This prototype is an improved version of the previous one using Wi-Fi, as this new prototype runs on BLE technology. This new prototype conceptualized induces great results.

Myotonic dystrophy type 1 (DM1) is a rare disease where the highest prevalence is found in the small geographical region of Saguenay-Lac-St-Jean in Quebec, Canada. This disease impacts the quality of life and the ability of the affected people to pursue their normal day to day activities. To attenuate the impact of DM1, one could suggest that physiotherapists or other health professionals prescribe adapted physical exercise programs to carry out at home. It is, however, unpractical for the professionals to monitor every patient during their program and prohibitive in terms of cost. One alternative solution is to rely on the use of ambient technologies. To do so, in this research, our team developed an assistive system able to recognize simple mobility related activities (MA) and monitor each exercise performed during training sessions. The system can help the person by providing guidance and motivation throughout the training. The system was tested on 10 persons affected by the disease in their home for 10 weeks. The results obtained are encouraging and we discuss them in comparison with our previous work conducted in lab settings. Finally, to help further advances the research, the datasets are openly available online to the community.