Browsing by Subject "activity recognition"

The growing aging of the world's population is leading to the need to take assistance measures and prepare health care systems for the elderly. The innovative vINCI system provides technologies and uses smart devices that can non-invasively monitor the activity of elderly, to intervene in case of alerts, to prevent possible health problems, such as falling, in the same time to keep their life independent and to improve their quality of life. Monitoring physical activity of the elderly with the help of smart cameras is important in identifying one of the most important lifestyle risk factors for many chronic conditions in the older age. In this paper there are presented the microservice-based vINCI architecture and how an Orbbec Persee camera is used to monitor the physical activity as well as to recognize the elderly. The advantages of the monitoring physical activity application consist in detecting a low level of activity or detecting health problems allowing intervention and correction of an unhealthy lifestyle.

Because the number of elderly people is predicted to increase quickly in the upcoming years, “aging in place” (which refers to living at home regardless of age and other factors) is becoming an important topic in the area of ambient assisted living. Therefore, in this paper, we propose a human physical activity recognition system based on data collected from smartphone sensors. The proposed approach implies developing a classifier using three sensors available on a smartphone: accelerometer, gyroscope, and gravity sensor. We have chosen to implement our solution on mobile phones because they are ubiquitous and do not require the subjects to carry additional sensors that might impede their activities. For our proposal, we target walking, running, sitting, standing, ascending, and descending stairs. We evaluate the solution against two datasets (an internal one collected by us and an external one) with great effect. Results show good accuracy for recognizing all six activities, with especially good results obtained for walking, running, sitting, and standing. The system is fully implemented on a mobile device as an Android application.