| Popis: |
This chapter provides a review of the State of the Art in the Field of advanced solutions for the monitoring of critical events against elderly persons and people with neurological pathologies (e.g., Alzheimer's, Parkinson's). Many systems have been proposed in the literature which use cameras or other forms of sensing. Privacy, reliability and false alarms are the main challenges to be considered for the development of efficient systems to detect and classify the Activities of Daily Living (ADL) and Falls. The design of such systems, especially if wearable, requires a user centered design approach as well as the use of reliable sensors and advanced signal processing techniques, which have to fulfill constraints given by the power and computational budgets. As a case study, a solution based on a multi-sensor data fusion approach is presented. The system is able to recognize critical events like falls or prolonged inactivity and to detect the user posture. In particular, algorithms developed for the Activities of Daily Living classification combine data from an accelerometer and a gyroscope embedded in the user device. Tests performed on the developed prototype confirm the suitability of the device performances, which have been estimated in terms of sensitivity and specificity in performing Falls and ADL classification tasks. Apart from alerts management, the information provided by this system is useful to track the evolution of the user pathology, also during rehabilitation tasks. |
