description
- This project falls within the EPSRC Artificial Intelligence Technologies In recent years, various changes in modern societies have resulted in a sig-nificant number of people spending a considerable amount of their day in their home environments. More and more people turn to self-employment, while businesses seem to be exploring recent studies related to increasing productivity, by allowing their employees to have flexible working times, of-ten working from home. At the same time, the advances in medicine and the increase in life expectancy have resulted in the phenomenon of the ageing population; even though nowadays older retired adults normally have many more years to live, they are often faced with age-related diseases, such as arthritis, Parkinson's, dementia or geriatric depression, that might keep them at home as they become more severe. In this DPhil project, the task of monitoring human behaviour in their home environment employing widely available, low-cost and light-weight sensors is tackled. In particular, we will explore the following research directions: 1. An algorithm for room identification, based only on BLE beacons and IMU data from smartwatches. Even though RSSI methods based on the use of smartwatches have been popular these last few years, the use of smartwatches as the main tool for tracking is not met frequently in existing literature; it is also particularly challenging, as smartwatch recordings are very noisy and also related to tasks that might be per-formed alongside movement intended to travel from one place to an-other. 2. An algorithm to perform PDR from smartwatches; the VICON system will be used to provide ground truth positioning, and the noisy acceleration data will have to be analysed carefully to identify steps. Smartwatches have been used in PDR methods before, but only as a sensor fusion method, with smartphones or smartglasses being the primary sensing device. 3. Motion pattern analysis at home using location and gait information. Though motion patterns have long been studied, it is either specific movements that are usually tackled, or the sensors used are either com-plicated networks, or intrusive (e.g., cameras), both of which are not appropriate for the privacy-preserving home environment. 4. Should data from dementia patients become available, application of the aforementioned The project includes collaboration with expert psychiatrists on dementia.