Project

PAL – Peripheral Assisted Living

PAL will develop an intelligent stratified system to support healthy aging in rural communities. The technology will aid early diagnosis, monitor lifestyle change and provide the ability of patients and the ageing population to live at home.

The objective is to impact a range of healthcare needs across a number of rural areas in NPA regions by employing feature extraction and pattern recognition across a network of low-cost non-medical/environmental networked sensors in a home environment.

The project aims to enhance existing assistive technologies by providing the capability to intelligently monitor and reason over activity and vital signs data and communicate these data in an effective manner to healthcare professionals.

An overarching cognitive framework will be developed to reason in a human-like manner about an individuals health status using a sensorised environment designed to measure environmental conditions and collect personal health status information. The cognitive framework will provide personalised status and communicate with public health care services when necessary, providing recent and historical data that would be otherwise unavailable when the subject lives alone. Such information would enable public health services to prioritise their resources in rural areas, whilst also providing confidence to the elderly population to remain at home.

The PAL cognitive framework will develop intelligent algorithms and utilise advanced sensors within the home for diagnosis and management of health and wellbeing. Sensorised data will provide knowledge with respect to both environmental conditions (room temperature, humidity, lights on etc.,) and activity recognition (person sitting, sleeping etc,) whilst intelligent algorithms will be used to process image data and provide additional contextual knowledge, adding robustness to the activity recognition and monitoring. Newly developed tactile sensing technologies will be used to monitor and record an individuals vital signs to ensure that health status is monitored effectively.

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  • Duration: 1 April 2018 – 30 September 2018
  • Our role: Partner
  • Contact person: Prof. Olli Silvén