Meeting challenges of activity recognition for ageing population in real life settings
| Autor: | Marina Kotsani, Roberto Orselli, Marina Polycarpou, Athanase Benetos, Konstantinos Deltouzos, Evangelia I. Zacharaki, Aimilia Papagiannaki, Vasileios Megalooikonomou, John Ellul, Elena Aristodemou, Anne Freminet, Sibora Cela |
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| Rok vydání: | 2018 |
| Předmět: |
Scheme (programming language)
Population ageing Point (typography) business.industry Computer science Heuristic Feature extraction 02 engineering and technology Data science Activity recognition 03 medical and health sciences 0302 clinical medicine Premise 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business computer 030217 neurology & neurosurgery Wearable technology computer.programming_language |
| Zdroj: | HealthCom |
| DOI: | 10.1109/healthcom.2018.8531105 |
| Popis: | As the global community becomes more interested in improving the quality of life of older people and preventing undesired events related to their health status, the development of sophisticated devices and analysis algorithms for monitoring everyday activities is necessary more than ever. Wearable devices lie among the most popular solutions from a hardware point of view, while machine learning techniques have shown to be very powerful in behavioral monitoring. Nevertheless, creating robust models from data collected unobtrusively in home environments can be challenging, especially for the vulnerable ageing population. Under that premise, we propose an activity recognition scheme for older people along with heuristic computational solutions to address the challenges due to inconsistent measurements in non-standardized environments. |
| Databáze: | OpenAIRE |
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