Mobile activity recognition and fall detection system for elderly people using Ameva algorithm
| Autor: | Luis Miguel Soria Morillo, Juan Antonio Álvarez-García, Luis Gonzalez-Abril, Miguel Ángel Álvarez de la Concepción |
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| Rok vydání: | 2017 |
| Předmět: |
Computer Networks and Communications
Computer science Population Cognitive computing 02 engineering and technology Accelerometer 01 natural sciences Activity recognition Human–computer interaction 0202 electrical engineering electronic engineering information engineering Set (psychology) education Selection (genetic algorithm) education.field_of_study business.industry 010401 analytical chemistry Usability 0104 chemical sciences Computer Science Applications Hardware and Architecture Order (business) 020201 artificial intelligence & image processing business Algorithm Software Information Systems |
| Zdroj: | Pervasive and Mobile Computing. 34:3-13 |
| ISSN: | 1574-1192 |
| Popis: | Currently, the lifestyle of elderly people is regularly monitored in order to establish guidelines for rehabilitation processes or ensure the welfare of this segment of the population. In this sense, activity recognition is essential to detect an objective set of behaviors throughout the day. This paper describes an accurate, comfortable and efficient system, which monitors the physical activity carried out by the user. An extension to an awarded activity recognition system that participated in the EvAAL 2012 and EvAAL 2013 competitions is presented. This approach uses data retrieved from accelerometer sensors to generate discrete variables and it is tested in a non-controlled environment. In order to achieve the goal, the core of the algorithm Ameva is used to develop an innovative selection, discretization and classification technique for activity recognition. Moreover, with the purpose of reducing the cost and increasing user acceptance and usability, the entire system uses only a smartphone to recover all the information required. |
| Databáze: | OpenAIRE |
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