Activity Recognition in Adaptive Assistive Systems Using Artificial Neural Networks
Our research was oriented to develop technologies for independent daily life assistance of elderly or sick persons and to improve the quality of human life. We designed a complex assistive system that can learn and adapt due to the uses of artificial neural networks (ANN). This paper presents the system developed for human activity and health parameters monitoring (temperature, heart rate, acceleration) and focuses on studies and results obtained on arm posture recognition, body posture recognition and usual activities recognition like: lying on various sides, sitting, standing, walking, running, descending or climbing stairs etc. For pattern recognition from the possible biologically inspired algorithms we opted for the ANNs. One direction of research was the design and test of several Matlab ANN models in order to find the best performing architecture. Another research direction was related to the necessary preprocessing of raw data aiming to have a better recognition rate. We find that standard deviation could be used with very good results as a supplementary input data for neurons. We optimized the number of sensors and their placement in order to obtain the best trade-off between recognition rate and the complexity of the recognition system.
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