Abstract
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. DOI: http://dx.doi.org/10.5755/j01.eee.22.1.14112
Highlights
The world’s population is aging and this trend increases the costs of social care and hospitalization
Related to the artificial neural network simulations we have developed our feed forward artificial neural networks (ANN) simulator [8]; 4
We modelled in Matlab several recognition systems for arm posture, body postures and for usual activities, like: lying on various sides, sitting, standing, walking, running, descending or climbing stairs, etc
Summary
The world’s population is aging and this trend increases the costs of social care and hospitalization. Our research is part of this trend, to develop technologies for independent daily life assistance of elderly or sick persons and to improve the quality of human life using Internet of things (IoT) techniques [7]. This is complex assistive system that can learn and adapt due to the uses of neural networks. The acquired data is used to train a neural network that allows recognition of the activity or the health status of the patient and trigger alert signals in case of unusual state detection. We implemented and tested a real time recognition system using Raspberry Pi mini-computer [12]
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