Abstract
The quality of life of the elderly is becoming more and more important. Prevention and early evaluation of falls in elderly people is a very important issue. Achieving the best comfort can be possible by contactless methods. The paper describes machine learning approach to a fall detection based on body posture classification using 8x8 pixel image acquired by Grid-EYE array temperature sensor. State-of-the-art computer vision tool - deep neural network - is used. The best result is achieved if classification into three or five classes is assumed. Even with such low resolution thermal image sensor, the final sensitivity and specificity of the class “laying” which corresponds to posture of a fallen person reaches 0.85 and 0.93, respectively.