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B. Pogorelc, Z. Bosnić, M. Gams: Automatic recognition of gait-related health problems in the elderly using machine learning. To appear in Multimedia Tools and applications, 2011.
Abstract:
This paper proposes a semantic ambient media system for the automatic recognition of health problems that manifest themselves in the form of gait. Purpose of the system is to prolong the autonomous living of the elderly at home. In the event that the system was to recognize a health problem, it would automatically notify a physician with an included explanation of the automatic diagnosis.The gait of the elderly user is captured using a motion-capture system, which consists of tags attached to the body and sensors situated in the apartment. The positions of the tags are acquired by the sensors and the resulting time series of position coordinates are analyzed with machine-learning algorithms in order to recognize a specific health problem. We propose novel features for training a machine-learning classifier that classifies the user’s gait into: i) normal, ii) with hemiplegia, iii) with Parkinson’s disease, iv) with pain in the back and v) with pain in the leg.The studies of i) the feasibility of automatic recognition and ii) the impact of tag placement and noise level on the accuracy of the recognition of health problems are presented. The experimental results of the first study (12 tags, no noise) showed that the k-nearest neighbors and neural network algorithms achieved classification accuracies of 100%. The experimental results of the second study showed that using several machine-learning algorithms a classification accuracy of over 99% is achievable using 8 or more tags with 0-15 mm standard deviation of noise. The results show that the proposed approach is very accurate and can be used as a guide for future studies in the increasingly important area of Ambient Assisted Living. Since the system is embedded in the domestic environment of the elderly person, it uses an artificial intelligence approach to interpret the health state and provides a natural explanation of the hypothesis; it is an example of the semantic ambient media for Ambient Assisted Living.
