Abstract
Rapid aging of the population of the developed countries could exceed the society’s capacity for taking care for them. In order to help solving this problem, we propose a system for automatic discovery of health problems from motion-capture data of gait of elderly. The gait of the user is captured with the motion capture system, which consists of tags attached to the body and sensors situated in the apartment. Position of the tags is acquired by the sensors and the resulting time series of position coordinates are analyzed with machine learning algorithms in order to identify the 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. Results show that naive Bayes needs more tags and less noise to reach classification accuracy of 98 % than support vector machines for 99 %.
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Acknowledgments
This work is partially financed by the European Union, the European Social Fund. The authors thank Martin Tomšič, Bojan Nemec and Leon Žlajpah for their help with data acquisition, Anton Gradišek for his medical expertise and Zoran Bosnić and Mitja Luštrek for helpful discussions.
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Pogorelc, B., Gams, M. (2011). Health Problems Discovery from Motion-Capture Data of Elderly. In: Bramer, M., Petridis, M., Hopgood, A. (eds) Research and Development in Intelligent Systems XXVII. SGAI 2010. Springer, London. https://doi.org/10.1007/978-0-85729-130-1_28
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DOI: https://doi.org/10.1007/978-0-85729-130-1_28
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