Abstract
We present an approach to estimate a person’s risk of falling by analysing gait recordings from a 6 m walk on a sensor floor. The risk of falls correlates with certain parameters of the human gait. Although these parameters are not measured directly with this sensor, their information is reflected in the data. The sensor floor works with a capacitance measurement principle and is sensitive to contact, such that persons standing, walking or lying on the floor are recognisable. In a preprocessing step, the person’s position is determined from the sensor data and the spread of contact points calculated. This spread implicitly contains the step sizes for the step phase in which two contact points are present. For each experiment, the distribution of occurring spreads is binned and taken as an input feature vector to a feed-forward perceptron. The neural network was trained by backpropagation with gait recordings from persons in low risk of falling and persons in high risk of falling. In the dataset, subjects were labelled as being in high risk of falling based on the prevalence of diseases, falls that already happened, and expert opinions. Though in this setup the data was recorded within a controlled environment, the results are transferable to larger installations and long-term observation periods.
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Acknowledgments
This work is supported by the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF) within the project Cognitive VillAge: Adaptiv-lernende, technische Alltagsbegleiter im Alter (CogAge, FKZ 16SV7311).
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Hoffmann, R., Lauterbach, C., Techmer, A., Conradt, J., Steinhage, A. (2016). Recognising Gait Patterns of People in Risk of Falling with a Multi-layer Perceptron. In: Piętka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technologies in Medicine. ITiB 2016. Advances in Intelligent Systems and Computing, vol 472. Springer, Cham. https://doi.org/10.1007/978-3-319-39904-1_8
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DOI: https://doi.org/10.1007/978-3-319-39904-1_8
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