Abstract
A relevant aspect in the field of health monitoring is represented by the evaluation of balance stability in the elderly. The Berg Balance Scale (BBS) represents a golden standard test for clinical assessment of balance stability. Recently, the Wii Balance Board has been successfully validated as an effective tool for the analysis of static balance-related features such as the duration or the speed of assessment of patient’s center of pressure. In this paper we propose an innovative unobtrusive approach for automatic evaluation of balance assessment, by analyzing the whole temporal information generated by the balance board. In particular, using Recurrent Neural Networks implemented according to the Reservoir Computing paradigm, we propose to estimate the BBS score of a patient from the temporal data gathered during the execution on the balance board of one simple BBS exercise. The experimental assessment of the proposed approach on real-world data shows promising results.
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Acknowledgments
The work was funded by a grant from DOREMI project (FP7-ICT-2013, GA no. 611650). The authors would like to acknowledge Dr. Sara Lanzisera, Dr. Cristina Laddaga (ASL5, Pisa), Dr. Andrea Bemi (Istituto Superiore di Istruzione C. Piaggia, Viareggio), Dr. Franca Giugni (CNR-IFC), Dr. Filippo Palumbo and Dr. Erina Ferro (CNR-ISTI) for their valuable inputs, support and effort during the preparation and execution of the tests. Finally, the authors would also like to acknowledge all the volunteer participants to the measurement campaign for their support and active participation in these activities.
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Gallicchio, C., Micheli, A., Pedrelli, L., Fortunati, L., Vozzi, F., Parodi, O. (2016). A Reservoir Computing Approach for Balance Assessment. In: Douzal-Chouakria, A., Vilar, J., Marteau, PF. (eds) Advanced Analysis and Learning on Temporal Data. AALTD 2015. Lecture Notes in Computer Science(), vol 9785. Springer, Cham. https://doi.org/10.1007/978-3-319-44412-3_5
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