Discrimination of cycling patterns using accelerometric data and deep learning techniques
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ORIGINAL ARTICLE
Discrimination of cycling patterns using accelerometric data and deep learning techniques Alesˇ Procha´zka1,2
•
Hana Charva´tova´3 • Oldrˇich Vysˇata4 • Delaram Jarchi5 • Saeid Sanei6
Received: 25 August 2020 / Accepted: 2 November 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract The monitoring of physical activities and recognition of motion disorders belong to important diagnostical tools in neurology and rehabilitation. The goal of the present paper is in the contribution to this topic by (1) analysis of accelerometric signals recorded by wearable sensors located at specific body positions and by (2) implementation of deep learning methods to classify signal features. This paper uses the general methodology to analysis of accelerometric signals acquired during cycling at different routes followed by the global positioning system. The experimental dataset includes 850 observations that were recorded by a mobile device in the spine area (L3 verterbra) for cycling routes with the different slope. The proposed methodology includes the use of deep learning convolutional neural networks with five layers applied to signal values transformed into the frequency domain without specification of any signal features. The accuracy of discrimination between different motion patterns for the uphill and downhill cycling and recognition of 4 classes associated with different route slopes was 96.6% with the loss criterion of 0.275 for sigmoidal activation functions. These results were compared with those evaluated for selected sets of features estimated for each observation and classified by the support vector machine, Bayesian methods, and the two-layer neural network. The best cross-validation error of 0.361 was achieved for the two-layer neural network model with the sigmoidal and softmax transfer functions. Our methodology suggests that deep learning neural networks are efficient in the assessment of motion activities for automated data processing and have a wide range of applications, including rehabilitation, early diagnosis of neurological problems, and possible use in engineering as well. Keywords Multimodal signal analysis Computational intelligence Machine learning Deep neural networks Accelerometers Classification Motion monitoring
1 Introduction Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00521-020-05504-3) contains supplementary material, which is available to authorised users. & Alesˇ Procha´zka [email protected] Hana Charva´tova´ [email protected] Oldrˇich Vysˇata [email protected] Delaram Jarchi [email protected] Saeid Sanei [email protected]
Computer-assisted monitoring of motion activities [31, 33, 34, 36, 57] allows us to improve the quality of life in many different areas, including health care and personal fitness. The need for early detection of different pathological situations motivates the study of specific methods to examine m
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