Key result
Positioning sensors on the spine for the classification of uphill and downhill cycling yielded an accuracy of 96.5%, which could be increased to 98.3% with additional features.
Why the study?
Motion analysis is important for monitoring physical activities and recognizing neurological disorders, but optimal methods and sensor positions using mobile accelerometers and heart rate during cycling require evaluation.
Mobile phone accelerometers and heart rate data can accurately classify cycling conditions, with optimal sensor placement on the spine.
Supports spine sensor feasibility for cycling terrain classification; leaves open real-world validation before adoption.
Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands ⟨ 3 , 8 ⟩ and ⟨ 8 , 15 ⟩ Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification.
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Charvátová et al. (2020) studied Uphill and downhill cycling. Sensor positioning on the spine was evaluated on Classification accuracy of uphill and downhill cycling. Positioning sensors on the spine for the classification of uphill and downhill cycling yielded an accuracy of 96.5%, which could be increased to 98.3% with additional features.
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