1. Machine learning algorithms can classify outdoor terrain types during running using accelerometry data
- Author
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Jack T. Dennerlein, Benedicte Vanwanseele, Boyi Hu, Jesse V. Jacobs, Jeffrey M. Schiffman, Schütte Kh, Philippe C. Dixon, and Fournier Pa
- Subjects
Adult ,Male ,Computer science ,Biophysics ,Wearable computer ,Terrain ,Accelerometer ,Machine learning ,computer.software_genre ,Convolutional neural network ,Running ,Machine Learning ,03 medical and health sciences ,Young Adult ,0302 clinical medicine ,Inertial measurement unit ,Accelerometry ,Feature (machine learning) ,Humans ,Orthopedics and Sports Medicine ,Exercise ,business.industry ,Deep learning ,Rehabilitation ,030229 sport sciences ,Female ,Gradient boosting ,Artificial intelligence ,Neural Networks, Computer ,business ,Algorithm ,computer ,030217 neurology & neurosurgery ,Algorithms - Abstract
Background Running is a popular physical activity that benefits health; however, running surface characteristics may influence loading impact and injury risk. Machine learning algorithms could automatically identify running surface from wearable motion sensors to quantify running exposures, and perhaps loading and injury risk for a runner. Research question (1) How accurately can machine learning algorithms identify surface type from three-dimensional accelerometer sensors? (2) Does the sensor count (single or two-sensor setup) affect model accuracy? Methods Twenty-nine healthy adults (23.3 ± 3.6 years, 1.8 ± 0.1 m, and 63.6 ± 8.5 kg) participated in this study. Participants ran on three different surfaces (concrete, synthetic, woodchip) while fit with two three-dimensional accelerometers (lower-back and right tibia). Summary features (n = 208) were extracted from the accelerometer signals. Feature-based Gradient Boosting (GB) and signal-based deep learning Convolutional Neural Network (CNN) models were developed. Models were trained on 90% of the data and tested on the remaining 10%. The process was repeated five times, with data randomly shuffled between train-test splits, to quantify model performance variability. Results All models and configurations achieved greater than 90% average accuracy. The highest performing models were the two-sensor GB and tibia-sensor CNN (average accuracy of 97.0 ± 0.7 and 96.1 ± 2.6%, respectively). Significance Machine learning algorithms trained on running data from a single- or dual-sensor accelerometer setup can accurately distinguish between surfaces types. Automatic identification of surfaces encountered during running activities could help runners and coaches better monitor training load, improve performance, and reduce injury rates.
- Published
- 2019