P C Dixon1, K H Schütte2, B Vanwanseele2, J V Jacobs3, J T Dennerlein4, J M Schiffman, P-A Fournier5, B Hu6. 1. Carré Technologies, Inc., Montreal, Canada. Electronic address: philippe.dixon@gmail.com. 2. Human Movement Biomechanics Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium. 3. Rehabilitation and Movement Science, University of Vermont, USA. 4. Bouvé College of Health Sciences, Northeastern University, Boston, USA; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, USA. 5. Carré Technologies, Inc., Montreal, Canada. 6. Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA.
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.
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.
Authors: Georgia A Tuckwell; James A Keal; Charlotte C Gupta; Sally A Ferguson; Jarrad D Kowlessar; Grace E Vincent Journal: Sensors (Basel) Date: 2022-09-01 Impact factor: 3.847
Authors: Christopher Fricke; Jalal Alizadeh; Nahrin Zakhary; Timo B Woost; Martin Bogdan; Joseph Classen Journal: Front Neurol Date: 2021-05-21 Impact factor: 4.003