Wearable Sensor Selection, Motion Representation and their Effect on Exercise Classification

Author(s):  
Nicholas Hosein ◽  
Soheil Ghiasi
2021 ◽  
Author(s):  
Jens Kirchner ◽  
Samira Faghih-Naini ◽  
Pinar Bisgin ◽  
Georg Fischer

Classification of physical activity based on the k-NN algorithm is assessed with different combinations of sensors (from accelerometer, gyroscope, barometer) with respect to classification accuracy, power consumption and computation time. For that purpose, a wearable sensor platform is proposed and a study with 20 subjects is conducted. The combination of accelerometer and barometer is found to provide the best trade-off for the three criteria: It provides an F1 score of 94.96 ± 1.73 %, while computation time and power consumption are reduced by 45 % and 88 %, respectively, compared to the full sensor set.


2021 ◽  
Author(s):  
Jens Kirchner ◽  
Samira Faghih-Naini ◽  
Pinar Bisgin ◽  
Georg Fischer

Classification of physical activity based on the k-NN algorithm is assessed with different combinations of sensors (from accelerometer, gyroscope, barometer) with respect to classification accuracy, power consumption and computation time. For that purpose, a wearable sensor platform is proposed and a study with 20 subjects is conducted. The combination of accelerometer and barometer is found to provide the best trade-off for the three criteria: It provides an F1 score of 94.96 ± 1.73 %, while computation time and power consumption are reduced by 45 % and 88 %, respectively, compared to the full sensor set.


Author(s):  
Robin Pla ◽  
Thibaut Ledanois ◽  
Escobar David Simbana ◽  
Anaël Aubry ◽  
Benjamin Tranchard ◽  
...  

The main aim of this study was to evaluate the validity and the reliability of a swimming sensor to assess swimming performance and spatial-temporal variables. Six international male open-water swimmers completed a protocol which consisted of two training sets: a 6×100m individual medley and a continuous 800 m set in freestyle. Swimmers were equipped with a wearable sensor, the TritonWear to collect automatically spatial-temporal variables: speed, lap time, stroke count (SC), stroke length (SL), stroke rate (SR), and stroke index (SI). Video recordings were added as a “gold-standard” and used to assess the validity and the reliability of the TritonWear sensor. The results show that the sensor provides accurate results in comparison with video recording measurements. A very high accuracy was observed for lap time with a mean absolute percentage error (MAPE) under 5% for each stroke (2.2, 3.2, 3.4, 4.1% for butterfly, backstroke, breaststroke and freestyle respectively) but high error ranges indicate a dependence on swimming technique. Stroke count accuracy was higher for symmetric strokes than for alternate strokes (MAPE: 0, 2.4, 7.1 & 4.9% for butterfly, breaststroke, backstroke & freestyle respectively). The other variables (SL, SR & SI) derived from the SC and the lap time also show good accuracy in all strokes. The wearable sensor provides an accurate real time feedback of spatial-temporal variables in six international open-water swimmers during classical training sets (at low to moderate intensities), which could be a useful tool for coaches, allowing them to monitor training load with no effort.


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