Cadence Detection in Road Cycling Using Saddle Tube Motion and Machine Learning

Abstract

Most commercial cadence-measurement systems in road cycling are strictly limited in their function to the measurement of cadence. Other relevant signals, such as roll angle, inclination or a round kick evaluation, cannot be measured with them. This work proposes an alternative cadence-measurement system with less of the mentioned restrictions, without the need for distinct cadence-measurement apparatus attached to the pedal and shaft of the road bicycle. The proposed design applies an inertial measurement unit (IMU) to the seating pole of the bike. In an experiment, the motion data were gathered. A total of four different road cyclists participated in this study to collect different datasets for neural network training and evaluation. In total, over 10 h of road cycling data were recorded and used to train the neural network. The network’s aim was to detect each revolution of the crank within the data. The evaluation of the data has shown that using pure accelerometer data from all three axes led to the best result in combination with the proposed network architecture. A working proof of concept was achieved with an accuracy of approximately 95% on test data. As the proof of concept can also be seen as a new method for measuring cadence, the method was compared with the ground truth. Comparing the ground truth and the predicted cadence, it can be stated that for the relevant range of 50 rpm and above, the prediction over-predicts the cadence with approximately 0.9 rpm with a standard deviation of 2.05 rpm. The results indicate that the proposed design is fully functioning and can be seen as an alternative method to detect the cadence of a road cyclist.

Torque Measurement and Control for Electric-Assisted Bike Considering Different External Load Conditions

“…References Torque control for assisted E-bike [1,2,24,38,39] Instantaneous pedaling torque waveform [4][5][6][7] Pedaling torque component analysis [8][9][10][11][12][13][14] Factors affecting riding [15][16][17][18][19][20][21][22][23][25][26][27][28][29][30][31][32][33][34][35]] Recharge control for assisted E-bike [36,37] To overcome these limitations on existing E-bike assisted torque control, this paper's motivation is to find the best-suited assisted torque considering external loads. It is shown that the overall cycling torque is affected by external loads, including the cyclist's weight, wind resistance, rolling resistance, and the road slope.…”

“…It has been noted that cycling quality is different with respect to the different physiological factors of cyclists. The sources in [ 15 , 16 , 17 , 18 , 19 , 20 , 21 ] detail that cycling quality can be affected by various factors. These include the cyclist’s sex, purpose, cadence, speed, acceleration, vibration, experience, as well as the weather.…”

“…By contrast, [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ] investigate the cyclist’s pedaling dynamic with no motor-assisted torque assumed. Moreover, [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ] focus on E-bike cycling performance with respect to human behaviors, including heartbeat, gender, and weight. The authors of [ 40 , 41 ] further evaluate recharge control for assisted motor output.…”