Inertial Measurement Units and Partial Least Square Regression to Predict Perceived Exertion During Repetitive Fatiguing Piano Tasks

• Published in: IEEE transactions on human-machine systems Vol. 53; no. 4; pp. 1 - 9
• Main Authors: Goubault, Etienne, Verdugo, Felipe, Bailly, Francois, Begon, Mickael, Maso, Fabien Dal
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Analytical models; Angular velocity; Biomechanics; Engineering Sciences; Fatigue; Inertial platforms; Injury prevention; jerk; Kinematics; Least squares; Mechanics; Pianos; playing-related muscular disorders; Predictive models; Quality assessment; spectral entropy; Task analysis; time-frequency analysis; Variables
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2023.3278874

Aim. Predict the rate of perceived exertion (RPE) of pianists using inertial measurement units (IMUs)-based kinematic descriptors. Method. Fifty expert pianists played Digital (right-hand 16-tone sequence) and Chord (right-hand chord sequence) excerpts in a continuous loop for 12 min or until exhaustion. Partial least square regression was used to predict RPE with IMUs-based kinematic descriptors. The mean error of prediction over 50 iterations with five-folds cross-validation was used to assess the quality of the model. Variable importance in projection was calculated to determine the most relevant features for predicting the RPE and reduce the number of input predictors. Results. Thirty and twenty-six participants showed signs of fatigue before 12 min of the Digital and Chord tasks, respectively, and were included in the analysis. The reduced model of 275 and 227 input variables including four-latent variables explained 86.95 ± 0.46 and 83.91 ± 0.54 of the variance of the RPE on the training set with an absolute error of 0.976 ± 0.033 and 1.189 ± 0.068 on the testing set for both Digital and Chord tasks, respectively. The best features, variables, and sensor positions to predict RPE were different between both Digital and Chord tasks suggesting a task-dependency in the prediction of effort exertion during piano performance. Conclusion. These results highlight the feasibility of continuously monitoring RPE in pianists using kinematic descriptors. These results are promising for developing methods to prevent high levels of fatigue and injuries in musicians.