TY - GEN
T1 - Activity Recognition of Local Muscular Endurance (LME) Exercises Using an Inertial Sensor
AU - Prabhu, Ghanashyama
AU - Ahmadi, Amin
AU - O’Connor, Noel E.
AU - Moran, Kieran
N1 - Funding Information:
Acknowledgement. The work was funded by ACQUIS BI, an industrial partner of INSIGHT Centre for Data Analytics, DCU and Science Foundation Ireland under Grant Number SFI/12/RC/2289.
Publisher Copyright:
© 2018, Springer International Publishing AG.
PY - 2018
Y1 - 2018
N2 - In this paper, we propose an algorithmic approach for a motion analysis framework to automatically recognize local muscular endurance (LME) exercises and to count their repetitions using a wrist-worn inertial sensor. LME exercises are prescribed for cardiovascular disease rehabilitation. As a technical solution, we propose activity recognition based on machine learning. We developed an algorithm to automatically segment the captured data from all participants. Relevant time and frequency domain features were extracted using a sliding window technique. Principal component analysis (PCA) was applied for dimensionality reduction of the extracted features. We trained 15 binary classifiers using support vector machine (SVM) to recognize individual LME exercises, achieving overall accuracy of more than 98%. We applied grid search technique to obtain the optimal SVM hyperplane parameters. The learning curves (mean ± stdev) for each model is investigated to verify that the models were not over-fitted and performed well on any new test data. Also, we devised a method to count the repetitions of the upper body exercises.
AB - In this paper, we propose an algorithmic approach for a motion analysis framework to automatically recognize local muscular endurance (LME) exercises and to count their repetitions using a wrist-worn inertial sensor. LME exercises are prescribed for cardiovascular disease rehabilitation. As a technical solution, we propose activity recognition based on machine learning. We developed an algorithm to automatically segment the captured data from all participants. Relevant time and frequency domain features were extracted using a sliding window technique. Principal component analysis (PCA) was applied for dimensionality reduction of the extracted features. We trained 15 binary classifiers using support vector machine (SVM) to recognize individual LME exercises, achieving overall accuracy of more than 98%. We applied grid search technique to obtain the optimal SVM hyperplane parameters. The learning curves (mean ± stdev) for each model is investigated to verify that the models were not over-fitted and performed well on any new test data. Also, we devised a method to count the repetitions of the upper body exercises.
UR - https://www.scopus.com/pages/publications/85029595481
UR - https://www.scopus.com/inward/citedby.url?scp=85029595481&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67846-7_4
DO - 10.1007/978-3-319-67846-7_4
M3 - Conference contribution
AN - SCOPUS:85029595481
SN - 9783319678450
T3 - Advances in Intelligent Systems and Computing
SP - 35
EP - 47
BT - Proceedings of the 11th International Symposium on Computer Science in Sport, IACSS 2017
A2 - Saupe, Dietmar
A2 - Lames, Martin
A2 - Wiemeyer, Josef
PB - Springer Verlag
T2 - 11th International Symposium on Computer Science in Sport, IACSS 2017
Y2 - 6 September 2017 through 9 September 2017
ER -
