Smart Boxing Glove “RD <i>α</i>”: IMU Combined with Force Sensor for Highly Accurate Technique and Target Recognition Using Machine Learning
Emerging smart devices have gathered increasing popularity within the sports community, presenting a promising avenue for enhancing athletic performance. Among these, the Rise Dynamics Alpha (RD <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline&...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/16/9073 |
| _version_ | 1827730550002024448 |
|---|---|
| author | Dea Cizmic Dominik Hoelbling René Baranyi Roland Breiteneder Thomas Grechenig |
| author_facet | Dea Cizmic Dominik Hoelbling René Baranyi Roland Breiteneder Thomas Grechenig |
| author_sort | Dea Cizmic |
| collection | DOAJ |
| description | Emerging smart devices have gathered increasing popularity within the sports community, presenting a promising avenue for enhancing athletic performance. Among these, the Rise Dynamics Alpha (RD <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>) smart gloves exemplify a system designed to quantify boxing techniques. The objective of this study is to expand upon the existing RD <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> system by integrating machine-learning models for striking technique and target object classification, subsequently validating the outcomes through empirical analysis. For the implementation, a data-acquisition experiment is conducted based on which the most common supervised ML models are trained: decision tree, random forest, support vector machine, k-nearest neighbor, naive Bayes, perceptron, multi-layer perceptron, and logistic regression. Using model optimization and significance testing, the best-performing classifier, i.e., support vector classifier (SVC), is selected. For an independent evaluation, a final experiment is conducted with participants unknown to the developed models. The accuracy results of the data-acquisition group are 93.03% (striking technique) and 98.26% (target object) and for the independent evaluation group 89.55% (striking technique) and 75.97% (target object). Therefore, it is concluded that the system based on SVC is suitable for target object and technique classification. |
| first_indexed | 2024-03-11T00:09:29Z |
| format | Article |
| id | doaj.art-4d96f7b8cf344dafabe3b8ef49dd866f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T00:09:29Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4d96f7b8cf344dafabe3b8ef49dd866f2023-11-19T00:03:48ZengMDPI AGApplied Sciences2076-34172023-08-011316907310.3390/app13169073Smart Boxing Glove “RD <i>α</i>”: IMU Combined with Force Sensor for Highly Accurate Technique and Target Recognition Using Machine LearningDea Cizmic0Dominik Hoelbling1René Baranyi2Roland Breiteneder3Thomas Grechenig4Research Group for Industrial Software (INSO), Vienna University of Technology, 1040 Vienna, AustriaResearch Group for Industrial Software (INSO), Vienna University of Technology, 1040 Vienna, AustriaResearch Group for Industrial Software (INSO), Vienna University of Technology, 1040 Vienna, AustriaResearch Group for Industrial Software (INSO), Vienna University of Technology, 1040 Vienna, AustriaResearch Group for Industrial Software (INSO), Vienna University of Technology, 1040 Vienna, AustriaEmerging smart devices have gathered increasing popularity within the sports community, presenting a promising avenue for enhancing athletic performance. Among these, the Rise Dynamics Alpha (RD <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>) smart gloves exemplify a system designed to quantify boxing techniques. The objective of this study is to expand upon the existing RD <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> system by integrating machine-learning models for striking technique and target object classification, subsequently validating the outcomes through empirical analysis. For the implementation, a data-acquisition experiment is conducted based on which the most common supervised ML models are trained: decision tree, random forest, support vector machine, k-nearest neighbor, naive Bayes, perceptron, multi-layer perceptron, and logistic regression. Using model optimization and significance testing, the best-performing classifier, i.e., support vector classifier (SVC), is selected. For an independent evaluation, a final experiment is conducted with participants unknown to the developed models. The accuracy results of the data-acquisition group are 93.03% (striking technique) and 98.26% (target object) and for the independent evaluation group 89.55% (striking technique) and 75.97% (target object). Therefore, it is concluded that the system based on SVC is suitable for target object and technique classification.https://www.mdpi.com/2076-3417/13/16/9073machine learningtime-series classificationsmart gearmartial artscombat sportsboxing |
| spellingShingle | Dea Cizmic Dominik Hoelbling René Baranyi Roland Breiteneder Thomas Grechenig Smart Boxing Glove “RD <i>α</i>”: IMU Combined with Force Sensor for Highly Accurate Technique and Target Recognition Using Machine Learning Applied Sciences machine learning time-series classification smart gear martial arts combat sports boxing |
| title | Smart Boxing Glove “RD <i>α</i>”: IMU Combined with Force Sensor for Highly Accurate Technique and Target Recognition Using Machine Learning |
| title_full | Smart Boxing Glove “RD <i>α</i>”: IMU Combined with Force Sensor for Highly Accurate Technique and Target Recognition Using Machine Learning |
| title_fullStr | Smart Boxing Glove “RD <i>α</i>”: IMU Combined with Force Sensor for Highly Accurate Technique and Target Recognition Using Machine Learning |
| title_full_unstemmed | Smart Boxing Glove “RD <i>α</i>”: IMU Combined with Force Sensor for Highly Accurate Technique and Target Recognition Using Machine Learning |
| title_short | Smart Boxing Glove “RD <i>α</i>”: IMU Combined with Force Sensor for Highly Accurate Technique and Target Recognition Using Machine Learning |
| title_sort | smart boxing glove rd i α i imu combined with force sensor for highly accurate technique and target recognition using machine learning |
| topic | machine learning time-series classification smart gear martial arts combat sports boxing |
| url | https://www.mdpi.com/2076-3417/13/16/9073 |
| work_keys_str_mv | AT deacizmic smartboxinggloverdiaiimucombinedwithforcesensorforhighlyaccuratetechniqueandtargetrecognitionusingmachinelearning AT dominikhoelbling smartboxinggloverdiaiimucombinedwithforcesensorforhighlyaccuratetechniqueandtargetrecognitionusingmachinelearning AT renebaranyi smartboxinggloverdiaiimucombinedwithforcesensorforhighlyaccuratetechniqueandtargetrecognitionusingmachinelearning AT rolandbreiteneder smartboxinggloverdiaiimucombinedwithforcesensorforhighlyaccuratetechniqueandtargetrecognitionusingmachinelearning AT thomasgrechenig smartboxinggloverdiaiimucombinedwithforcesensorforhighlyaccuratetechniqueandtargetrecognitionusingmachinelearning |