Action Recognition in Basketball with Inertial Measurement Unit-Supported Vest
In this study, an action recognition system was developed to identify fundamental basketball movements using a single Inertial Measurement Unit (IMU) sensor embedded in a wearable vest. This study aims to enhance basketball training by providing a high-performance, low-cost solution that minimizes d...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/2/563 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587487089786880 |
|---|---|
| author | Hamza Sonalcan Enes Bilen Bahar Ateş Ahmet Çağdaş Seçkin |
| author_facet | Hamza Sonalcan Enes Bilen Bahar Ateş Ahmet Çağdaş Seçkin |
| author_sort | Hamza Sonalcan |
| collection | DOAJ |
| description | In this study, an action recognition system was developed to identify fundamental basketball movements using a single Inertial Measurement Unit (IMU) sensor embedded in a wearable vest. This study aims to enhance basketball training by providing a high-performance, low-cost solution that minimizes discomfort for athletes. Data were collected from 21 collegiate basketball players, and movements such as dribbling, passing, shooting, layup, and standing still were recorded. The collected IMU data underwent preprocessing and feature extraction, followed by the application of machine learning algorithms including KNN, decision tree, Random Forest, AdaBoost, and XGBoost. Among these, the XGBoost algorithm with a window size of 250 and a 75% overlap yielded the highest accuracy of 96.6%. The system demonstrated superior performance compared to other single-sensor systems, achieving an overall classification accuracy of 96.9%. This research contributes to the field by presenting a new dataset of basketball movements, comparing the effectiveness of various feature extraction and machine learning methods, and offering a scalable, efficient, and accurate action recognition system for basketball. |
| format | Article |
| id | doaj-art-35d22b90df3344d9ab30b66cfe47f349 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-35d22b90df3344d9ab30b66cfe47f3492025-01-24T13:49:21ZengMDPI AGSensors1424-82202025-01-0125256310.3390/s25020563Action Recognition in Basketball with Inertial Measurement Unit-Supported VestHamza Sonalcan0Enes Bilen1Bahar Ateş2Ahmet Çağdaş Seçkin3Computer Engineering Department, Engineering Faculty, Aydın Adnan Menderes University, Aydın 09100, TürkiyeComputer Engineering Department, Engineering Faculty, Aydın Adnan Menderes University, Aydın 09100, TürkiyeFaculty of Sport Science, Uşak University, Uşak 64100, TürkiyeComputer Engineering Department, Engineering Faculty, Aydın Adnan Menderes University, Aydın 09100, TürkiyeIn this study, an action recognition system was developed to identify fundamental basketball movements using a single Inertial Measurement Unit (IMU) sensor embedded in a wearable vest. This study aims to enhance basketball training by providing a high-performance, low-cost solution that minimizes discomfort for athletes. Data were collected from 21 collegiate basketball players, and movements such as dribbling, passing, shooting, layup, and standing still were recorded. The collected IMU data underwent preprocessing and feature extraction, followed by the application of machine learning algorithms including KNN, decision tree, Random Forest, AdaBoost, and XGBoost. Among these, the XGBoost algorithm with a window size of 250 and a 75% overlap yielded the highest accuracy of 96.6%. The system demonstrated superior performance compared to other single-sensor systems, achieving an overall classification accuracy of 96.9%. This research contributes to the field by presenting a new dataset of basketball movements, comparing the effectiveness of various feature extraction and machine learning methods, and offering a scalable, efficient, and accurate action recognition system for basketball.https://www.mdpi.com/1424-8220/25/2/563action recognitionbasketball traininginertial measurement unit (IMU)machine learningwearable sensors |
| spellingShingle | Hamza Sonalcan Enes Bilen Bahar Ateş Ahmet Çağdaş Seçkin Action Recognition in Basketball with Inertial Measurement Unit-Supported Vest Sensors action recognition basketball training inertial measurement unit (IMU) machine learning wearable sensors |
| title | Action Recognition in Basketball with Inertial Measurement Unit-Supported Vest |
| title_full | Action Recognition in Basketball with Inertial Measurement Unit-Supported Vest |
| title_fullStr | Action Recognition in Basketball with Inertial Measurement Unit-Supported Vest |
| title_full_unstemmed | Action Recognition in Basketball with Inertial Measurement Unit-Supported Vest |
| title_short | Action Recognition in Basketball with Inertial Measurement Unit-Supported Vest |
| title_sort | action recognition in basketball with inertial measurement unit supported vest |
| topic | action recognition basketball training inertial measurement unit (IMU) machine learning wearable sensors |
| url | https://www.mdpi.com/1424-8220/25/2/563 |
| work_keys_str_mv | AT hamzasonalcan actionrecognitioninbasketballwithinertialmeasurementunitsupportedvest AT enesbilen actionrecognitioninbasketballwithinertialmeasurementunitsupportedvest AT baharates actionrecognitioninbasketballwithinertialmeasurementunitsupportedvest AT ahmetcagdasseckin actionrecognitioninbasketballwithinertialmeasurementunitsupportedvest |