Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms
The aim of this study was to develop a physical activity advisory system supporting the correct implementation of sport exercises using inertial sensors and machine learning algorithms. Specifically, three mobile sensors (tags), six stationary anchors and a system-controlling server (gateway) were e...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/3/1137 |
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| author | Justyna Patalas-Maliszewska Iwona Pajak Pascal Krutz Grzegorz Pajak Matthias Rehm Holger Schlegel Martin Dix |
| author_facet | Justyna Patalas-Maliszewska Iwona Pajak Pascal Krutz Grzegorz Pajak Matthias Rehm Holger Schlegel Martin Dix |
| author_sort | Justyna Patalas-Maliszewska |
| collection | DOAJ |
| description | The aim of this study was to develop a physical activity advisory system supporting the correct implementation of sport exercises using inertial sensors and machine learning algorithms. Specifically, three mobile sensors (tags), six stationary anchors and a system-controlling server (gateway) were employed for 15 scenarios of the series of subsequent activities, namely squats, pull-ups and dips. The proposed solution consists of two modules: an activity recognition module (ARM) and a repetition-counting module (RCM). The former is responsible for extracting the series of subsequent activities (so-called scenario), and the latter determines the number of repetitions of a given activity in a single series. Data used in this study contained 488 three defined sport activity occurrences. Data processing was conducted to enhance performance, including an overlapping and non-overlapping window, raw and normalized data, a convolutional neural network (CNN) with an additional post-processing block (PPB) and repetition counting. The developed system achieved satisfactory accuracy: CNN + PPB: non-overlapping window and raw data, 0.88; non-overlapping window and normalized data, 0.78; overlapping window and raw data, 0.92; overlapping window and normalized data, 0.87. For repetition counting, the achieved accuracies were 0.93 and 0.97 within an error of ±1 and ±2 repetitions, respectively. The archived results indicate that the proposed system could be a helpful tool to support the correct implementation of sport exercises and could be successfully implemented in further work in the form of web application detecting the user’s sport activity. |
| first_indexed | 2024-03-11T09:26:43Z |
| format | Article |
| id | doaj.art-f8011e45bdf341d2a1c877f4ab39067e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T09:26:43Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f8011e45bdf341d2a1c877f4ab39067e2023-11-16T17:56:43ZengMDPI AGSensors1424-82202023-01-01233113710.3390/s23031137Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning AlgorithmsJustyna Patalas-Maliszewska0Iwona Pajak1Pascal Krutz2Grzegorz Pajak3Matthias Rehm4Holger Schlegel5Martin Dix6Institute of Mechanical Engineering, University of Zielona Góra, 65-417 Zielona Gora, PolandInstitute of Mechanical Engineering, University of Zielona Góra, 65-417 Zielona Gora, PolandInstitute for Machine Tools and Production Processes, Chemnitz University of Technology, 09126 Chemnitz, GermanyInstitute of Mechanical Engineering, University of Zielona Góra, 65-417 Zielona Gora, PolandInstitute for Machine Tools and Production Processes, Chemnitz University of Technology, 09126 Chemnitz, GermanyInstitute for Machine Tools and Production Processes, Chemnitz University of Technology, 09126 Chemnitz, GermanyInstitute for Machine Tools and Production Processes, Chemnitz University of Technology, 09126 Chemnitz, GermanyThe aim of this study was to develop a physical activity advisory system supporting the correct implementation of sport exercises using inertial sensors and machine learning algorithms. Specifically, three mobile sensors (tags), six stationary anchors and a system-controlling server (gateway) were employed for 15 scenarios of the series of subsequent activities, namely squats, pull-ups and dips. The proposed solution consists of two modules: an activity recognition module (ARM) and a repetition-counting module (RCM). The former is responsible for extracting the series of subsequent activities (so-called scenario), and the latter determines the number of repetitions of a given activity in a single series. Data used in this study contained 488 three defined sport activity occurrences. Data processing was conducted to enhance performance, including an overlapping and non-overlapping window, raw and normalized data, a convolutional neural network (CNN) with an additional post-processing block (PPB) and repetition counting. The developed system achieved satisfactory accuracy: CNN + PPB: non-overlapping window and raw data, 0.88; non-overlapping window and normalized data, 0.78; overlapping window and raw data, 0.92; overlapping window and normalized data, 0.87. For repetition counting, the achieved accuracies were 0.93 and 0.97 within an error of ±1 and ±2 repetitions, respectively. The archived results indicate that the proposed system could be a helpful tool to support the correct implementation of sport exercises and could be successfully implemented in further work in the form of web application detecting the user’s sport activity.https://www.mdpi.com/1424-8220/23/3/1137mobile sensors (tags)anchorsfitness trackingpersonal trainingsport activitiessport activity advisory system |
| spellingShingle | Justyna Patalas-Maliszewska Iwona Pajak Pascal Krutz Grzegorz Pajak Matthias Rehm Holger Schlegel Martin Dix Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms Sensors mobile sensors (tags) anchors fitness tracking personal training sport activities sport activity advisory system |
| title | Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms |
| title_full | Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms |
| title_fullStr | Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms |
| title_full_unstemmed | Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms |
| title_short | Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms |
| title_sort | inertial sensor based sport activity advisory system using machine learning algorithms |
| topic | mobile sensors (tags) anchors fitness tracking personal training sport activities sport activity advisory system |
| url | https://www.mdpi.com/1424-8220/23/3/1137 |
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