LSTM-Guided Coaching Assistant for Table Tennis Practice
Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be u...
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| Format: | Article |
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MDPI AG
2018-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/12/4112 |
| _version_ | 1798039702959292416 |
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| author | Se-Min Lim Hyeong-Cheol Oh Jaein Kim Juwon Lee Jooyoung Park |
| author_facet | Se-Min Lim Hyeong-Cheol Oh Jaein Kim Juwon Lee Jooyoung Park |
| author_sort | Se-Min Lim |
| collection | DOAJ |
| description | Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching. |
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| format | Article |
| id | doaj.art-5d1a2915487d4225b1f15a896ceff5e9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:57:24Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5d1a2915487d4225b1f15a896ceff5e92022-12-22T04:01:02ZengMDPI AGSensors1424-82202018-11-011812411210.3390/s18124112s18124112LSTM-Guided Coaching Assistant for Table Tennis PracticeSe-Min Lim0Hyeong-Cheol Oh1Jaein Kim2Juwon Lee3Jooyoung Park4Department of Electronic and Information Engineering, Korea University, 2511 Sejong-ro, Sejong-City 30016, KoreaDepartment of Electronic and Information Engineering, Korea University, 2511 Sejong-ro, Sejong-City 30016, KoreaDepartment of Mathematics, Korea University, 145 Anam-ro, Anamdong 5-ga, Seoul 02841, KoreaDepartment of Control and Instrumentation Engineering, Korea University, 2511 Sejong-ro, Sejong-City 30016, KoreaDepartment of Control and Instrumentation Engineering, Korea University, 2511 Sejong-ro, Sejong-City 30016, KoreaRecently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.https://www.mdpi.com/1424-8220/18/12/4112wearable sensorsskill assessmentdeep learningLSTMstate space modelprobabilistic inferencelatent features |
| spellingShingle | Se-Min Lim Hyeong-Cheol Oh Jaein Kim Juwon Lee Jooyoung Park LSTM-Guided Coaching Assistant for Table Tennis Practice Sensors wearable sensors skill assessment deep learning LSTM state space model probabilistic inference latent features |
| title | LSTM-Guided Coaching Assistant for Table Tennis Practice |
| title_full | LSTM-Guided Coaching Assistant for Table Tennis Practice |
| title_fullStr | LSTM-Guided Coaching Assistant for Table Tennis Practice |
| title_full_unstemmed | LSTM-Guided Coaching Assistant for Table Tennis Practice |
| title_short | LSTM-Guided Coaching Assistant for Table Tennis Practice |
| title_sort | lstm guided coaching assistant for table tennis practice |
| topic | wearable sensors skill assessment deep learning LSTM state space model probabilistic inference latent features |
| url | https://www.mdpi.com/1424-8220/18/12/4112 |
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