慣性センサデータを用いたディープラーニングによる空手動作識別手法の開発
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| Published in | 日本機械学会論文集 Vol. 87; no. 903; p. 21-00214 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
一般社団法人 日本機械学会
2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2187-9761 |
| DOI | 10.1299/transjsme.21-00214 |
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| Author | 佐武, 陸史 岩田, 浩康 相原, 伸平 石部, 開 |
|---|---|
| Author_xml | – sequence: 1 fullname: 岩田, 浩康 organization: 早稲田大学 理工学術院 – sequence: 1 fullname: 佐武, 陸史 organization: 早稲田大学 創造理工学研究科 – sequence: 1 fullname: 石部, 開 organization: 早稲田大学 創造理工学研究科 – sequence: 1 fullname: 相原, 伸平 organization: 早稲田大学 創造理工学研究科 |
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| Copyright | 2021 一般社団法人日本機械学会 |
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| References | Hafer, J. F. and Boyer, K. A., Variability of segment coordination using a vector coding technique: Reliability analysis for treadmill walking and running, Gait and Posture, Vol.51 (2017), pp.222–227. Groh, B. H., Flaschka, J., Wirth, M., Kautz, T., Fleckenstein, M. and Eskofier, B. M., Wearable real-time skateboard trick visualization and its community perception, IEEE Computer Graphics and Applications, No.36, Vol.5 (2016), pp.12–18. Edelmann-Nusser, A., Raschke, A., Bentz, A., Montenbruck, S., Edelmann-Nusser, J. and Lames, M., Validation of sensor-based game analysis tools in tennis, International Journal of Computer Science in Sport, Vol.18, No.2 (2019), pp.49–59. Srivastava, N., Hinton, G., Krizhevsky, A. and Salakhutdinov, R., Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, Vol.15, No.56 (2014), pp.1929–1958. Wang, L. K. and Liu, R. Y., Human activity recognition based on wearable sensor using hierarchical deep LSTM networks, Circuits, Systems, and Signal Processing, Vol.39, No.2 (2020), pp.837–856. Kearns, M. and Ron, D., Algorithmic stability and sanity-check bounds for leave-one-out cross-validation, Neural Computation, Vol.11, No.6 (1999), pp.1427–1453. Ronao, C. A. and Cho, S. B., Human activity recognition with smartphone sensors using deep learning neural networks, Expert Systems with Applications, Vol.59 (2016), pp.235–244. Wundersitz, D. W. T., Gastin, P. B., Richter, C., Robertson, S. J. and Netto, K. J., Validity of a trunk-mounted accelerometer to assess peak accelerations during walking, jogging and running, European Journal of Sport Science, Vol.15, No.5 (2015a), pp.382–390. Lim, S. M., Oh, H. C., Kim, J., Lee, J. and Park, J., LSTM-guided coaching assistant for table tennis practice, Sensors, Vol.18, No.12 (2018), 4112. Hossain, H. M. S., Khan, M. A. A. H. and Roy, N., SoccerMate: A personal soccer attribute profiler using wearables, In IEEE International Conference on Pervasive Computing and Communications Workshops (2017), pp.164-169. Jens, B., Magni, M., Allan, P., Jorge, P. G. and Peter, K., Training and testing the elite athlete, Journal of Exercise Science and Fitness, Vol.4 No.1 (2006), pp.1–14. Preece, S. J., Goulermas, J. Y., Kenney, L. P. J. and Howard, D., A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data, IEEE Transactions on Biomedical Engineering, Vol.56, No.3 (2009), pp.871–879. Vales-Alonso, J., Chaves-Dieguez, D., Lopez-Matencio, P., Alcaraz, J. J., Parrado-Garcia, F. J. and Gonzalez-Castano, F. J., SAETA: A smart coaching assistant for professional volleyball training. IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol.45, No.8 (2015), pp.1138–1150. Lückemann, P., Haid, D. M., Brömel, P., Schwanitz, S. and Maiwald, C., Validation of an inertial sensor system for swing analysis in golf, Multidisciplinary Digital Publishing Institute Proceedings, Vol.2, No.6 (2018), pp.246-252. Prayudi, I. and Kim, D., Design and implementation of IMU-based human arm motion capture system, In IEEE International Conference on Mechatronics and Automation (2012), pp.670–675. Yang, J., Nguyen, M. N., San, P. P., Li, X. and Krishnaswamy, S., Deep convolutional neural networks on multichannel time series for human activity recognition, In International Conference on Artificial Intelligence (2015), pp.3995–4001. Morris, D., Saponas, T. S., Guillory, A. and Kelner, I., RecoFit: Using a wearable sensor to find, recognize, and count repetitive exercises, In Conference on Human Factors in Computing Systems (2014), pp.3225–3234. Bao, L. and Intille, S. S., Activity recognition from user-annotated acceleration data, In International conference on pervasive computing, Vol.3001 (2004), pp.1–17. Bux, A., Angelov, P. and Habib, Z., Vision based human activity recognition: A review, In Advances in Intelligent Systems and Computing, Vol. 513 (2017), pp. 341–371. Marcard, T. von, Rosenhahn, B., Black, M. J. and Pons-Moll, G., Sparse inertial poser: automatic 3D human pose estimation from sparse IMUs, Computer Graphics Forum, Vol.36, No.2 (2017), pp.349–360. Ravi, D., Wong, C., Lo, B. and Yang, G. Z., A deep learning approach to on-node sensor data analytics for mobile or wearable devices, IEEE Journal of Biomedical and Health Informatics, Vol.21, No.1 (2017), pp.56–64. Wagner, D., Kalischewski, K., Velten, J. and Kummert, A., Activity recognition using inertial sensors and a 2-D convolutional neural network, In International Workshop on Multidimensional Systems (2017), pp.1–6. Zebin, T., Scully, P. J. and Ozanyan, K. B., Human activity recognition with inertial sensors using a deep learning approach, In Proceedings of IEEE Sensors (2017), pp.1–3. Lecun, Y., Bengio, Y. and Hinton, G., Deep learning, Nature, Vol.521, No.7553 (2015), pp.436–444. Giblin, G., Tor, E. and Parrington, L., The impact of technology on elite sports performance, Sensoria A Journal of Mind Brain and Culture, Vol.12, No.2 (2016), pp.3–12. Bengio, Y., Deep learning of representations: Looking forward, In International Conference on Statistical Language and Speech Processing, Vol.7978 (2013), pp.1–37. Connaghan, D., Kelly, P., O’Connor, N. E., Gaffney, M., Walsh, M. and O’Mathuna, C., Multi-sensor classification of tennis strokes, In Proceedings of IEEE Sensors (2011), pp. 1437–1440. Mannini, A. and Sabatini, A. M., Machine learning methods for classifying human physical activity from on-body accelerometers, Sensors, Vol.10, No.2 (2010), pp.1154–1175. Schuldhaus, D., Zwick, C., Körger, H., Dorschky, E., Kirk, R. and Eskofier, B. M., Inertial sensor-based approach for shot/pass classification during a soccer match, In KDD workshop on large-scale sports analytics (2015), pp. 1–4. Hochreiter, S. and Schmidhuber, J., Long short-term memory, Neural Computation, Vol.9, No.8 (1997), pp.1735–1780. Mitchell, E., Monaghan, D. and O’Connor, N. E., Classification of sporting activities using smartphone accelerometers, Sensors, Vol.13, No.4 (2013), pp.5317–5337. Oetken, G., Parks, T. W. and SchÜSsler, H. W., New results in the design of digital interpolators. IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol.23, No.3 (1975), pp.301–309. Thomas, O., Sunehag, P., Dror, G., Yun, S., Kim, S., Robards, M., Smola, A., Green, D. and Saunders, P., Wearable sensor activity analysis using semi-Markov models with a grammar, Pervasive and Mobile Computing, Vol.6, No.3 (2010), pp.342–350. Wundersitz, D. W. T., Josman, C., Gupta, R., Netto, K. J., Gastin, P. B. and Robertson, S., Classification of team sport activities using a single wearable tracking device, Journal of Biomechanics, Vol.48, No.15 (2015b), pp.3975–3981. Groh, B. H., Fleckenstein, M., Kautz, T. and Eskofier, B. M., Classification and visualization of skateboard tricks using wearable sensors, Pervasive and Mobile Computing, Vol.40 (2017), pp.42–55. Rana, M. and Mittal, V., Wearable sensors for real-time kinematics analysis in sports: a review, IEEE Sensors Journal, Vol.21, No.2 (2021), pp.1187–1207. Coren, S., The lateral preference inventory for measurement of handedness, footedness, eyedness, and earedness: Norms for young adults, Bulletin of the Psychonomic Society, Vol.31, No.1 (1993), pp.1–3. Sze, V., Chen, Y. H., Yang, T. J. and Emer, J. S., Efficient processing of deep neural networks: A tutorial and survey, Proceedings of the IEEE, Vol.105, No.12 (2017), pp.2295–2329. Young, C. and Reinkensmeyer, D. J., Judging complex movement performances for excellence: A principal components analysis-based technique applied to competitive diving, Human Movement Science, Vol.36 (2014), pp.107–122. Sharma, M., Srivastava, R., Anand, A., Prakash, D. and Kaligounder, L., Wearable motion sensor based phasic analysis of tennis serve for performance feedback, In IEEE International Conference on Acoustics, Speech and Signal Processing (2017), pp.5945–5949. Ahmadi, A., Mitchell, E., Richter, C., Destelle, F., Gowing, M., O’Connor, N. E. and Moran, K., Toward automatic activity classification and movement assessment during a sports training session, IEEE Internet of Things Journal, Vol.2, No.1 (2015), pp.23–32. Myers, N., Kibler, W., Axtell, A. and Uhl, T., The Sony Smart Tennis Sensor accurately measures external workload in junior tennis players, International Journal of Sports Science & Coaching, Vol.14, No.1 (2018), pp.24–31. Dopico-Calvo, X., Iglesias-Soler, E., Morenilla, L., Giráldez, M. A., Santos, L. and Ardá, A., Laterality and performance in combat sports, Archives of Budo, Vol.12 (2016), pp.167–177. |
| References_xml | – reference: Hafer, J. F. and Boyer, K. A., Variability of segment coordination using a vector coding technique: Reliability analysis for treadmill walking and running, Gait and Posture, Vol.51 (2017), pp.222–227. – reference: Wundersitz, D. W. T., Gastin, P. B., Richter, C., Robertson, S. J. and Netto, K. J., Validity of a trunk-mounted accelerometer to assess peak accelerations during walking, jogging and running, European Journal of Sport Science, Vol.15, No.5 (2015a), pp.382–390. – reference: Yang, J., Nguyen, M. N., San, P. P., Li, X. and Krishnaswamy, S., Deep convolutional neural networks on multichannel time series for human activity recognition, In International Conference on Artificial Intelligence (2015), pp.3995–4001. – reference: Wundersitz, D. W. T., Josman, C., Gupta, R., Netto, K. J., Gastin, P. B. and Robertson, S., Classification of team sport activities using a single wearable tracking device, Journal of Biomechanics, Vol.48, No.15 (2015b), pp.3975–3981. – reference: Giblin, G., Tor, E. and Parrington, L., The impact of technology on elite sports performance, Sensoria A Journal of Mind Brain and Culture, Vol.12, No.2 (2016), pp.3–12. – reference: Mannini, A. and Sabatini, A. M., Machine learning methods for classifying human physical activity from on-body accelerometers, Sensors, Vol.10, No.2 (2010), pp.1154–1175. – reference: Schuldhaus, D., Zwick, C., Körger, H., Dorschky, E., Kirk, R. and Eskofier, B. M., Inertial sensor-based approach for shot/pass classification during a soccer match, In KDD workshop on large-scale sports analytics (2015), pp. 1–4. – reference: Coren, S., The lateral preference inventory for measurement of handedness, footedness, eyedness, and earedness: Norms for young adults, Bulletin of the Psychonomic Society, Vol.31, No.1 (1993), pp.1–3. – reference: Hossain, H. M. S., Khan, M. A. A. H. and Roy, N., SoccerMate: A personal soccer attribute profiler using wearables, In IEEE International Conference on Pervasive Computing and Communications Workshops (2017), pp.164-169. – reference: Ahmadi, A., Mitchell, E., Richter, C., Destelle, F., Gowing, M., O’Connor, N. E. and Moran, K., Toward automatic activity classification and movement assessment during a sports training session, IEEE Internet of Things Journal, Vol.2, No.1 (2015), pp.23–32. – reference: Sharma, M., Srivastava, R., Anand, A., Prakash, D. and Kaligounder, L., Wearable motion sensor based phasic analysis of tennis serve for performance feedback, In IEEE International Conference on Acoustics, Speech and Signal Processing (2017), pp.5945–5949. – reference: Thomas, O., Sunehag, P., Dror, G., Yun, S., Kim, S., Robards, M., Smola, A., Green, D. and Saunders, P., Wearable sensor activity analysis using semi-Markov models with a grammar, Pervasive and Mobile Computing, Vol.6, No.3 (2010), pp.342–350. – reference: Myers, N., Kibler, W., Axtell, A. and Uhl, T., The Sony Smart Tennis Sensor accurately measures external workload in junior tennis players, International Journal of Sports Science & Coaching, Vol.14, No.1 (2018), pp.24–31. – reference: Srivastava, N., Hinton, G., Krizhevsky, A. and Salakhutdinov, R., Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, Vol.15, No.56 (2014), pp.1929–1958. – reference: Wagner, D., Kalischewski, K., Velten, J. and Kummert, A., Activity recognition using inertial sensors and a 2-D convolutional neural network, In International Workshop on Multidimensional Systems (2017), pp.1–6. – reference: Jens, B., Magni, M., Allan, P., Jorge, P. G. and Peter, K., Training and testing the elite athlete, Journal of Exercise Science and Fitness, Vol.4 No.1 (2006), pp.1–14. – reference: Mitchell, E., Monaghan, D. and O’Connor, N. E., Classification of sporting activities using smartphone accelerometers, Sensors, Vol.13, No.4 (2013), pp.5317–5337. – reference: Lückemann, P., Haid, D. M., Brömel, P., Schwanitz, S. and Maiwald, C., Validation of an inertial sensor system for swing analysis in golf, Multidisciplinary Digital Publishing Institute Proceedings, Vol.2, No.6 (2018), pp.246-252. – reference: Connaghan, D., Kelly, P., O’Connor, N. E., Gaffney, M., Walsh, M. and O’Mathuna, C., Multi-sensor classification of tennis strokes, In Proceedings of IEEE Sensors (2011), pp. 1437–1440. – reference: Groh, B. H., Fleckenstein, M., Kautz, T. and Eskofier, B. M., Classification and visualization of skateboard tricks using wearable sensors, Pervasive and Mobile Computing, Vol.40 (2017), pp.42–55. – reference: Hochreiter, S. and Schmidhuber, J., Long short-term memory, Neural Computation, Vol.9, No.8 (1997), pp.1735–1780. – reference: Bux, A., Angelov, P. and Habib, Z., Vision based human activity recognition: A review, In Advances in Intelligent Systems and Computing, Vol. 513 (2017), pp. 341–371. – reference: Vales-Alonso, J., Chaves-Dieguez, D., Lopez-Matencio, P., Alcaraz, J. J., Parrado-Garcia, F. J. and Gonzalez-Castano, F. J., SAETA: A smart coaching assistant for professional volleyball training. IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol.45, No.8 (2015), pp.1138–1150. – reference: Zebin, T., Scully, P. J. and Ozanyan, K. B., Human activity recognition with inertial sensors using a deep learning approach, In Proceedings of IEEE Sensors (2017), pp.1–3. – reference: Preece, S. J., Goulermas, J. Y., Kenney, L. P. J. and Howard, D., A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data, IEEE Transactions on Biomedical Engineering, Vol.56, No.3 (2009), pp.871–879. – reference: Marcard, T. von, Rosenhahn, B., Black, M. J. and Pons-Moll, G., Sparse inertial poser: automatic 3D human pose estimation from sparse IMUs, Computer Graphics Forum, Vol.36, No.2 (2017), pp.349–360. – reference: Edelmann-Nusser, A., Raschke, A., Bentz, A., Montenbruck, S., Edelmann-Nusser, J. and Lames, M., Validation of sensor-based game analysis tools in tennis, International Journal of Computer Science in Sport, Vol.18, No.2 (2019), pp.49–59. – reference: Bengio, Y., Deep learning of representations: Looking forward, In International Conference on Statistical Language and Speech Processing, Vol.7978 (2013), pp.1–37. – reference: Rana, M. and Mittal, V., Wearable sensors for real-time kinematics analysis in sports: a review, IEEE Sensors Journal, Vol.21, No.2 (2021), pp.1187–1207. – reference: Oetken, G., Parks, T. W. and SchÜSsler, H. W., New results in the design of digital interpolators. IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol.23, No.3 (1975), pp.301–309. – reference: Lim, S. M., Oh, H. C., Kim, J., Lee, J. and Park, J., LSTM-guided coaching assistant for table tennis practice, Sensors, Vol.18, No.12 (2018), 4112. – reference: Ravi, D., Wong, C., Lo, B. and Yang, G. Z., A deep learning approach to on-node sensor data analytics for mobile or wearable devices, IEEE Journal of Biomedical and Health Informatics, Vol.21, No.1 (2017), pp.56–64. – reference: Wang, L. K. and Liu, R. Y., Human activity recognition based on wearable sensor using hierarchical deep LSTM networks, Circuits, Systems, and Signal Processing, Vol.39, No.2 (2020), pp.837–856. – reference: Bao, L. and Intille, S. S., Activity recognition from user-annotated acceleration data, In International conference on pervasive computing, Vol.3001 (2004), pp.1–17. – reference: Ronao, C. A. and Cho, S. B., Human activity recognition with smartphone sensors using deep learning neural networks, Expert Systems with Applications, Vol.59 (2016), pp.235–244. – reference: Dopico-Calvo, X., Iglesias-Soler, E., Morenilla, L., Giráldez, M. A., Santos, L. and Ardá, A., Laterality and performance in combat sports, Archives of Budo, Vol.12 (2016), pp.167–177. – reference: Morris, D., Saponas, T. S., Guillory, A. and Kelner, I., RecoFit: Using a wearable sensor to find, recognize, and count repetitive exercises, In Conference on Human Factors in Computing Systems (2014), pp.3225–3234. – reference: Lecun, Y., Bengio, Y. and Hinton, G., Deep learning, Nature, Vol.521, No.7553 (2015), pp.436–444. – reference: Kearns, M. and Ron, D., Algorithmic stability and sanity-check bounds for leave-one-out cross-validation, Neural Computation, Vol.11, No.6 (1999), pp.1427–1453. – reference: Sze, V., Chen, Y. H., Yang, T. J. and Emer, J. S., Efficient processing of deep neural networks: A tutorial and survey, Proceedings of the IEEE, Vol.105, No.12 (2017), pp.2295–2329. – reference: Groh, B. H., Flaschka, J., Wirth, M., Kautz, T., Fleckenstein, M. and Eskofier, B. M., Wearable real-time skateboard trick visualization and its community perception, IEEE Computer Graphics and Applications, No.36, Vol.5 (2016), pp.12–18. – reference: Prayudi, I. and Kim, D., Design and implementation of IMU-based human arm motion capture system, In IEEE International Conference on Mechatronics and Automation (2012), pp.670–675. – reference: Young, C. and Reinkensmeyer, D. J., Judging complex movement performances for excellence: A principal components analysis-based technique applied to competitive diving, Human Movement Science, Vol.36 (2014), pp.107–122. |
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| Title | 慣性センサデータを用いたディープラーニングによる空手動作識別手法の開発 |
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