Sensor Data Required for Automatic Recognition of Athletic Tasks Using Deep Neural Networks
Open accessAbstract
Movement screens are used to assess the overall movement quality of an athlete. However, these rely on visual observation of a series of movements and subjective scoring. Data-driven methods to provide objective scoring of these movements are being developed. These currently use optical motion capture and require manual pre-processing of data to identify the start and end points of each movement. Therefore, we aimed to use deep learning techniques to automatically identify movements typically found in movement screens and assess the feasibility of performing the classification based on wearable sensor data. Optical motion capture data were collected on 417 athletes performing 13 athletic movements. We trained an existing deep neural network architecture that combines convolutional and recurrent layers on a subset of 278 athletes. A validation subset of 69 athletes was used to tune the hyperparameters and the final network was tested on the remaining 70 athletes. Simulated inertial measurement data were generated based on the optical motion capture data and the network was trained on this data for different combinations of body segments. Classification accuracy was similar for networks trained using the optical and full-body simulated inertial measurement unit data at 90.1 and 90.2%, respectively. A good classification accuracy of 85.9% was obtained using as few as three simulated sensors placed on the torso and shanks. However, using three simulated sensors on the torso and upper arms or fewer than three sensors resulted in poor accuracy. These results for simulated sensor data indicate the feasibility of classifying athletic movements using a small number of wearable sensors. This could facilitate objective data-driven methods that automatically score overall movement quality using wearable sensors to be easily implemented in the field.
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Integrating more body parts increased the classification accuracy of the NN system.
“According to this study, integrating more body parts increased the classification accuracy of their NN system [171].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [171]
Deep neural networks trained on optical and IMU data obtained nearly identical F1 scores between 0.8 and 0.9.
“Deep neural networks trained on optical and IMU data obtained nearly identical F1 scores between 0.8 and 0.9.”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [171]
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Cites
Lecture Notes in Computer Science 1205
(1999) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir.1999.04926fae.001 · Cited by 39,228 · see citing works
2009 International Joint Conference on Artificial Intelligence
(2009) · doi:10.1109/jcai15434.2009 · Cited by 2,846 · see citing works
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Ordóñez, F., & Roggen, D. (2016) · Sensors · doi:10.3390/s16010115 · Cited by 2,761 · cited 4× · see citing works
“The network combines convolutional and recurrent layers (Ordóñez and Roggen, 2016).”
BackgroundMethodInfluentialA tutorial on human activity recognition using body-worn inertial sensors
Bulling, A., Blanke, U., & Schiele, B. (2014) · ACM Computing Surveys · doi:10.1145/2499621 · Cited by 1,668 · see citing works
Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors
Zeng, M., Nguyen, L. T., Yu, B., Mengshoel, O. J., Zhu, J., Wu, P., & Zhang, J. (2014) · Proceedings of the 6th International Conference on Mobile Computing, Applications and Services · doi:10.4108/icst.mobicase.2014.257786 · Cited by 876 · see citing works
Physical Human Activity Recognition Using Wearable Sensors
Attal, F., Mohammed, S., Dedabrishvili, M., Chamroukhi, F., Oukhellou, L., & Amirat, Y. (2015) · Sensors · doi:10.3390/s151229858 · Cited by 806 · see citing works
Activity Classification Using Realistic Data From Wearable Sensors
Pärkkä, J., Ermes, M., Korpipää, P., Mäntyjärvi, J., Peltola, J., & Korhonen, I. (2006) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2005.856863 · Cited by 759 · see citing works
Optimal Placement of Accelerometers for the Detection of Everyday Activities
Cleland, I., Kikhia, B., Nugent, C., Boytsov, A., Hallberg, J., Synnes, K., McClean, S. I., & Finlay, D. (2013) · Sensors · doi:10.3390/s130709183 · Cited by 403 · see citing works
Hand Gesture Recognition Using Micro-Doppler Signatures With Convolutional Neural Network
Kim, Y., & Toomajian, B. (2016) · IEEE Access · doi:10.1109/access.2016.2617282 · Cited by 379 · see citing works
Interrater Reliability of the Functional Movement Screen
Minick, K. I., Kiesel, K. B., Burton, L., et al. (2010) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0b013e3181c09c04 · Cited by 376 · see citing works
Human activity recognition from accelerometer data using Convolutional Neural Network
Lee, S.-M., Yoon, S. M., & Cho, H. (2017) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp.2017.7881728 · Cited by 356 · see citing works
Sensor Positioning for Activity Recognition Using Wearable Accelerometers
Atallah, L., Lo, B., King, R., & Yang, G.-Z. (2011) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2011.2160540 · Cited by 346 · see citing works
Cust, E. E., Sweeting, A. J., Ball, K. A., & Robertson, S. J. (2018) · Journal of Sports Sciences · doi:10.1080/02640414.2018.1521769 · Cited by 340 · see citing works
The Functional Movement Screen: A Reliability Study
Teyhen, D. S., Shaffer, S. W., Lorenson, C. L., Halfpap, J. P., Donofry, D. F., Walker, M. J., Dugan, J. L., & Childs, J. D. (2012) · Journal of Orthopaedic and Sports Physical Therapy · doi:10.2519/jospt.2012.3838 · Cited by 335 · see citing works
Bonazza, N. A., Smuin, D. M., Onks, C. A., Silvis, M. L., & Dhawan, A. (2016) · The American Journal of Sports Medicine · doi:10.1177/0363546516641937 · Cited by 329 · see citing works
Accelerometer Placement for Posture Recognition and Fall Detection
Gjoreski, H., Luštrek, M., & Gams, M. (2011) · International Conference on Intelligent Environments (IE) · doi:10.1109/ie.2011.11 · Cited by 209 · see citing works
Interrater and Intrarater Reliability of the Functional Movement Screen
Smith, C. A., Chimera, N. J., Wright, N. J., & Warren, M. (2012) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0b013e3182606df2 · Cited by 168 · see citing works
Activity recognition in beach volleyball using a Deep Convolutional Neural Network
Kautz, T., Groh, B. H., Hannink, J., Jensen, U. J., Strubberg, H., & Eskofier, B. M. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-017-0495-0 · Cited by 162 · see citing works
Intrarater Reliability of the Functional Movement Screen
Gribble, P. A., Brigle, J., Pietrosimone, B. G., et al. (2012) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0b013e31825c32a8 · Cited by 132 · see citing works
Human Activity Recognition: Accuracy across Common Locations for Wearable Sensors
õn, D. O. ́., & õn, O. ́. (2006) · Cited by 113 · see citing works
Frost, D. M., Beach, T. A. C., Callaghan, J. P., & McGill, S. M. (2015) · The Journal of Strength and Conditioning Research · doi:10.1097/jsc.0000000000000211 · Cited by 78 · see citing works
Basketball Activity Recognition using Wearable Inertial Measurement Units
Nguyen, L. N., Rodríguez-Martín, D., Catalá, A., Pérez‐López, C., Samà, A., & Cavallaro, A. (2015) · International Conference on Human Computer Interaction (Interacción) · doi:10.1145/2829875.2829930 · Cited by 76 · see citing works
RSSIからCSIへ:チャネルレスポンスによるインドア・ローカリゼーション
Zheng, Y., Zhou, Z., & Liu, Y. (2013) · ACM Computing Surveys · Cited by 70 · see citing works
Ambulatory monitoring of human posture and walking speed using wearable accelerometer sensors
Yeoh, W.-S., Pek, I., Yong, Y.-H., et al. (2008) · doi:10.1109/iembs.2008.4650382 · Cited by 68 · see citing works
IJCAI International Joint Conference on Artificial Intelligence
Grossi, D., & Modgil, S. (2015) · International Joint Conference on Artificial Intelligence · Cited by 60 · see citing works
Gulgin, H. R., & Hoogenboom, B. J. (2014) · PubMed · Cited by 59 · see citing works
Wearable Motion Sensor Based Analysis of Swing Sports
Anand, A., Sharma, M., Srivastava, R., Kaligounder, L., & Prakash, D. (2017) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2017.0-149 · Cited by 54 · see citing works
Pannurat, N., Thiemjarus, S., Nantajeewarawat, E., & Anantavrasilp, I. (2017) · Sensors · doi:10.3390/s17040774 · Cited by 45 · see citing works
Objectively Differentiating Movement Patterns between Elite and Novice Athletes
Ross, G. B., Dowling, B., Troje, N. F., et al. (2018) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001571 · Cited by 35 · see citing works
Self-citationProceedings of the 9th international conference on intelligentenvironments (IE '13)
Augusto, J. C., Bourdakis, V., Braga, D., et al. (2013) · IEEE Press eBooks · Cited by 32 · see citing works
Relationship Between Functional Assessments and Exercise-Related Changes During Static Balance
Clifton, D. R., Harrison, B. C., Hertel, J. N., et al. (2012) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0b013e318260b723 · Cited by 32 · see citing works
Wearable trick classification in freestyle snowboarding
Groh, B. H., Fleckenstein, M., & Eskofier, B. M. (2016) · IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN) · doi:10.1109/bsn.2016.7516238 · Cited by 24 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Paszke A. Gross S. Chintala S. Chanan G. Yang E. DeVito Z. (2017). Automatic differentiation in PyTorch, in NIPS Autodiff Workshop (Long Beach, CA).
- Schuldhaus D. Zwick C. Körger H. Dorschky E. Kirk R. Eskofier B. M. (2015). Inertial sensor-based approach for shot/pass classification during a soccer match, in 21st ACM KDD Workshop on Large-Scale Sports Analytics (Sydney, NSW), 1– 4.
Cited by
Sorted by citations
Review of Wearable Devices and Data Collection Considerations for Connected Health
Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021) · Sensors · doi:10.3390/s21165589 · Cited by 435 · see citing works
- Authors
- Clouthier, A. L., Ross, G. B., & Graham, R. B.
- Affiliations
- University of Ottawa (CA)
- Corresponding author
- Graham, Ryan B.
- Venue
- Frontiers in Bioengineering and Biotechnology
- Publisher
- Frontiers Media SA
- Published
- 2020-01-21
- Volume
- 7
- Pages
- 473-473
- ISSN
- 2296-4185 (ISSN-L)
- PMC
- PMC6985033
- OpenAlex
- W3002496996
- Cited by
- 29
- References
- 33
- Citations by year
- 2025: 3 · 2024: 4 · 2023: 7 · 2022: 2 · 2021: 7 · 2020: 6
- FWCI
- 2.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Bioengineering and Biotechnology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Ontario Ministry of Economic Development, Job Creation and Trade
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
- Keywords
- wearable sensors, inertial measurement unit, simulated sensor data, optical motion capture, convolutional recurrent neural network, movement quality assessment, sensor placement
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