Alexander Gebhard
ABSTRACT
Timely detection of runner exertion is crucial for preventing overuse injuries and conditioning training. Similarly, maintaining high levels of wellbeing while running can improve retention rates for onboarders to the sport, with the associated benefits to public health that this entails. Thus, predicting exertion and wellbeing is a promising avenue of research for biomedical sports research. Previous work has shown that exertion and wellbeing can be predicted using biomechanical data collected from wearables attached to the runners' body. However, a particular challenge in outdoor running conditions is the mediating effect of running surface. We experimentally model this mediating effect by using surface-adapted models, which improve prediction rates for both variables. To that end, we investigate the feasibility of using audio-based surface classification to distinguish three main surface categories: gravel, asphalt, and dirt. Our best models achieve an unweighted average recall (UAR) of .619 and a UAR of .690 on our session-independent and session-dependent test set, respectively, which is an improvement over the .363 UAR achieved by a GPS-based approximation.
- Shahin Amiriparian. 2019. Deep representation learning techniques for audio signal processing. Ph. D. Dissertation. Technische Universität München.Google Scholar
- Shahin Amiriparian, Maurice Gerczuk, Sandra Ottl, Nicholas Cummins, Michael Freitag, Sergey Pugachevskiy, Alice Baird, and Björn Schuller. 2017. Snore Sound Classification Using Image-Based Deep Spectrum Features. In Interspeech 2017. ISCA, Stockholm, Sweden, 3512--3516.Google ScholarCross Ref
- Shahin Amiriparian, Maurice Gerczuk, Sandra Ottl, Lukas Stappen, Alice Baird, Lukas Koebe, and Björn Schuller. 2020. Towards cross-modal pre-training and learning tempo-spatial characteristics for audio recognition with convolutional and recurrent neural networks. EURASIP Journal on Audio, Speech, and Music Processing 2020, 1 (2020), 1--11.Google ScholarDigital Library
- Shahin Amiriparian, Tobias Hübner, Vincent Karas, Maurice Gerczuk, Sandra Ottl, and Björn W. Schuller. 2022. DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing From Decentralized Data. Frontiers in Artificial Intelligence 5 (2022), 10 pages. https://doi.org/10.3389/frai.2022.856232Google Scholar
- Shahin Amiriparian and Björn Schuller. 2021. AI Hears Your Health: Computer Audition for Health Monitoringy. In Proceedings of ICT for Health, Accessibility and Wellbeing, IC-IHAW (Lecture Notes in Communications in Computer and Information Science). Springer, Larnaca, Cyprus.Google ScholarCross Ref
- PC Dixon, KH Schütte, B Vanwanseele, JV Jacobs, JT Dennerlein, JM Schiffman, PA Fournier, and B Hu. 2019. Machine learning algorithms can classify outdoor terrain types during running using accelerometry data. Gait & posture 74 (2019), 176--181.Google Scholar
- Moritz Einfalt, Charles Dampeyrou, Dan Zecha, and Rainer Lienhart. 2019. Framelevel event detection in athletics videos with pose-based convolutional sequence networks. In Proceedings Proceedings of the 2nd International Workshop on Multimedia Content Analysis in Sports. Association for Computing Machinery, Nice, France, 42--50.Google ScholarDigital Library
- Moritz Einfalt, Dan Zecha, and Rainer Lienhart. 2018. Activity-conditioned continuous human pose estimation for performance analysis of athletes using the example of swimming. In 2018 IEEE winter conference on applications of computer vision (WACV). IEEE, Lake Tahoe, NV, USA, 446--455.Google Scholar
- Bjoern Eskofier, Florian Hoenig, and Pascal Kuehner. 2008. Classification of perceived running fatigue in digital sports. In 2008 19th International Conference on Pattern Recognition. IEEE, Tampa, FL, USA, 1--4.Google ScholarCross Ref
- Björn Eskofier, Patrick Kugler, Daniel Melzer, and Pascal Kuehner. 2012. Embedded classification of the perceived fatigue state of runners: Towards a body sensor network for assessing the fatigue state during running. In Proc. International Conference on Wearable and Implantable Body Sensor Networks (Virtual). IEEE, London, UK, 113--117.Google ScholarDigital Library
- Alessandro Giusti, Jérôme Guzzi, Dan C Cirean, Fang-Lin He, Juan P Rodríguez, Flavio Fontana, Matthias Faessler, Christian Forster, Jürgen Schmidhuber, Gianni Di Caro, Davide Scaramuzza, and Luca M Gambardella. 2015. A machine learning approach to visual perception of forest trails for mobile robots. IEEE Robotics and Automation Letters 1, 2 (2015), 661--667.Google ScholarCross Ref
- Xiaole Guan, Yanfei Lin, Qun Wang, Zhiwen Liu, and Chengyi Liu. 2021. Sports fatigue detection based on deep learning. In Proc. International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, Shanghai, China (online), 1--6.Google ScholarCross Ref
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Las Vegas, NV, USA, 770--778.Google ScholarCross Ref
- Christina R. Hollis, Rachel M. Koldenhoven, Jacob E. Resch, and Jay Hertel. 2021. Running biomechanics as measured by wearable sensors: effects of speed and surface. Sports Biomechanics 20, 5 (2021), 521--531.Google ScholarCross Ref
- Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Honolulu, HI, USA, 4700--4708.Google ScholarCross Ref
- Pouya Jafarzadeh, Petra Virjonen, Paavo Nevalainen, Fahimeh Farahnakian, and Jukka Heikkonen. 2021. Pose Estimation of Hurdles Athletes using OpenPose. In 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE, Mauritius, Mauritius, 1--6.Google ScholarCross Ref
- Taha Khan, Lina E Lundgren, Eric Järpe, M Charlotte Olsson, and Pelle Viberg. 2019. A novel method for classification of running fatigue using change-point segmentation. Sensors 19, 21 (2019), 4729.Google ScholarCross Ref
- Qiuqiang Kong, Yin Cao, Turab Iqbal, Yuxuan Wang, Wenwu Wang, and Mark D Plumbley. 2020. Panns: Large-scale pretrained audio neural networks for audio pattern recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020), 2880--2894.Google ScholarDigital Library
- L. I. Lin. 1989. A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics 45, 1 (March 1989), 255--268.Google ScholarCross Ref
- Shane Malone, Adam Owen, Matt Newton, Bruno Mendes, Leo Tiernan, Brian Hughes, and Kieran Collins. 2018. Wellbeing perception and the impact on external training output among elite soccer players. Journal of Science and Medicine in Sport 21, 1 (2018), 29--34.Google ScholarCross Ref
- Laura Mandolesi, Arianna Polverino, Simone Montuori, Francesca Foti, Giampaolo Ferraioli, Pierpaolo Sorrentino, and Giuseppe Sorrentino. 2018. Effects of physical exercise on cognitive functioning and wellbeing: biological and psychological benefits. Frontiers in psychology 9 (2018), 509.Google Scholar
- Marcus Nolte, Nikita Kister, and Markus Maurer. 2018. Assessment of deep convolutional neural networks for road surface classification. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, Maui, HI, USA, 381--386.Google ScholarDigital Library
- Raqib Omer and Liping Fu. 2010. An automatic image recognition system for winter road surface condition classification. In 13th international IEEE conference on intelligent transportation systems. IEEE, Funchal, Portugal, 1375--1379.Google ScholarCross Ref
- Tim Op De Beéck, Wannes Meert, Kurt Schütte, Benedicte Vanwanseele, and Jesse Davis. 2018. Fatigue prediction in outdoor runners via machine learning and sensor fusion. In Proc. of the ACM-SIGKDD International Conference on Knowledge Discovery & Data Mining. Association for Computing Machinery, London, UK, 606--615.Google Scholar
- Helge Rhodin, Jörg Spörri, Isinsu Katircioglu, Victor Constantin, Frédéric Meyer, Erich Müller, Mathieu Salzmann, and Pascal Fua. 2018. Learning monocular 3d human pose estimation from multi-view images. In Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Salt Lake City, UT, USA, 8437--8446.Google ScholarCross Ref
- Pedro Rodrigues, R. Chang, Trampas Tenbroek, Richard Emmerik, and Joseph Hamill. 2014. Evaluating the Coupling Between Foot Pronation and Tibial Internal Rotation Continuously Using Vector Coding. Journal of applied biomechanics 31 (11 2014). https://doi.org/10.1123/jab.2014-006Google Scholar
- Ognjen Rudovic, Jaeryoung Lee, Miles Dai, Björn Schuller, and Rosalind W Picard. 2018. Personalized machine learning for robot perception of affect and engagement in autism therapy. Science Robotics 3, 19 (2018), 1--11.Google ScholarCross Ref
- Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and LiangChieh Chen. 2018. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Salt Lake City, UT, USA, 4510--4520.Google ScholarCross Ref
- Dominik Schuldhaus, Patrick Kugler, Ulf Jensen, Bjoern Eskofier, Heiko Schlarb, and Magnus Leible. 2012. Classification of surfaces and inclinations during outdoor running using shoe-mounted inertial sensors. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012). IEEE, Tsukuba, Japan, 2258--2261.Google Scholar
- Kurt H Schütte, Stefan Seerden, Rachel Venter, and Benedicte Vanwanseele. 2016. Fatigue-related asymmetry and instability during a 3200-m time-trial performance in healthy runners. In ISBS-Conference Proceedings Archive. ISBS, Tsukuba, Japan.Google Scholar
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).Google Scholar
- Christina Strohrmann, Holger Harms, Cornelia Kappeler-Setz, and Gerhard Troster. 2012. Monitoring kinematic changes with fatigue in running using body-worn sensors. IEEE transactions on information technology in biomedicine 16, 5 (2012), 983--990.Google ScholarDigital Library
- Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi. 2017. Inception-v4, inception-resnet and the impact of residual connections on learning. In Thirty-first AAAI conference on artificial intelligence.Google ScholarDigital Library
- Andreas Triantafyllopoulos, Sandra Ottl, Alexander Gebhard, Esther RituertoGonzález, Mirko Jaumann, Steffen Hüttner, Valerie Dieter, Patrick Schneeweiß, Inga Krauß, Maurice Gerczuk, Shahin Amiriparian, and Björn W. Schuller. 2022. Fatigue Prediction in Outdoor Running Conditions using Audio Data. Accepted for publication to EMBC, arXiv preprint arXiv:2205.04343 (2022).Google Scholar
- Abhinav Valada and Wolfram Burgard. 2017. Deep spatiotemporal models for robust proprioceptive terrain classification. The International Journal of Robotics Research 36, 13--14 (2017), 1521--1539.Google ScholarDigital Library
- Matthew TO Worsey, Hugo G Espinosa, Jonathan B Shepherd, and David V Thiel. 2021. Automatic classification of running surfaces using an ankle-worn inertial sensor. Sports Engineering 24, 1 (2021), 1--15.Google ScholarCross Ref
- Dan Zecha, Moritz Einfalt, Christian Eggert, and Rainer Lienhart. 2018. Kinematic pose rectification for performance analysis and retrieval in sports. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. IEEE, Salt Lake City, UT, USA, 1791--1799.Google ScholarCross Ref
- Dan Zecha, Moritz Einfalt, and Rainer Lienhart. 2019. Refining joint locations for human pose tracking in sports videos. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. IEEE, Long Beach, CA, USA, 0--0.Google ScholarCross Ref
- Markus Zrenner, Christoph Feldner, Ulf Jensen, Nils Roth, Robert Richer, and Bjoern M Eskofier. 2019. Evaluation of Foot Kinematics During Endurance Running on Different Surfaces in Real-World Environments. In International Symposium on Computer Science in Sport. Springer, Cham, 106--113Google Scholar
Index Terms
- Improving Exertion and Wellbeing Prediction in Outdoor Running Conditions using Audio-based Surface Recognition
Recommendations
Prediction of lower-limb joint kinematics from surface EMG during overground locomotion
2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)Recent advancements in powered lower limb prostheses have led to the development of neural-machine interfaces for natural control during bipedal locomotion. In particular, electromyography (EMG) patterns recorded from the amputated limb can be leveraged ...
Classification of Upper limb phantom movements in transhumeral amputees using electromyographic and kinematic features
Recent studies have shown the ability of transhumeral amputees to generate surface electromyography (sEMG) patterns associated to distinct phantom limb movements of the hand, wrist and elbow. This ability could improve the control of myoelectric ...
Personalized Knee Angle Prediction Models Using Machine Learning
IC3-2022: Proceedings of the 2022 Fourteenth International Conference on Contemporary ComputingGait analysis had been traditionally used to diagnose underlying pathological conditions, but recently it has seen widespread applications in varied fields like bio-metrics, rehabilitation, sports, animation, etc. This study focuses on the ...
Comments