Johann P. Wolff
ABSTRACT
Human activity recognition using inertial sensors is an increasingly used feature in smartphones or smartwatches, providing information on sports and physical activities of each individual. But while the position a smartphone is worn in varies between persons and circumstances, a smartwatch moves constantly, in rhythm with its user's arms. Both problems make activity recognition less reliable. Attaching an inertial sensor to the head provides reliable information on the movements of the whole body while not being superimposed by many additional movements. This can be achieved by fixing sensors to glasses, helmets, or headphones. In this paper, we present a system using head-mounted inertial sensors for human activity recognition. We compare it to existing research work and show possible advantages or disadvantages of positioning a single sensor on the head to recognize physical activities. Furthermore we evaluate the benefits of using different sensor configurations on activity recognition.
- Mohammad Abu Alsheikh, Ahmed Selim, Dusit Niyato, Linda Doyle, Shaowei Lin, and Hwee-Pink Tan. 2016. Deep Activity Recognition Models with Triaxial Accelerometers.. In AAAI Workshop: Artificial Intelligence Applied to Assistive Technologies and Smart Environments.Google Scholar
- Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge Luis Reyes-Ortiz. 2013. A Public Domain Dataset for Human Activity Recognition using Smartphones.. In ESANN.Google Scholar
- Apple Inc. 2015. (2015). http://www.freepatentsonline.com/y2017/0078785.html, accessed 2018-06-01.Google Scholar
- Apple Inc. 2015. (2015). http://www.freepatentsonline.com/y2017/0078781.html, accessed 2018-06-01.Google Scholar
- Apple Inc. 2015. (2015). http://www.freepatentsonline.com/y2017/0078780.html, accessed 2018-06-01.Google Scholar
- Apple Inc. 2015. (2015). http://www.freepatentsonline.com/9497534.html, accessed 2018-06-01.Google Scholar
- Akin Avci, Stephan Bosch, Mihai Marin-Perianu, Raluca Marin-Perianu, and Paul Havinga. 2010. Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey. In Architecture of computing systems (ARCS), 2010 23rd international conference on. VDE, 1--10.Google Scholar
- Ling Bao and Stephen Intille. 2004. Activity recognition from user-annotated acceleration data. Pervasive computing (2004), 1--17.Google Scholar
- Bragi GmbH. 2018. The Dash - wireless ear-buds. https://www.bragi.com/thedashpro/, accessed 2018-06-01. (2018).Google Scholar
- Andreas Bulling, Ulf Blanke, and Bernt Schiele. 2014. A tutorial on human activity recognition using body-worn inertial sensors. ACM Computing Surveys (CSUR) 46, 3 (2014), 33. Google ScholarDigital Library
- Susannah Fox and Maeve Duggan. 2013. Part Three: Tracking for Health. http://www.pewinternet.org/2013/11/26/part-three-tracking-for-health/, accessed 2018-06-01. (2013).Google Scholar
- Korbinian Frank, Vera Nadales, María Josefa, Patrick Robertson, and Michael Angermann. 2010. Reliable real-time recognition of motion related human activities using MEMS inertial sensors. (2010).Google Scholar
- GitHub (Aqib Saeed). 2016. http://aqibsaeed.github.io/2016-11-04-human-activity-recognition-cnn/, accessed 2018-06-01. (2016).Google Scholar
- Google. 2018. TensorFlow. https://www.tensorflow.org/, accessed 2018-06-01. (2018).Google Scholar
- Nils Y Hammerla, Shane Halloran, and Thomas Ploetz. 2016. Deep, convolutional, and recurrent models for human activity recognition using wearables. arXiv preprint arXiv:1604.08880 (2016). Google ScholarDigital Library
- Ernst A Heinz, Kai S Kunze, Matthias Gruber, David Bannach, and Paul Lukowicz. 2006. Using wearable sensors for real-time recognition tasks in games of martial arts-an initial experiment. In Computational Intelligence and Games, 2006 IEEE Symposium on. IEEE, 98--102.Google ScholarCross Ref
- Shoya Ishimaru, Kai Kunze, Koichi Kise, Jens Weppner, Andreas Den-gel, Paul Lukowicz, and Andreas Bulling. 2014. In the blink of an eye: combining head motion and eye blink frequency for activity recognition with Google Glass. In Proceedings of the 5th augmented human international conference. ACM, 15. Google ScholarDigital Library
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105. Google ScholarDigital Library
- Oscar D Lara and Miguel A Labrador. 2013. A survey on human activity recognition using wearable sensors. IEEE Communications Surveys and Tutorials 15, 3 (2013), 1192--1209.Google ScholarCross Ref
- Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature 521, 7553 (2015), 436.Google Scholar
- John Mannes. 2017. GoogleâĂrŹs TensorFlow Lite brings machine learning to Android devices. https://techcrunch.com/2017/05/17/googles-tensorflow-lite-brings-machine-learning-to-android-devices/, accessed 2018-06-01. (2017).Google Scholar
- MJ Mathie, ACF Coster, NH Lovell, and BG Celler. 2003. Detection of daily physical activities using a triaxial accelerometer. Medical and Biological Engineering and Computing 41, 3 (2003), 296--301.Google ScholarCross Ref
- MJ Mathie, Nigel H Lovell, ACF Coster, and BG Celler. 2002. Determining activity using a triaxial accelerometer. In Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint, Vol. 3. IEEE, 2481--2482.Google ScholarCross Ref
- Nishkam Ravi, Nikhil Dandekar, Preetham Mysore, and Michael L Littman. 2005. Activity recognition from accelerometer data. In Aaai, Vol. 5. 1541--1546. Google ScholarDigital Library
- Charissa Ann Ronao and Sung-Bae Cho. 2015. Deep convolutional neural networks for human activity recognition with smartphone sensors. In International Conference on Neural Information Processing. Springer, 46--53.Google ScholarCross Ref
- Charissa Ann Ronao and Sung-Bae Cho. 2016. Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications 59 (2016), 235--244. Google ScholarDigital Library
- Brittany Sauser. 2007. A Helmet That Detects Hard Hits. https://www.technologyreview.com/s/408643/a-helmet-that-detects-hard-hits/, accessed 2018-06-01. (2007).Google Scholar
- Du Tran and Alexander Sorokin. 2008. Human activity recognition with metric learning. Computer Vision--ECCV 2008 (2008), 548--561. Google ScholarDigital Library
- Eduardo Velloso, Andreas Bulling, Hans Gellersen, Wallace Ugulino, and Hugo Fuks. 2013. Qualitative activity recognition of weight lifting exercises. In Proceedings of the 4th Augmented Human International Conference. ACM, 116--123. Google ScholarDigital Library
- X (Subsidiary of Alphabet Inc.). 2018. Google Glass. https://www.x.company/glass/, accessed 2018-06-01. (2018).Google Scholar
- Jianbo Yang, Minh Nhut Nguyen, Phyo Phyo San, Xiaoli Li, and Shonali Krishnaswamy. 2015. Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition.. In IJCAI. 3995--4001. Google ScholarDigital Library
- Douglas Yeung and Astrid Stuth Cevallos. 2017. Using Wearable Fitness Devices to Monitor More Than Just Fitness. https://blogs.scientificamerican.com/observations/using-wearable-fitness-devices-to-monitor-more-than-just-fitness/, accessed 2018-06-01. (2017).Google Scholar
- Ming Zeng, Le T Nguyen, Bo Yu, Ole J Mengshoel, Jiang Zhu, Pang Wu, and Joy Zhang. 2014. Convolutional neural networks for human activity recognition using mobile sensors. In Mobile Computing, Applications and Services (MobiCASE), 2014 6th International Conference on. IEEE, 197--205.Google Scholar
Index Terms
- Activity Recognition using Head Worn Inertial Sensors
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