Abstract
In this chapter we present a case study on drinking gesture recognition from a dataset annotated by Experience Sampling (ES). The dataset contains 8825 “sensor events” and users reported 1808 “drink events” through experience sampling. We first show that the annotations obtained through ES do not reflect accurately true drinking events. We present then how we maximise the value of this dataset through two approaches aiming at improving the quality of the annotations post-hoc. Based on the work presented in Ciliberto et al. (2018), we extend the application of template-matching (Warping Longest Common Subsequence) to multiple sensor channels in order to spot a subset of events which are highly likely to be drinking gestures. We then propose an unsupervised approach which can perform drinking gesture recognition by combining K-Means clustering with WLCSS. Experimental results verify the effectiveness of the proposed method, with an improvement of the F1 score by 16% compared to standard K-Means using Euclidean distance.
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References
Bulling A et al (2014) A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput Surv (3):1–33
Ciliberto M, Wang L, Roggen D, Zillmer R (2018) A case study for human gesture recognition from poorly annotated data. In: Proceedings of the 2018 ACM international joint conference and 2018 international symposium on pervasive and ubiquitous computing and wearable computers. ACM, pp 1434–1443
Duffy W et al (2018) Addressing the problem of activity recognition with experience sampling and weak learning. In: Proceedings of SAI intelligent systems conference, pp 1–6
Gjoreski H, Roggen D (2017) Unsupervised online activity discovery using temporal behaviour assumption. In: Proceedings of the ACM international symposium on wearable computers, pp 42–49
Hartigan JA, Wong MA (1979) Algorithm AS 136: A k-means clustering algorithm. J R Stat Society Ser C (Appl Stat) (1):100–108
Kwon Y et al (2014) Unsupervised learning for human activity recognition using smartphone sensors. Expert Syst Appl (14):6067–6074
Michalewicz Z (1996) Evolution strategies and other methods. Genetic algorithms + data structures = evolution programs. Springer, Berlin, Heidelberg, pp 159–177
Mitra S et al (2007) Gesture recognition: A survey. IEEE Trans Syst Man Cybern Part C: Appl Rev (3):311–324
Nguyen-Dinh LV et al (2012) Improving online gesture recognition with template matching methods in accelerometer data. In: International conference on intelligent systems design and applications, pp 831–836
Nguyen-Dinh LV et al (2014) Robust online gesture recognition with crowdsourced annotations. J Mach Learn Res 3187–3220
Nguyen-Dinh LV et al (2017) Supporting One-Time Point Annotations for Gesture Recognition. IEEE Trans Pattern Anal Mach Intell (11):2270. http://ieeexplore.ieee.org/document/7778186/
Roggen D et al (2010) Collecting complex activity datasets in highly rich networked sensor environments. In: Proceedings of international conference on networked sensing systems, pp 233–240
Stikic M et al (2008) Exploring semi-supervised and active learning for activity recognition. In: IEEE international symposium on wearable computers, pp 81–88
Stikic M et al (2009) Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning. In: International symposium on location- and context-awareness. IEEE, pp 156–173
Zhang X et al (2011) A framework for hand gesture recognition based on accelerometer and EMG sensors. IEEE Trans Syst Man Cybern Part A: Syst Humans (6):1064–1076
Zillmer R et al (2014) A robust device for large-scale monitoring of bar soap usage in free-living conditions. Pers Ubiquitous Comput (8):2057–2064
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Ciliberto, M., Wang, L., Roggen, D., Zillmer, R. (2019). Drinking Gesture Recognition from Poorly Annotated Data: A Case Study. In: Kawaguchi, N., Nishio, N., Roggen, D., Inoue, S., Pirttikangas, S., Van Laerhoven, K. (eds) Human Activity Sensing. Springer Series in Adaptive Environments. Springer, Cham. https://doi.org/10.1007/978-3-030-13001-5_6
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DOI: https://doi.org/10.1007/978-3-030-13001-5_6
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