A tutorial on human activity recognition using body-worn inertial sensors

Abstract

The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance.

References

1. Gregory D. Abowd, Anind K. Dey, R. Orr, and J. Brotherton. 1998. Context-awareness in wearable and ubiquitous computing. Virtual Reality 3, 3 (1998), 200--211.
2. J. K. Aggarwal and M. S. Ryoo. 2011. Human activity analysis: A review. Comput. Surveys 43, 3 (2011), 16:1--16:43. DOI: http://dx.doi.org/10.1145/1922649.1922653
3. Barbara E. Ainsworth, William L. Haskell, Stephen D. Herrmann, Nathanael Meckes, David R. Bassett, Catrine Tudor-Locke, Jennifer L. Greer, Jesse Vezina, Melicia C. Whitt-Glover, and Arthur S. Leon. 2011. 2011 compendium of physical activities: A second update of codes and MET values. Medicine and Science in Sports and Exercise 43, 8 (2011), 1575--1581.
4. Bashar Altakouri, Gerd Kortuem, Agnes Grunerbl, Kunze Kai, and Paul Lukowicz. 2010. The benefit of activity recognition for mobile phone based nursing documentation: A Wizard-of-Oz study. In Proceedings of ISWC. 1--4.
5. Oliver Amft. 2011. Self-taught learning for activity spotting in on-body motion sensor data. In Proceedings of ISWC 0 (2011), 83--86. DOI: http://dx.doi.org/10.1109/ISWC.2011.37
6. Oliver Amft, Holger Junker, and Gerhard Tröster. 2005. Detection of eating and drinking arm gestures using inertial body-worn sensors. In Proceedings of the IEEE International Symposium on Wearable Computing. 160--163.
7. Oliver Amft, Martin Kusserow, and Gerhard Tröster. 2007. Probabilistic parsing of dietary activity events. In Proceedings of BSN. Springer, 242--247.
8. Urs Anliker, Jamie A. Ward, Paul Lukowicz, Gerhard Tröster, François Dolveck, Michel Baer, Fatou Keita, Eran B. Schenker, Fabrizio Catarsi, Luca Coluccini, Andrea Belardinelli, Dror Shklarski, Menachem Alon, Etienne Hirt, Rolf Schmid, and Milica Vuskovic. 2004. AMON: A wearable multiparameter medical monitoring and alert system. IEEE Trans. Inf. Technol. Biomed. 8, 4 (2004), 415--427.
9. Daniel Ashbrook and Thad Starner. 2003. Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing 7, 5 (2003), 275--286.
10. Daniel Ashbrook and Thad Starner. 2010. MAGIC: A motion gesture design tool. In Proceedings of CHI. 2159--2168.
11. Marc Bächlin, Daniel Roggen, Meir Plotnik, Noit Inbar, Inbal Meidan, Talia Herman, Marina Brozgol, Eliya Shaviv, Nir Giladi, Jeffrey M Hausdorff, and Gerhard Tröster. 2009. Potentials of enhanced context awareness in wearable assistants for Parkinson's disease patients with freezing of gait syndrome. In Proceedings of ISWC. 123--130.
12. Ling Bao and Stephen S. Intille. 2004. Activity recognition from user-annotated acceleration data. In Proceedings of Pervasive. 1--17.
13. H. Bayati, J. d. R. Millán, and R. Chavarriaga. 2011. Unsupervised adaptation to on-body sensor displacement in acceleration-based activity recognition. In Proceedings of ISWC.
14. Ulf Blanke, Robert Rehner, and Bernt Schiele. 2011. South by South-East or sitting at the desk. Can orientation be a place? In Proceedings of ISWC.
15. Ulf Blanke and Bernt Schiele. 2008. Sensing location in the Pocket. In Adj. Proceedings of UbiComp.
16. Ulf Blanke and Bernt Schiele. 2009. Daily routine recognition through activity spotting. In Proceedings of LoCa. 192--206.
17. Ulf Blanke and Bernt Schiele. 2010. Remember and transfer what you have learned—recognizing composite activities based on activity spotting. In Proceedings of ISWC. 1--8.
18. Ulf Blanke, Bernt Schiele, Matthias Kreil, Paul Lukowicz, Bernard Sick, and Thiemo Gruber. 2010. All for one or one for all? Combining heterogeneous features for activity spotting. In Proceedings of the IEEE PerCom Workshop on Context Modeling and Reasoning. 18--24.
19. M. Buettner, R. Prasad, M. Philipose, and D. Wetherall. 2009. Recognizing daily activities with RFID-based sensors. In Proceedings of UbiComp. 51--60.
20. Andreas Bulling and Daniel Roggen. 2011. Recognition of visual memory recall processes using eye movement analysis. In Proceedings of the 13th International Conference on Ubiquitous Computing (UbiComp'11). ACM, 455--464. DOI: http://dx.doi.org/10.1145/2030112.2030172
21. Andreas Bulling, Jamie A. Ward, and Hans Gellersen. 2012. Multimodal recognition of reading activity in transit using body-worn sensors. ACM Transactions on Applied Perception 9, 1 (2012), 2:1--2:21. DOI: http://dx.doi.org/10.1145/2134203.2134205
22. Andreas Bulling, Jamie A. Ward, Hans Gellersen, and Gerhard Tröster. 2008. Robust recognition of reading activity in transit using wearable electrooculography. In Proceedings of the 6th International Conference on Pervasive Computing (Pervasive'08). Springer, 19--37. DOI: http://dx.doi.org/10.1007/978-3-540-79576-6_2
23. Andreas Bulling, Jamie A. Ward, Hans Gellersen, and Gerhard Tröster. 2009. Eye movement analysis for activity recognition. In Proceedings of the 11th International Conference on Ubiquitous Computing (UbiComp'09). ACM, 41--50. DOI: http://dx.doi.org/10.1145/1620545.1620552
24. Andreas Bulling, Jamie A. Ward, Hans Gellersen, and Gerhard Tröster. 2011. Eye movement analysis for activity recognition using electrooculography. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 4 (April 2011), 741--753. DOI: http://dx.doi.org/10.1109/TPAMI.2010.86
25. Andreas Bulling, Christian Weichel, and Hans Gellersen. 2013. EyeContext: Recognition of high-level contextual cues from human visual behaviour. In Proceedings of the 31st SIGCHI International Conference on Human Factors in Computing Systems. 305--308. DOI: http://dx.doi.org/10.1145/2470654.2470697
26. A. Cassinelli, C. Reynolds, and M. Ishikawa. 2006. Augmenting spatial awareness with haptic radar. In Proceedings of ISWC. 61--64.
27. J. Chen, K. Kwong, D. Chang, J. Luk, and R. Bajcsy. 2005. Wearable sensors for reliable fall detection. In Proceedings of the 27th IEEE International Conference of Engineering in Medicine and Biology. 3551--3554.
28. Jingyuan Cheng, Oliver Amft, and Paul Lukowicz. 2010. Active capacitive sensing: Exploring a new wearable sensing modality for activity recognition. In Proceedings of Pervasive. 319--336.
29. B. Clarkson, K. Mase, and A. Pentland. 2000. Recognizing user's context from wearable sensors: Baseline system. In Proceedings of ISWC. 69--76.
30. B. Clarkson and A. Pentland. 1999. Unsupervised clustering of ambulatory audio and video. In Proceedings of ASSP. 3037--3040.
31. I. Cohen and M. Goldszmidt. 2004. Properties and benefits of calibrated classifiers. In Proceedings of the International Conference on Knowledge Discovery in Databases. 125--136.
32. R. de Oliveira, M. Cherubini, and N. Oliver. 2010. MoviPill: Improving medication compliance for elders using a mobile persuasive social game. In Proceedings of UbiComp, Vol. 1001. 36.
33. M. Everingham and J. Winn. 2007. The PASCAL Visual Object Classes Challenge 2007 Development Kit. Technical Report.
34. Tom Fawcett. 2006. An introduction to ROC analysis. Pattern Recognition Letters 27, 8 (2006), 861--874. DOI: http://dx.doi.org/DOI: 10.1016/j.patrec.2005.10.010
35. Davide Figo, Pedro Diniz, Diogo Ferreira, and João Cardoso. 2010. Preprocessing techniques for context recognition from accelerometer data. Personal and Ubiquitous Computing 14, 7 (2010), 645--662.
36. Gernot A. Fink. 2008. Markov Models for Pattern Recognition: From Theory to Applications. Springer.
37. J. Friedman, T. Hastie, and R. Tibshirani. 2000. Additive logistic regression: a statistical view of boosting. Annals of Statistics 28, 2 (2000), 337--407.
38. A. Godfrey, R. Conway, D. Meagher, and G. ÓLaighin. 2008. Direct measurement of human movement by accelerometry. Medical Engineering and Physics 30, 10 (2008), 1364--1386.
39. Eric Guenterberg, Sarah Ostadabbas, Hassan Ghasemzadeh, and Roozbeh Jafari. 2009. An automatic segmentation technique in body sensor networks based on signal energy. In Proceedings of BAN. 21:1--21:7. DOI: http://dx.doi.org/10.4108/ICST.BODYNETS2009.6036
40. Isabelle Guyon and André Elisseeff. 2003. An introduction to variable and feature selection. Journal of Machine Learning Research 3 (2003), 1157--1182.
41. Björn Hartmann, Leith Abdulla, Manas Mittal, and Scott R. Klemmer. 2007. Authoring sensor-based interactions by demonstration with direct manipulation and pattern recognition. In Proceedings of CHI. 145--154.
42. T. Ho, J. Hull, and S. Srihari. 1994. Decision combination in multiple classifier systems. IEEE Trans. on Pattern Analysis and Machine Intelligence 16 (1994), 66--75.
43. T. Huynh, U. Blanke, and B. Schiele. 2007. Scalable recognition of daily activities with wearable sensors. In Proceedings of LoCa. 50--67.
44. Tâm Huynh, Mario Fritz, and Bernt Schiele. 2008. Discovery of activity patterns using topic models. In Proceedings of UbiComp. 10--19.
45. Tam Huynh and Bernt Schiele. 2005. Analyzing features for activity recognition. In Proceedings of the Joint Conference on Smart Objects and Ambient Intelligence. 159--163. http://dx.doi.org/10.1145/1107548.1107591
46. Wen-Juh Kang, Jiue-Rou Shiu, Cheng-Kung Cheng, Jin-Shin Lai, Hen-Wai Tsao, and Te-Son Kuo. 1995. The application of cepstral coefficients and maximum likelihood method in EMG pattern recognition. IEEE Trans. on Biomedical Engineering 42, 8 (1995), 777--785.
47. Ashish Kapoor and Eric Horvitz. 2008. Experience sampling for building predictive user models: A comparative study. In Proceedings of CHI. 657--666. DOI: http://dx.doi.org/10.1145/1357054.1357159
48. S. Katz, T. D. Downs, H. R. Cash, and R. C. Grotz. 1970. Progress in development of the index of ADL. The Gerontologist 10, 1 Part 1 (1970), 20.
49. J. Kittler et al. 1998. On combining classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence 20, 3 (1998), 226--239.
50. Ron Kohavi and George H. John. 1997. Wrappers for feature subset selection. Artificial Intelligence 97, 1--2 (1997), 273--324.
51. Matthias Kranz, Andreas Möller, Nils Hammerla, Stefan Diewald, Thomas Plötz, Patrick Olivier, and Luis Roalter. 2013. The mobile fitness coach: Towards individualized skill assessment using personalized mobile devices. Pervasive and Mobile Computing 9, 2 (2013), 203--215.
52. J. Krumm and E. Horvitz. 2006. Predestination: Inferring destinations from partial trajectories. In Proceedings of UbiComp. 243--260.
53. K. Kunze, M. Barry, E.A. Heinz, P. Lukowicz, D. Majoe, and J. Gutknecht. 2006. Towards recognizing tai chi—An initial experiment using wearable sensors. Proceedings of FAWC (2006), 1--6.
54. Kai Kunze and Paul Lukowicz. 2008. Dealing with sensor displacement in motion-based onbody activity recognition systems. In Proceedings of UbiComp. 20--29.
55. K. Kunze, P. Lukowicz, H. Junker, and G. Tröster. 2005. Where am I: Recognizing on-body positions of wearable sensors. In Proceedings of the International Workshop on Location and Context-Awareness. 257--268.
56. Cassim Ladha, Nils Hammerla, Patrick Olivier, and Thomas Plötz. 2013. ClimbAX: Skill assessment for climbing enthusiasts. In Proceedings of the Int. Conf. Ubiquitous Comp. (UbiComp). to appear.
57. C. Lee and Y. Xu. 1996. Online, interactive learning of gestures for human/robot interfaces. In Proceedings of the IEEE International Conference on Robotics and Automation. 2982--2987.
58. S. W. Lee and K. Mase. 2002. Activity and location recognition using wearable sensors. IEEE Pervasive Computing 1, 3 (2002), 24--32.
59. Jonathan Lester, Tanzeem Choudhury, and Gaetano Borriello. 2006. A practical approach to recognizing physical activities. In Proceedings of the International Conference on Pervasive Computing. 1--16.
60. Jonathan Lester, Tanzeem Choudhury, Nicky Kern, Gaetano Borriello, and Blake Hannaford. 2005. A hybrid discriminative/generative approach for modeling human activities. In Proceedings of the 19th International Joint Conference on Artificial Intelligence. 766--772.
61. Lin Liao, Dieter Fox, and Henry Kautz. 2005. Location-based activity recognition using relational Markov networks. In Proceedings of the 19th International Joint Conference on Artificial Intelligence. 773--778.
62. Charles X. Ling, Jin Huang, and Harry Zhang. 2003. AUC: A statistically consistent and more discriminating measure than accuracy. In Proceedings of the 18th International Conference on Artificial Intelligence. 329--341.
63. B. Logan, J. Healey, M. Philipose, E.M. Tapia, and S. Intille. 2007. A long-term evaluation of sensing modalities for activity recognition. In Proceedings of UbiComp. Springer-Verlag, 483--500.
64. Hong Lu, Wei Pan, Nicholas D. Lane, Tanzeem Choudhury, and Andrew T. Campbell. 2009. SoundSense: scalable sound sensing for people-centric applications on mobile phones. In Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services. 165--178. DOI: http://dx.doi.org/10.1145/1555816.1555834
65. Hong Lu, Jun Yang, Zhigang Liu, Nicholas D. Lane, Tanzeem Choudhury, and Andrew T. Campbell. 2010. The Jigsaw continuous sensing engine for mobile phone applications. In Proceedings of ENSS. ACM, 71--84.
66. P. Lukowicz, F. Hanser, C. Szubski, and W. Schobersberger. 2006. Detecting and interpreting muscle activity with wearable force sensors. In Proceedings of Pervasive. 101--116.
67. C. Mattmann, O. Amft, H. Harms, G. Tröster, and F. Clemens. 2007. Recognizing upper body postures using textile strain sensors. In Proceedings of ISWC. 29--36.
68. U. Maurer, A. Smailagic, D. P. Siewiorek, and M. Deisher. 2006. Activity recognition and monitoring using multiple sensors on different body positions. In Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks. 113--116.
69. I. Maurtua, P. T. Kirisci, T. Stiefmeier, M. L. Sbodio, and H. Witt. 2007. A wearable computing prototype for supporting training activities in automative production. In Proceedings of the 4th International Forum on Applied Wearable Computing. 1--12.
70. David Minnen, Thad Starner, Irfan Essa, and Charles Isbell. 2006a. Discovering characteristic sctions from on-body sensor data. In Proceedings of the 10th IEEE International Symposium on Wearable Computers (ISWC).
71. D. Minnen, T. Westeyn, T. Starner, J. Ward, and P. Lukowicz. 2006b. Performance metrics and evaluation issues for continuous activity recognition. In Performance Metrics for Intelligent Systems.
72. Sushmita Mitra and Tinku Acharya. 2007. Gesture recognition: A survey. IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Reviews 37, 3 (2007), 311--324.
73. S. J. Morris and J. Paradiso. 2002. Shoe-integrated sensor system for wireless gait analysis and real-time feedback. In Proceedings of the 2nd Joint IEEE EMBS and BMES Conference. 2468--2469.
74. G. Ogris, T. Stiefmeier, P. Lukowicz, and G. Tröster. 2008. Using a complex multi-modal on-body sensor system for activity spotting. In Proceedings of ISWC. 55--62.
75. N. Oliver and F. Flores-Mangas. 2007. HealthGear: Automatic sleep apnea detection and monitoring with a mobile phone. Journal of Communications 2, 2 (2007), 1--9.
76. K. Partridge and P. Golle. 2008. On using existing time-use study data for ubiquitous computing applications. In Proceedings of UbiComp. ACM, 144--153.
77. D. Patterson, D. Fox, H. Kautz, and M. Philipose. 2005. Fine-grained activity recognition by aggregating abstract object usage. In Proceedings of ISWC. 44--51.
78. Donald Patterson and Mohan Singh. 2010. Involuntary gesture recognition for predicting cerebral palsy in high-risk infants. In Proceedings of ISWC.
79. Hanchuan Peng, Fuhui Long, and C. Ding. 2005. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. on Pattern Analysis and Machine Intelligence 27, 8 (2005), 1226--1238.
80. A. S. Pentland. 2004. Healthwear: Medical technology becomes wearable. Computer (2004), 42--49.
81. M. Philipose, K. P. Fishkin, M. Perkowitz, D. J. Patterson, D. Fox, H. Kautz, and D. Hahnel. 2004. Inferring activities from interactions with objects. IEEE Pervasive Computing (2004), 50--57.
82. G. Pirkl, K. Stockinger, K. Kunze, and P. Lukowicz. 2008. Adapting magnetic resonant coupling based relative positioning technology for wearable activity recognition. In Proceedings of ISWC. 47--54.
83. Thomas Plötz, Nils Y Hammerla, and Patrick Olivier. 2011. Feature learning for activity recognition in ubiquitous computing. In Proceedings of the 22nd international Joint Conference on Artificial Intelligence. AAAI Press, 1729--1734.
84. Thomas Plötz, Nils Y. Hammerla, Agata Rozga, Andrea Reavis, Nathan Call, and Gregory D. Abowd. 2012. Automatic assessment of problem behavior in individuals with developmental disabilities. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing. 391--400. http://dx.doi.org/10.1145/2370216.2370276
85. R. Polikar. 2006. Ensemble Based Systems in Decision Making. IEEE Circuits and Systems Magazine 6, 3 (2006), 21--45.
86. L. R. Rabiner. 1989. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of IEEE 77, 2 (1989), 257--285.
87. C. Randell and H. Muller. 2000. Context awareness by analysing accelerometer data. In Proceedings of ISWC. 175--176.
88. Nishkam Ravi, Nikhil Dandekar, Preetham Mysore, and Michael L. Littman. 2005. Activity recognition from accelerometer data. In Proceedings of the 17th International Conference on Innovative Applications of Artificial Intelligence. 1541--1546.
89. D. Roggen, M. Baechlin, J. Schumm, T. Holleczek, C. Lombriser, G. Tröster, L. Widmer, D. Majoe, and J. Gutknecht. 2010. An educational and research kit for activity and context recognition from on-body sensors. In Proceedings of BSN. 277--282.
90. D. Roggen, K. Förster, A. Calatroni, T. Holleczek, Yu Fang, G. Tröster, P. Lukowicz, G. Pirkl, D. Bannach, K. Kunze, A. Ferscha, C. Holzmann, A. Riener, R. Chavarriaga, and J. del R. Millan. 2009. OPPORTUNITY: Towards opportunistic activity and context recognition systems. In Proceedings of Wowmom. 1--6.
91. Daniel Roggen, Kilian Förster, Alberto Calatroni, and Gerhard Tröster. 2013. The adARC pattern analysis architecture for adaptive human activity recognition systems. Journal of Ambient Intelligence and Humanized Computing 4, 2 (2013), 169--186. DOI: http://dx.doi.org/10.1007/s12652-011-0064-0
92. G. Schindler, C. Metzger, and T. Starner. 2006. A wearable interface for topological mapping and localization in indoor environments. Proceedings of LoCa (2006), 64--73.
93. Petr Somol, Jana Novovičová, and Pavel Pudil. 2006. Flexible-Hybrid Sequential Floating Search in Statistical Feature Selection. In Structural, Syntactic, and Statistical Pattern Recognition. 632--639.
94. T. Starner, J. Weaver, and A. Pentland. 1997. A wearable computer-based American sign language recogniser. Personal and Ubiquitous Computing 1, 4 (1997), 241--250.
95. Thomas Stiefmeier, Daniel Roggen, Georg Ogris, Paul Lukowicz, and Gerhard Tröster. 2008. Wearable activity tracking in car manufacturing. IEEE Pervasive Computing 7, 2 (2008), 42--50.
96. Thomas Stiefmeier, Daniel Roggen, and Gerhard Tröster. 2007. Gestures are strings: efficient online gesture spotting and classification using string matching. In Proceedings of the 2nd International Conference on Body Area Networks. 1--8.
97. M. Stikic, T. Huynh, K. van Laerhoven, and B. Schiele. 2008. ADL recognition based on the combination of RFID and accelerometer sensing. In Proceedings of PervasiveHealth. 258--263.
98. Maja Stikic, Diane Larlus, Sandra Ebert, and Bernt Schiele. 2011. Weakly supervised recognition of daily life activities with wearable sensors. IEEE Trans. on Pattern Analysis and Machine Intelligence (2011).
99. J. Sung, C. Ponce, B. Selman, and A. Saxena. 2011. Human activity detection from RGBD images. In Proceedings of the AAAI Workshop on Plan, Activity, and Intent Recognition.
100. M. Sung, C. Marci, and A. Pentland. 2005. Wearable feedback systems for rehabilitation. Journal of NeuroEngineering and Rehabilitation 2, 1 (2005).
101. E. Munguia Tapia, S. S. Intille, and K. Larson. 2004. Activity recognition in the home using simple and ubiquitous sensors. In Proceedings of PERVASIVE. 158--175.
102. Bernd Tessendorf, Andreas Bulling, Daniel Roggen, Thomas Stiefmeier, Manuela Feilner, Peter Derleth, and Gerhard Tröster. 2011a. Recognition of hearing needs from body and eye movements to improve hearing instruments. In Proceedings of the 9th International Conference on Pervasive Computing. Springer, 314--331. DOI: http://dx.doi.org/10.1007/978-3-642-21726-5_20
103. Bernd Tessendorf, Franz Gravenhorst, Bert Arnrich, and Gerhard Tröster. 2011b. An IMU-based sensor network to continuously monitor rowing technique on the water. In Proceedings of ISSNIP.
104. Antonio Torralba, Kevin P. Murphy, and William T. Freeman. 2007. Sharing visual features for multiclass and multiview object detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 29, 5 (2007), 854--869.
105. P. Turaga, R. Chellappa, V. S. Subrahmanian, and O. Udrea. 2008. Machine recognition of human activities: A survey. IEEE Trans. on Circuits and Systems for Video Technology 18, 11 (2008), 1473--1488.
106. T. L. M. van Kasteren, G. Englebienne, and B. J. A. Kröse. 2010. Transferring knowledge of activity recognition across sensor networks. Proceedings of the Pervasive (2010), 283--300.
107. T. van Kasteren, A. Noulas, G. Englebienne, and B. Kröse. 2008. Accurate activity recognition in a home setting. In Proceedings of UbiComp. 1--9.
108. Kristof Van Laerhoven and Eugen Berlin. 2009. When else did this happen? Efficient subsequence representation and matching for wearable activity data. In Proceedings of ISWC. IEEE Press, 69--77.
109. K. van Laerhoven and O. Cakmakci. 2000. What shall we teach our pants. In Proceedings of ISWC. 77--83.
110. Kristof van Laerhoven, David Kilian, and Bernt Schiele. 2008. Using rhythm awareness in long-term activity recognition. In Proceedings of ISWC. 63--68.
111. K. van Laerhoven, A. Schmidt, and H.-W. Gellersen. 2002. Multi-sensor context aware clothing. In Proceedings of ISWC. 49--56.
112. Eduardo Velloso, Andreas Bulling, and Hans Gellersen. 2013a. MotionMA: Motion modelling and analysis by demonstration. In Proceedings of the 31st SIGCHI International Conference on Human Factors in Computing Systems. 1309--1318. DOI: http://dx.doi.org/10.1145/2470654.2466171
113. Eduardo Velloso, Andreas Bulling, Hans Gellersen, Wallace Ugulino, and Hugo Fuks. 2013b. Qualitative activity recognition of weight lifting exercises. In Proceedings of the 4th Augmented Human International Conference (AugmentedHuman 2013). 116--123. DOI: http://dx.doi.org/10.1145/2459236.2459256
114. D. Wan. 1999. Magic medicine cabinet: A situated portal for consumer healthcare. In Handheld and Ubiquitous Computing. Springer, 352--355.
115. S. Wang, W. Pentney, A.M. Popescu, T. Choudhury, and M. Philipose. 2007. Common sense based joint training of human activity recognizers. In Proceedings of the 20th International Joint Conference on Artificial Intelligence. 2237--2242.
116. Jamie A. Ward, Paul Lukowicz, and Hans W. Gellersen. 2011. Performance metrics for activity recognition. ACM Trans. on Intelligent Systems and Technology 2, 1 (2011), 6:1--6:23. http://dx.doi.org/10.1145/1889681.1889687
117. Jamie A. Ward, Paul Lukowicz, Gerhard Tröster, and Thad E. Starner. 2006. Activity recognition of assembly tasks using body-worn microphones and accelerometers. IEEE Trans. on Pattern Analysis and Machine Intelligence 28, 10 (2006), 1553--1567.
118. Tracy Westeyn, Kristin Vadas, Xuehai Bian, Thad Starner, and Gregory D. Abowd. 2005. Recognizing mimicked autistic self-stimulatory behaviors using HMMs. In Proceedings of ISWC. 164--169.
119. Andrew D. Wilson and Aaron F. Bobick. 2000. Realtime online adaptive gesture recognition. In Proceedings of the 15th International Conference on Pattern Recognition. 270--275. DOI: http://dx.doi.org/10.1109/ICPR. 2000.905317
120. C. Wren, Y. Ivanov, I. Kaur, D. Leigh, and J. Westhues. 2007. Socialmotion: Measuring the hidden social life of a building. Proceedings of LoCa (2007), 85--102.
121. Zhixian Yan, Vigneshwaran Subbaraju, Dipanjan Chakraborty, Archan Misra, and Karl Aberer. 2012. Energy-efficient continuous activity recognition on mobile phones: An activity-adaptive approach. In Proceedings of ISWC. IEEE, 17--24.
122. P. Zappi, T. Stiefmeier, E. Farella, D. Roggen, L. Benini, and Tröster. 2007. Activity recognition from on-body sensors by classifier fusion: Sensor scalability and robustness. In Proceedings of ISSNIP. 281--286.
123. Mi Zhang and Alexander A. Sawchuk. 2012. Motion primitive-based human activity recognition using a bag-of-features approach. In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium. ACM, 631--640.
124. V. W. Zheng, D. H. Hu, and Q. Yang. 2009. Cross-domain activity recognition. In Proceedings of UbiComp. 61--70.
125. Andreas Zinnen, Ulf Blanke, and Bernt Schiele. 2009a. An analysis of sensor-oriented vs. model-based activity recognition. In Proceedings of ISWC.
126. Andreas Zinnen, Christian Wojek, and Bernt Schiele. 2009b. Multi activity recognition based on body model-derived primitives. In Proceedings of LoCa. DOI: http://dx.doi.org/10.1007/978-3-642-01721-6_1

Index Terms

• Computer systems organization

  • Embedded and cyber-physical systems

  • Real-time systems

• Computing methodologies

  • Machine learning