Abstract
Smartphones are widely available commercial devices and using them as a basis to creates the possibility of future widespread usage and potential applications. This paper utilizes the embedded sensors in a smartphone to recognise a number of common human actions and postures. We group the range of all possible human actions into five basic action classes, namely walking, standing, sitting, crouching and lying. We also consider the postures pertaining to three of the above actions, including standing postures (backward, straight, forward and bend), sitting postures (lean, upright, slouch and rest) and lying postures (back, side and stomach) . Training data was collected through a number of people performing a sequence of these actions and postures with a smartphone in their shirt pockets. We analysed and compared three classification algorithms, namely k Nearest Neighbour (kNN), Decision Tree Learning (DTL) and Linear Discriminant Analysis (LDA) in terms of classification accuracy and efficiency (training time as well as classification time). kNN performed the best overall compared to the other two and is believed to be the most appropriate classification algorithm to use for this task. The developed system is in the form of an Android app. Our system can real-time accesses the motion data from the three sensors and on-line classifies a particular action or posture using the kNN algorithm. It successfully recognizes the specified actions and postures with very high precision and recall values of generally above 96 %.
Keywords
- Shirt Pocket
- Decision Tree Learning (DTL)
- Linear Discriminant Analysis (LDA)
- Vertical Station Position
- Standing Position
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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- 1.
Our android APP is available for download at: https://drive.google.com/file/d/0Bwk_YqDcv7VsaEZySXoyN2ttM2c/view?usp=sharing.
- 2.
Video demo of our system available at: http://cs.adelaide.edu.au/~wenjie/HRAphone.mp4.
References
Adib, F., Hsu, C.Y., Mao, H., Katabi, D., Durand, F.: Capturing the human figure through a wall. ACM Trans. Graph. (TOG) 34(6), 219 (2015)
Adib, F., Katabi, D.: See through walls with wifi!. In: Proceedings of the ACM SIGCOMM 2013 Conference (SIGCOMM 2013), pp. 75–86 (2013)
Asadzadeh, P., Kulik, L., Tanin, E.: Gesture recognition using RFID technology. Pers. Ubiquit. Comput. 16(3), 225–234 (2012)
Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) Pervasive 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24646-6_1
Buettner, M., Prasad, R., Philipose, M., Wetherall, D.: Recognizing daily activities with RFID-based sensors. In: Proceedings of 11th ACM International Conference on Ubiquitous Computing (UbiComp), pp. 51–60 (2009)
Ermes, M., Pärkkä, J., Mäntyjärvi, J., Korhonen, I.: Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Trans. Inf. Technol. Biomed. 12(1), 20–26 (2008)
Henpraserttae, A., Thiemjarus, S., Marukatat, S.: Accurate activity recognition using a mobile phone regardless of device orientation and location. In: 2011 International Conference on Body Sensor Networks, pp. 41–46. IEEE (2011)
Hong, J., Ohtsuki, T.: Ambient intelligence sensing using array sensor: device-free radio based approach. In: Proceedings of ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication (2013)
Hung, H., Englebienne, G., Cabrera Quiros, L.: Detecting conversing groups with a single worn accelerometer. In: Proceedings of the 16th International Conference on Multimodal Interaction, pp. 84–91. ACM (2014)
Hung, H., Englebienne, G., Kools, J.: Classifying social actions with a single accelerometer. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 207–210. ACM (2013)
Kern, N., Schiele, B., Junker, H., Lukowicz, P., Tröster, G.: Wearable sensing to annotate meeting recordings. Pers. Ubiquit. Comput. 7(5), 263–274 (2003)
Krishnan, N.C., Panchanathan, S.: Analysis of low resolution accelerometer data for continuous human activity recognition. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3337–3340. IEEE (2008)
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SigKDD Explor. Newslett. 12(2), 74–82 (2011)
Lane, N.D.. et al.: Bewell: a smartphone application to monitor, model and promote wellbeing. In: Proceedings of 5th International ICST Conference on Pervasive Computing Technologies for Healthcare, pp. 23–26 (2011)
Marsan, R.: Weka for android. GitHubRepository (2011). https://github.com/rjmarsan/weka-for-android/
Ruan, W.: Unobtrusive human localization and activity recognition for supporting independent living of the elderly. In: Proceedings of 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp. 1–3 (2016)
Ruan, W., Sheng, Q.Z., Yao, L., Gu, T., Ruta, M., Shangguan, L.: Device-free indoor localization and tracking through human-object interactions. In: 2016 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), June 2016
Ruan, W., Sheng, Q.Z., Yang, L., Gu, T., Xu, P., Shangguan, L.: Audiogest: enabling fine-grained hand gesture detection by decoding echo signals. In: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) (2016)
Ruan, W., Yao, L., Sheng, Q.Z., Falkner, N.J.G., Li, X.: Tagtrack: device-free localization and tracking using passive RFID tags. In: Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2014), pp. 80–89 (2014)
Ruan, W., Yao, L., Sheng, Q.Z., et al.: Tagfall: towards unobstructive fine-grained fall detection based on UHF passive RFID tags. In: The International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2015), pp. 140–149 (2015)
Saeed, A., Kosba, A.E., Youssef, M.: Ichnaea: a low-overhead robust WLAN device-free passive localization system. IEEE J. Sel. Topics Signal Process. 8(1), 5–15 (2014)
Sigg, S., Scholz, M., Shi, S., Ji, Y., Beigl, M.: Rf-sensing of activities from non-cooperative subjects in device-free recognition systems using ambient and local signals. IEEE Trans. Mob. Comput. (TMC) 13(4), 907–920 (2014)
Stikic, M., et al.: ADL recognition based on the combination of RFID and accelerometer sensing. In: Proceedings of International Conference Pervasive Computing Technologies for Healthcare (2008)
Wang, L., Gu, T., Xie, H., Tao, X., Lu, J., Huang, Y.: A wearable RFID system for real-time activity recognition using radio patterns. In: Proceedings of the 10th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) (2013)
Yang, A.Y., Iyengar, S., Kuryloski, P., Jafari, R.: Distributed segmentation and classification of human actions using a wearable motion sensor network. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2008), pp. 1–8. IEEE (2008)
Yao, L., Sheng, Q.Z., Ruan, W., Li, X., Wang, S., Yang, Z.: Unobtrusive posture recognition via online learning of multi-dimensional RFID received signal strength. In: Proceedings of IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS 2015), pp. 116–123 (2015)
Yao, L., Ruan, W., Sheng, Q.Z., Falkner, N.J.G., Li, X.: Exploring tag-free RFID-based passive localization and tracking via learning-based probabilistic approaches. In: Proceedings of 23rd ACM International Conference on Information and Knowledge Management (CIKM) (2014)
Yao, L., Sheng, Q.Z., Li, X., Wang, S., Gu, T., Ruan, W., Zou, W.: Freedom: online activity recognition via dictionary-based sparse representation of RFID sensing data. In: 2015 IEEE International Conference on Data Mining (ICDM), pp. 1087–1092. IEEE (2015)
Zhang, D., Zhou, J., Guo, M., Cao, J., Li, T.: Tasa: tag-free activity sensing using RFID tag arrays. IEEE Trans. Parallel Distrib. Syst. (TPDS) 22(4), 558–570 (2011)
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Ruan, W., Chea, L., Sheng, Q.Z., Yao, L. (2016). Recognizing Daily Living Activity Using Embedded Sensors in Smartphones: A Data-Driven Approach. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science(), vol 10086. Springer, Cham. https://doi.org/10.1007/978-3-319-49586-6_17
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