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基于智慧型手机行为辨识进行社群活动之推荐

【中文摘要】:随着智慧型手机硬体的显着进步,藉由手机内建的感测器来达到人类活动的辨识已经不再是那幺遥不可及。活动辨识对于医疗保健,健康管[cus

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tom_frame_center]szwox.com_189[/custom_frame_center]理和预防是一项重要的资讯的来源。那些具有创意的活动辨识手机程式,即所谓有能力去判断目前个人在进行怎样活动的软体,给予其用者可以查看他们家人目前的健康及安全情况。

本篇论文旨在达成两项目标:複杂活动的辨识以及可以促进社群互动的手机程式。为了完成这两项目标,有些步骤是需要的。首先,对于简单活动特徵向量的分析。简单活动的资料像是坐姿、走路、骑机车及搭电梯等讯息被搜集起来并转换成特徵向量供进一步使用。结果指出,藉由所选的特徵选择方法,辨识率得到了改善,计算成本也被降到最低。

複杂活动辨识像是搭乘大众运输工具、出门购物、放鬆及用餐仍然具有相当的挑战性。这些複杂活动通常由好几个简单活动所组成,而且随着个体的不同,其变化性也对的大。在这项研究中,共有17个小时的数据记录。另外,为了降低数据集的数量和基于所创建的模型来表示複杂活动,两个高阶特徵选择方法,BoF和LDA,被採用。结果显示,活动标籤结合BoF可以提供比LDA更好的準确率。

最后本篇论文也给予其它研究者有关感应器探勘相关应用程式的考量,对于未来的开发将会有一定的助益。
【英文摘要】:With significant advances made in smartphone hardware, recognizing human activities from smartphone sensors has become possible. Activity recognition is a valuable source of information for healthcare, fitness tracking and prevention. Those innovative activity-recognition-based applications which have the ability to identify the activity a person is performing allow users to monitor the health and safety of their family members.

This thesis aims to achieve two major goals: the recognition of complex activities and an Android-based application for physical social interaction. In order to complete the two goals, some steps are required. Firstly, an analysis of

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feature selection methods for micro-activity recognition. A data set of micro-activities such as sitting, walking, riding a scooter, driving, and riding an elevator were collected and transformed into feature vectors for further use. The results indicate that with the help of chosen feature selection methods, recognition accuracy was improved and computational cost was also minimized.

Recognition of complex activity such as using public transportation, going out for shopping, relaxing, and having a meal is still very challenging. These activities are composed of several micro activities and vary strongly across individuals. In this study, a total 17 hours of data were recorded. Besides, in order to reduce the volume of dataset and represent complex activities based on a created model, two high-order feature selection methods, BoF and LDA, are considered. The result indicates that BopF combined with recognized activity labels can provide better accuracy than LDA.

In the end, this thesis also gives some thoughts to the design of the sensor mining application which will be useful for others to build their own application.
【参考文献】:

  • [1] Fitbit. http://www.fitbit.com
  • [2] Nike+ Fuelband. http://www.nike.com/fuelband
  • [3] S. Consolvo et al., “Activity Sensing in the Wild: A Field Trial of Ubifit Garden,” Proc.
  • 26th Annual ACM SIGCHI Conf. Human Factors Comp. Sys., 2008, pp.1797–1806.
  • [4] A. Cheung, A. Thiagarajan, and S. Madden. “Automatically generating interesting
  • events with lifejoin”. In proc. Sensys, pages 411–412, 2011.
  • [5] CHOUJAA, D. AND DULAY, N. Activity recognition using mobile phones:
  • Achievements, challenges and recommendations. In Proceedings of the Workshop
  • on How To Do Good Research in Activity Recognition: Experimental Methodology,
  • Performance Evaluation and Reproducibility in conjunction with UBICOMP, 2010.
  • [6] A.J. Bernheim Brush, John Krumm, James Scott, and T. Scott Saponas.
  • Recognizing Activities from Mobile Sensor Data: Challenges and Opportunities.
  • Microsoft Research, 2011.
  • [7] J.R. Kwapisz, G. M. Weiss, and S.A. Moore. “Activity recognition using cell phone
  •   accelerometers,” Proceedings of the Fourth International Workshop on Knowledge
  •   Discovery from Sensor Data, pp. 10-18, 2010.
  • [8] A. Rai, Z. Yan, D. Chakraborty, T. Wijaya, and K. Aberer. Mining complex activities
  • in the wild via a single smartphone accelerometer. In Proceedings of the Sixth
  • International Workshop on Knowledge Discovery from Sensor Data, pages
  •   43-51.ACM, 2012.
  • [9] Yang, J. 2009. Toward physical activity diary: Motion recognition using simple
  • acceleration features with mobile phones, In First International Workshop on
  • Interactive Multimedia for Consumer Electronics at ACM Multimedia.
  • [10] Oreskovic NM, Blossom J, Field AE, Chiang SR, Winickoff JP, Kleinman RE.
  • Combining global positioning system and accelerometer data to determine the
  • locations of physical activity in children. Geospat Health. 2012.
  • [11] L. Liao, D. J. Patterson, D. Fox, and H. Kautz, “Learning and inferring transportation
  • routines,” Artif. Intell., vol. 171, no. 5–6, pp. 311–331,2007.
  • [12] S. Kratz, M. Rohs, G. Essl. Combining Acceleration and Gyroscope Data for
  • Motion Gesture Recognition using Classifiers with Dimensionality Constraints.
  • IUI”13, 2013.
  • [13] Q. Li, J.A. Stankovic, M. Hanson, A. Barth, J. Lach. Accurate, fast fall detection
  • using gyroscopes and accelerometer-derived posture information. In Proceedings
  • of the 6th International Workshop on Wearable and Implantable Body Sensor
  • Networks (BSN’09), 2009.
  • [14] Qin, C., Bao, X., Choudhury, R. R., and Nelakuditi, S.TagSense: A smartphone
  • based approach to automatic image tagging. In ACM MobiSys , 2011.
  • [15] H. Lu et al., “Sound-Sense: Scalable Sound Sensing for People-Centric
  • Applications on Mobile Phones,” Proc. 7th ACM MobiSys, 2009, pp. 165–78.
  • [16] A. Nishimura, I. Siio, “conteXinger : A Context-aware Song Generator”, Proc.
  • 13th ACM, UbiComp, 2013, pp. 87-90.
  • [17] M. Fahim, I. Fatima, S. Lee, Y.K. Lee, “Daily Life Activity Tracking Application
  • for Smart Homes using Android Smartphone”. In IEEE ICACT, 2012.
  • [18] B. Das, A. M. Seelye, B. L. Thomas, D. J. Cook, L. B. Holder, M. S. Edgecombe,
  • “Using Smart Phones for Context-Aware Prompting in Smart Environments”. In
  • EEE CCNC, 2012.
  • [19] A. Anjum and M. U. Ilyas, “Activity recognition using smartphone sensors,”
  • Consumer Commun. and Networking Conference (CCNC), 2013 IEEE, pp. 914-919,
  • Jan. 2013.
  • [20] C. Qin, X. Bao, R. R. Choudhury and S. Nelakuditi, “TagSense: A smartphone based
  • approach to automatic image tagging”. In ACM MobiSys (2011).
  • [21] E. Miluzzo et al., “Sensing meets Mobile Social Networks: The Design,
  • Implementation, and Evaluation of the CenceMe Application”, Proc. 6th ACM SenSys,
  • 2008, pp. 337–50.
  • [22] T. Hu`ynh, M. Fritz, B. Schiele, “Discovery of Activity Patterns using Topic Models”,
  • Proceedings of the Tenth International Conference on Ubiquitous Computing; 2008.
  • pp. 10–19.
  • [23] M. Zhang and A. A. Sawchuk, “Motion primitive-based human activity recognition
  • using a bag-of-features approach,” in ACM SIGHIT International Health Informatics
  • Symposium (IHI), (Miami, Florida, USA), pp. 1–10, January 2012.
  • [24] T. Hu`ynh, M. Fritz, B. Schiele, “Discovery of Activity Patterns using Topic Models”,
  • Proceedings of the Tenth International Conference on Ubiquitous Computing; 2008.
  • pp. 10–19.
  • [25] D. J. Cook, N. C. Krishnan, P. Rashidi, “Activity Discovery and Activity Recognition:
  • New Partnership”, IEEE Transactions on Cybernetics, June, 2013.
  • [26] L. Breiman, E. Schapire, “Random Forests”, In Machine Learning, pages 5-32 June,
  • 2001.
  • [27] P. Casale, O. Pujol, P. Radeva, ”Human activity recognition from accelerometer
  • data using a wearable device” , Proc. 5th ACM, IbPRIA, 2011, pp. 289-296.
  • [28] S. Dernbach, B. Das, N. C. Krishnan, B. L. Thomas, D. J. Cook, ”Simple and
  • Complex Activity Recognition Through Smart Phones” , Proc. 8th IEEE, IE, 2012,
  • pp. 214-221.
  • [29] D. L. Vail, M. M. Veloso, J. D. Lafferty, ”Conditional random fields for activity
  • recognition”, Proc. 6th ACM, AAMAS, 2007.
  • [30] G. M. Weiss and J. W. Lockhart. The Impact of Personalization on Smartphone-
  • Based Activity Recognition. In AAAI Workshop on Activity Context Representation:
  • Techniques and Languages, 2012.
  • [31] G. Dong and J. Li. “Efficient Mining of Emerging Patterns: Discovering Trends and
  • Differences”, Proc. 5th ACM, SIGKDD, 1999, pp. 43-52.
  • [32] T. Gu, L. Wang, Z. Wu, X. Tao and J. Lu. “A Pattern Mining Approach to Sensor-
  • Based Human Activity Recognition”, IEEE, Transactions, 2011, pp.1359-1372.
  • [33] Z. Yan, D. Chakraborty, A. Misra, H. Jeung and K. Aberer. “Semantic Activity
  • Classification Using Locomotive Signatures from Mobile Phones”, EPFL-REPORT,
  • 2012.
  • [34] Z. HE, L. Jin, “Activity Recognition from acceleration data Based on Discrete Consine
  • Transform and SVM”, Proc. IEEE, Man and Cybernetics, 2009, pp. 5041–5044.
  • [35] Mallet. http://mallet.cs.umass.edu/
  • [36] Lockhart, J.W. and Weiss, G. 2011. Design considerations for the WISDM smart
  • phone-based sensor mining architecture. In Proceedings of the Fifth International
  • Workshop on Knowledge Discovery from Sensor Data, San Diego, CA.
  • 来源:中山大学;作者:陈俊廷
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