Abstract
The diffusion of heterogeneous smart devices capable of capturing and analysing data about users, and/or the environment, has encouraged the growth of novel sensing methodologies. One of the most attractive scenarios in which such devices, such as smartphones, tablet computers, or activity trackers, can be exploited to infer relevant information is human activity recognition (HAR). Even though some simple HAR techniques can be directly implemented on mobile devices, in some cases, such as when complex activities need to be analysed timely, users’ smart devices can operate as part of a more complex architecture. In this article, we propose a multi-device HAR framework that exploits the fog computing paradigm to move heavy computation from the sensing layer to intermediate devices and then to the cloud. As compared to traditional cloud-based solutions, this choice allows to overcome processing and storage limitations of wearable devices while also reducing the overall bandwidth consumption. Experimental analysis aims to evaluate the performance of the entire platform in terms of accuracy of the recognition process while also highlighting the benefits it might bring in smart environments.
- Sylvain Arlot and Alain Celisse. 2010. A survey of cross-validation procedures for model selection. Statistics Surveys 4 (2010), 40--79.Google Scholar
Cross Ref
- Ling Bao and Stephen S. Intille. 2004. Activity Recognition From User-Annotated Acceleration Data. Germany, 1--17.Google Scholar
- Timothy C. Bell, John G. Cleary, and Ian H. Witten. 1990. Text Compression. Prentice Hall, Upper Saddle River, NJ. Google Scholar
Digital Library
- James Bergstra and Yoshua Bengio. 2012. Random search for hyper-parameter optimization. Journal of Machine Learning Research 13 (Feb. 2012), 281--305. Google Scholar
Digital Library
- Flavio Bonomi, Rodolfo Milito, Jiang Zhu, and Sateesh Addepalli. 2012. Fog computing and its role in the Internet of Things. In Proceedings of the 1st Edition of the MCC Workshop on Mobile Cloud Computing (MCC’12). ACM, New York, NY, 13--16. Google Scholar
Digital Library
- Y. Cao, S. Chen, P. Hou, and D. Brown. 2015. FAST: A fog computing assisted distributed analytics system to monitor fall for stroke mitigation. In Proceedings of the 2015 IEEE International Conference on Networking, Architecture, and Storage (NAS’15). IEEE, Los Alamitos, CA, 2--11.Google Scholar
- Giuseppe Cardone, Andrea Cirri, Antonio Corradi, Luca Foschini, and Dario Maio. 2013. MSF: An efficient mobile phone sensing framework. International Journal of Distributed Sensor Networks 9, 3 (2013), 538937.Google Scholar
Cross Ref
- Giuseppe Cardone, Antonio Corradi, Luca Foschini, and Raffaele Ianniello. 2016. ParticipAct: A large-scale crowdsensing platform. IEEE Transactions on Emerging Topics in Computing 4, 1 (2016), 21--32. Google Scholar
Digital Library
- Byung-Gon Chun, Sunghwan Ihm, Petros Maniatis, Mayur Naik, and Ashwin Patti. 2011. CloneCloud: Elastic execution between mobile device and cloud. In Proceedings of the 6th Conference on Computer Systems (EuroSys’11). ACM, New York, NY, 301--314. Google Scholar
Digital Library
- F. Concone, P. Ferraro, and G. Lo Re. 2018. Towards a smart campus through participatory sensing. In Proceedings of the 2018 IEEE International Conference on Smart Computing (SMARTCOMP’18). IEEE, Los Alamitos, CA, 393--398.Google Scholar
- Federico Concone, Salvatore Gaglio, Giuseppe Lo Re, and Marco Morana. 2017. Smartphone Data Analysis for Human Activity Recognition. Springer International Publishing, Cham, Switzerland, 58--71.Google Scholar
- Božidara Cvetković, Vito Janko, Alfonso E. Romero, Özgür Kafalı, Kostas Stathis, and Mitja Luštrek. 2016. Activity recognition for diabetic patients using a smartphone. Journal of Medical Systems 40, 12 (2016), 256. Google Scholar
Digital Library
- A. V. Dastjerdi and R. Buyya. 2016. Fog computing: Helping the Internet of Things realize its potential. Computer 49, 8 (Aug. 2016), 112--116. Google Scholar
Digital Library
- Jesse Davis and Mark Goadrich. 2006. The relationship between precision-recall and ROC curves. In Proceedings of the 23rd International Conference on Machine Learning (ICML’06). ACM, New York, NY, 233--240. Google Scholar
Digital Library
- Kaibo Duan and S. Sathiya Keerthi. 2005. Which is the best multiclass SVM method? An empirical study. Multiple Classifier Systems 3541 (2005), 278--285. Google Scholar
Digital Library
- S. Gaglio, G. Lo Re, and M. Morana. 2015. Human activity recognition process using 3-D posture data. IEEE Transactions on Human-Machine Systems 45, 5 (Oct. 2015), 586--597.Google Scholar
Cross Ref
- R. K. Ganti, F. Ye, and H. Lei. 2011. Mobile crowdsensing: Current state and future challenges. IEEE Communications Magazine 49, 11 (Nov. 2011), 32--39.Google Scholar
Cross Ref
- Google. 2016. Activity Recognition API. Retrieved March 15, 2019 from https://developers.google.com/android/reference/com/google/android/gms/location/ActivityRecognitionApi/.Google Scholar
- John A. Hartigan and Manchek A. Wong. 1979. Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics) 28, 1 (1979), 100--108.Google Scholar
Cross Ref
- Kirak Hong, David Lillethun, Umakishore Ramachandran, Beate Ottenwälder, and Boris Koldehofe. 2013. Mobile fog: A programming model for large-scale applications on the Internet of Things. In Proceedings of the 2nd ACM SIGCOMM Workshop on Mobile Cloud Computing (MCC’13). ACM, New York, NY, 15--20. Google Scholar
Digital Library
- Chih-Wei Hsu and Chih-Jen Lin. 2002. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13, 2 (2002), 415--425. Google Scholar
Digital Library
- S. Ickin, K. Wac, M. Fiedler, L. Janowski, J. H. Hong, and A. K. Dey. 2012. Factors influencing quality of experience of commonly used mobile applications. IEEE Communications Magazine 50, 4 (April 2012), 48--56.Google Scholar
Cross Ref
- Prem Prakash Jayaraman, João Bártolo Gomes, Hai Long Nguyen, Zahraa Said Abdallah, Shonali Krishnaswamy, and Arkady Zaslavsky. 2014. CARDAP: A Scalable Energy-Efficient Context Aware Distributed Mobile Data Analytics Platform for the Fog. Springer International Publishing, Cham, Switzerland, 192--206.Google Scholar
- Wazir Zada Khan, Yang Xiang, Mohammed Y. Aalsalem, and Quratulain Arshad. 2013. Mobile phone sensing systems: A survey. IEEE Communications Surveys and Tutorials 15, 1 (2013), 402--427.Google Scholar
Cross Ref
- E. Kim, S. Helal, and D. Cook. 2010. Human activity recognition and pattern discovery. IEEE Pervasive Computing 9, 1 (Jan. 2010), 48--53. Google Scholar
Digital Library
- S. R. Kodituwakku and U. S. Amarasinghe. 2010. Comparison of lossless data compression algorithms for text data. Indian Journal of Computer Science and Engineering 1, 4 (2010), 416--425.Google Scholar
- Yongjin Kwon, Kyuchang Kang, and Changseok Bae. 2014. Unsupervised learning for human activity recognition using smartphone sensors. Expert Systems With Applications 41, 14 (2014), 6067--6074.Google Scholar
Cross Ref
- John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the 18th International Conference on Machine Learning (ICML’01). 282--289. http://dl.acm.org/citation.cfm?id=645530.655813. Google Scholar
Digital 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 Scholar
Cross Ref
- Jonathan Lester, Tanzeem Choudhury, and Gaetano Borriello. 2006. A Practical Approach to Recognizing Physical Activities. Germany, 1--16. Google Scholar
Digital Library
- Zhen Li, Zhiqiang Wei, Yaofeng Yue, Hao Wang, Wenyan Jia, Lora E. Burke, Thomas Baranowski, et al. 2015. An adaptive hidden Markov model for activity recognition based on a wearable multi-sensor device. Journal of Medical Systems 39, 5 (2015), 57. Google Scholar
Digital Library
- Andrea Mannini, Mary Rosenberger, William L. Haskell, Angelo M. Sabatini, and Stephen S. Intille. 2017. Activity recognition in youth using single accelerometer placed at wrist or ankle. Medicine and Science in Sports and Exercise 49, 4 (2017), 801--812.Google Scholar
Cross Ref
- Peter M. Mell and Timothy Grance. 2011. SP 800-145. The NIST Definition of Cloud Computing. Technical Report. NIST, Gaithersburg, MD. Google Scholar
Digital Library
- Nurzhan Nurseitov, Michael Paulson, Randall Reynolds, and Clemente Izurieta. 2009. Comparison of JSON and XML data interchange formats: A case study. Caine 2009 (2009), 157--162.Google Scholar
- Abhijit S. Ogale, Alap Karapurkar, and Yiannis Aloimonos. 2007. View-Invariant Modeling and Recognition of Human Actions Using Grammars. Germany, 115--126.Google Scholar
- Shyamal Patel, Hyung Park, Paolo Bonato, Leighton Chan, and Mary Rodgers. 2012. A review of wearable sensors and systems with application in rehabilitation. Journal of Neuroengineering and Rehabilitation 9, 1 (2012), 21.Google Scholar
Cross Ref
- Charith Perera, Yongrui Qin, Julio C. Estrella, Stephan Reiff-Marganiec, and Athanasios V. Vasilakos. 2017. Fog computing for sustainable smart cities: A survey. ACM Computing Surveys 50, 3 (June 2017), Article 32, 43 pages. Google Scholar
Digital Library
- C. Perera, D. S. Talagala, C. H. Liu, and J. C. Estrella. 2015. Energy-efficient location and activity-aware on-demand mobile distributed sensing platform for sensing as a service in IoT clouds. IEEE Transactions on Computational Social Systems 2, 4 (Dec. 2015), 171--181.Google Scholar
Cross Ref
- David Martin Powers. 2011. Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. Machine Learning Technologies 2 (2011), 37--63.Google Scholar
Cross Ref
- L. Rabiner and B. Juang. 1986. An introduction to hidden Markov models. IEEE ASSP Magazine 3, 1 (1986), 4--16.Google Scholar
Cross Ref
- Nishkam Ravi, Nikhil Dandekar, Preetham Mysore, and Michael L. Littman. 2005. Activity recognition from accelerometer data. In Proceedings of the 17th Conference on Innovative Applications of Artificial Intelligence—Volume 3 (IAAI’05). 1541--1546. http://dl.acm.org/citation.cfm?id=1620092.1620107. Google Scholar
Digital Library
- Juan D. Rodriguez, Aritz Perez, and Jose A. Lozano. 2010. Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 3 (2010), 569--575. Google Scholar
Digital Library
- Michael S. Ryoo and Jake K. Aggarwal. 2009. Semantic representation and recognition of continued and recursive human activities. International Journal of Computer Vision 82, 1 (2009), 1--24. Google Scholar
Digital Library
- Bernhard Scholkopf and Alexander J. Smola. 2001. Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA. Google Scholar
Digital Library
- Bo Tang, Zhen Chen, Gerald Hefferman, Tao Wei, Haibo He, and Qing Yang. 2015. A hierarchical distributed fog computing architecture for big data analysis in smart cities. In Proceedings of the 2015 ASE BigData and SocialInformatics Conference (ASE BD8SI’15). ACM, New York, NY, Article 28, 6 pages. Google Scholar
Digital Library
- Van Vo, Jiawei Luo, and Bay Vo. 2016. Time series trend analysis based on K-means and support vector machine. Computing and Informatics 35, 1 (2016), 111--127.Google Scholar
- J. Vora, S. Tanwar, S. Tyagi, N. Kumar, and J. J. P. C. Rodrigues. 2017. FAAL: Fog computing-based patient monitoring system for ambient assisted living. In Proceedings of the 2017 IEEE 19th International Conference one-Health Networking, Applications, and Services (Healthcom’17). IEEE, Los Alamitos, CA, 1--6.Google Scholar
Cross Ref
- Yukai Yao, Yang Liu, Yongqing Yu, Hong Xu, Weiming Lv, Zhao Li, and Xiaoyun Chen. 2013. K-SVM: An effective SVM algorithm based on K-means clustering. Journal of Computers 8, 10 (2013), 2632--2639.Google Scholar
Cross Ref
- Xinwen Zhang, Anugeetha Kunjithapatham, Sangoh Jeong, and Simon Gibbs. 2011. Towards an elastic application model for augmenting the computing capabilities of mobile devices with cloud computing. Mobile Networks and Applications 16, 3 (June 2011), 270--284. Google Scholar
Digital Library
Index Terms
A Fog-Based Application for Human Activity Recognition Using Personal Smart Devices
Recommendations
Deep Reinforcement Scheduling for Mobile Crowdsensing in Fog Computing
Special Issue on Fog, Edge, and Cloud IntegrationMobile crowdsensing becomes a promising technology for the emerging Internet of Things (IoT) applications in smart environments. Fog computing is enabling a new breed of IoT services, which is also a new opportunity for mobile crowdsensing. Thus, in ...
Bootstrapping Human Activity Recognition Systems for Smart Homes from Scratch
Smart Homes have come a long way: From research laboratories in the early days, through (almost) neglect, to their recent revival in real-world environments enabled through the existence of commodity devices and robust, standardized software frameworks. ...
A spanning tree-based human activity prediction system using life logs from depth silhouette-based human activity recognition
CAIP'11: Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part IIn this work, we propose a Human Activity Prediction (HAP) system using activity sequence spanning trees constructed from a life-log created by a video sensor-based daily Human Activity Recognition (HAR) system using time-sequential Independent ...






Comments