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Abstract

Managing and Mining mobile sensor data has become a topic of advanced research in several fields of computer science, such as the distributed systems, the database systems, and data mining. The main objective of the sensor based applications is to make the real-time decision which has been proved to be very challenging due to the high resource-constrained computing and the enormous volume of sensor data generated by Wireless Sensor Networks (WSNs). This challenge motivates the sensor research community to explore new data mining techniques to extract information from large continuous raw data streams obtained from WSNs. Existing traditional data mining methods are not directly suited to WSNs due to the aggressive nature of sensor data and the presence of anomalies or outliers in WSNs. This work provides an overview of how traditional outlier detection method algorithms are revised and implemented in the application of Human Activity Recognition (HAR). Based on the limitations of the existing technique, a hybrid outlier detection method is proposed.

Keywords

Classification data mining human activity outlier detection sensor data

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References

M.R.

Alam ,

M.B.I.

Reaz ,

M.A.M.

Ali ,

S.A.

Samad ,

F.H.

Hashim and

M.K.

Hamzah , Human Activity Classification for Smart Home: A Multiagent Approach, in procedia of IEEE Symposium on Industrial Electronics and Applications (ISIEA 2010), Penang, Malaysia, 2010, pp. 511–514.

C.C.

Aggarwal and

Sathe , Theoretical Foundations and Algorithms for Outlier Ensembles, ACM SIGKDD Explorations Newsletter 17(1) (2015), 24–47.

A.S.

Evani ,

Sreenivasan ,

J.S.

Sudesh ,

Prakash and

Bapat , Activity recognition using wearable sensors for healthcare, in the proceedings of Sensor Technologies and Applications (SENSORCOMM) (2013), 173–177.

Alsheikh et al., Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications, IEEE Communications - Surveys & Tutorials (2014), 1996–2018.

Alshurafa ,

Xu ,

J.J.

Liu , et al., Designing a robust activity recognition framework for health and exergaming using wearable sensors, Journal of Biomedical and Health Informatics 18 (2014), 1636–1646.

Althloothi ,

M.H.

Mahoor ,

Zhang and

R.M.

Voyles , Human activity recognition using multi-features and multiple kernel learning, Journal of Pattern Recognition 5 (2014), 1800–1812.

Al-Zoubi ,

Al-Dahoud and

A.A.

Yahya , New outlier detection method based on fuzzy clustering, WSEAS Transactions on Information Science and Applications (2010), 681–690.

Bayat ,

Pomplun and

D.A.

Tran , A Study on Human Activity Recognition Using Accelerometer Data from Smartphones, in Procedia Computer Science (2014), 450–457.

Bhaduri and

Stolpe , Distributed Data Mining in Sensor Networks. In: Aggarwal C. Managing and Mining Sensor Data.- Springer Boston, (2013), 211–236.

10.

Cateni ,

Colla ,

Vannucci and

Vannocci , A Procedure for Building Reduced reliable Training Datasets from Real-WorldData, International Conference on Artificial Intelligence and Applications AIA (IASTED 2014), Austria, (2014), 17–19.

11.

Cateni ,

Colla and

Nastasi , A multivariate fuzzy system applied for outliers detection, Journal of Intelligent and Fuzzy Systems 24(4) (2013), 889–903.

12.

Cheng ,

Chen and

Shen , A framework for daily activity monitoring and fall detection based on surface electromyography and accelerometer signals (2013), 38–45.

13.

Cheng and

Zhu , Lightweight anomaly detection for wireless sensor networks, Journal of Distributed Sensors Networks, 2015, DOI:10.1155/2015/653232

14.

Arifoglu and

Bouchachia , Activity Recognition and Abnormal Behaviour Detection with Recurrent Neural Networks, The 14th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2017) (2017), 86–93.

15.

De ,

Bharti ,

Sajal K.

Das and

Chellappan , Multimodal wearable sensing for fine-grained activity recognition in healthcare, IEEE transactions on Internet Computing 19 (2015), 26–35.

16.

W.-Y.

Deng ,

Q.-H.

Zheng and

Z.-M.

Wang , Cross-person activity recognition using reduced kernel extreme learning machine, Journal of Neural Networks 53 (2014), 1–7.

17.

Duan ,

Xu ,

Liu and

Lee , Cluster-based outlier detection, Annals of Operating Research (2009), 151–168.

18.

Fawzy and

H.M.

Mokhtar , O. Outliers detection and classification in wireless sensor networks, Egyptian Informatics (2013), 157–164.

19.

Gao ,

A.K.

Bourke and

Nelson , Evaluation of accelerometer-based multi-sensor versus single-sensor activity recognition systems, Journal of Medical Engineering and Physics 36 (2014), 779–785.

20.

Gomes ,

Krishnaswamy ,

Gaber ,

Sousa and

Emars , A Personalized Mobile Activity Recognition System, in Proceedings of the IEEE International Conference on Mobile Data Management (MDM), Bengaluru, Karnataka, (2012), 316–319.

21.

S.A.

Haque ,

Rahman and

S.M.

Aziz , Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare, Sensors (2015), 8764–8786.

22.

Jurdak ,

X.R.

Wang ,

Obst and

Valencia , Wireless Sensor Network Anomalies: Diagnosis and Detection Strategies, Intelligent Systems Reference Library (2011), 309–325.

23.

Vishwakarma ,

Kapoor and

Dhiman , a Unified framework for human activity recognition: An approach using spatial edge distribution and R-transform, International Journal of Electronic & Communication (2016), 341–353.

24.

T.S.

Kim ,

J.H.

Cho and

J.T.

Kim , Mobile Motion Sensor-Based Human Activity Recognition and Energy Expenditure Estimation in Building Environments, Smart Innovations, Systems and Technology (2013), 987–993.

25.

Kose ,

O.D.

Incel and

Ersoy , Online Human Activity Recognition on Smart Phones, in Proceedings of the workshop on Mobile Sensing: From Smart phones and Wearables to Big Data, Beijing, China (2012), pp. 11–15.

26.

Lara and

Labrador , A mobile platform for real-time Human Activity Recognition, Proceedings of the IEEE Consumer Communications and Networking Conference, Las Vegas, USA, (2012), pp. 667–671.

27.

Lu and

qing-Ling and

Yi-Ju , Activity Recognition in Smart Homes, Multimedia Tools Appl 2016, doi: 10.1007/s11042-016-4197-1

28.

Mahmood ,

Shi ,

Khatoon and

Xiao , Data Mining Techniques for Wireless Sensor Networks: A Survey, Journal of Distributed Sensor Networks (2013), 1–24.

29.

Mannini and

A.M.

Sabatini , Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers, Sensors (2010), 1154–1175.

30.

E.A.

Mosabbeb ,

Cabral ,

De la F.

Torre and

Fathy , Multi-label Discriminative Weakly-Supervised Human Activity Recognition and Localization, in the proceedings of Computer Vision - ACCV 2014. Springer International Publishing (2014), pp. 241–258.

31.

Nivetha and

Venkatalakshmi , Hybrid outlier detection (HOD) method in sensor data for human activity classification, Journal of Intelligent Data Analysis (2018), 245–260, doi: 10.3233/IDA-163329

32.

J.C.

Quiroz ,

Banerjee ,

S.M.

Dascalu and

S.L.

Lau , Feature Selection for Activity Recognition from Smartphone Accelerometer Data Feature Selection for Activity Recognition from Smartphone Accelerometer Data, Journal of Intelligent Automation & Soft Computing (2017), 1–9.

33.

Rajasegarar ,

Leckie ,

Palaniswami ,

J.C.

Bezdek and

Palaniswami , Anomaly detection in wireless sensor networks, IEEE Transactions on Wireless Communications 15 (2008), 34–40.

34.

Rajasegarar ,

Gluhak ,

Ali ,

Nati ,

Moshtaghi ,

Leckie and

Palaniswami , Ellipsoidal neighborhood outlier factor for distributed anomaly detection in resource-constrained networks, Journal of Pattern Recognition 47 (2014), 2867–2879.

35.

San-segundo ,

J.M.

Montero ,

Barra-chicote ,

Fernández and

J.M.

Pardo , Feature extraction from smartphone inertial signals for human activity segmentation, Signal Processing (2016), 359–372.

36.

Shahid ,

Haider ,

Saad and

Qaisar , Characteristics, and classification of outlier detection techniques for wireless sensor networks in harsh environments: A survey, Journal of Artificial Intelligence Review (2015), 193–228.

37.

Shahid ,

Haider ,

Saad and

Qaisar , One-class support vector machines: Analysis of outlier detection for wireless sensor networks in harsh environments, Artificial Intelligence Review, 2013. doi: 10.1007/s10462-013-9395-x

38.

Sharma ,

Rajpoot and

L.K.

Sharma , Nearest Neighbour Classification for Wireless Sensor Network Data, Journal of Computer Trends and Technology (2011), 41–43.

39.

Sheng ,

Li ,

Mao and

Jin , Outlier detection in sensor networks, Proc. of MobiHoc’07, Québec, Canada, 2007, pp. 219–228.

40.

Shoaib et al., A Survey of Online Activity Recognition Using Mobile Phones, Journal of Sensors (2015), 2059–2085.

41.

Su ,

Long ,

Qiu ,

Li and

Liu , Anomaly Detection of Single Sensors Using OCSVM_KNN. In Big Data Computing and Communications; Springer: Cham, Switzerland, (2015), pp. 217–230.

42.

Subramaniam ,

Palpanas ,

Papadopoulos, V.

Kalogeraki and

Gunopulos , Online outlier detection in sensor data using non-parametric models, in Proceedings of VLDB’06 International conference on Very large databases, Korea, (2006), pp. 187–198.

43.

Q.V.

Vo ,

M.T.

Hoang and

Choi , Personalization in Mobile Activity Recognition System Using K- Medoids Clustering Algorithm, Journal of Distributed Sensor Networks, (2013), 1–12. www.mathwords.com/o/outlier.htm

44.

Yan ,

You ,

Ji ,

Yin and

Yang , A hybrid outlier detection method for health care big data. Proceedings - 2016 IEEE International Conferences on Big Data and Cloud Computing, BDCloud, Social Computing and Networking, SocialCom and Sustainable Computing and Communications, SustainCom, (2016), pp. 157–162.

45.

N.A.

Yousri ,

M.A.

Ismail and

M.S.

Kamel , Fuzzy outlier analysis a combined clustering - Outlier detection approach, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2007, pp. 412–418.

46.

Yu ,

Junsong and

Zicheng , Propagative hough voting for human activity 645 recognition, Computer Vision-ECCV 2012, Springer Berlin Heidelberg (2012), pp. 693–706.

47.

Yu ,

Jibin and

Jiang , An Improved ARIMA-Based Traffic Anomaly Detection Algorithm for Wireless Sensor Networks, Journal of Distributed Sensors Networks (2016), 1–9.

48.

Zhang ,

Meratnia and

Havinga , Outlier Detection Techniques for Wireless Sensor Networks: A Survey, IEEE Transactions on Communications and Surveys (2010), pp. 159–170.

49.

Zhao ,

Du ,

Li ,

Zhang ,

Liu and

Xu , Healthy: A Diary System Based on Activity Recognition Using Smartphone, in Proceedings of Mobile Ad-Hoc and Sensor Systems (MASS)”, Hangzhou, China, (2013), pp. 290–294.

50.

Zhang ,

Shi ,

Gao and Li, Unsupervised outlier detection in sensor networks using aggregation tree, in Proc of ADMA-Advanced Data Mining and Applications, Harbin, China, (2007), pp. 158–169.

51.

Zimek ,

R.J.G.B.

Campello and

Sander , Ensembles for unsupervised outlier detection, ACM SIGKDD Explorations Newsletter 15(1) (2014), 11–22.

Improving data classification accuracy in sensor networks using hybrid outlier detection in HAR

Abstract

Keywords

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