Open Access Open Access  Restricted Access Subscription or Fee Access

A Machine Learning Model for Human Motion Detection and Event Feedback

Zhipeng Cai, Luis Fernando, Bechir Hamdaoui


Fundamentally it is tough to answer, what is a peculiar event. Completely normal event in one situation would possibly turn out to be bizarre in the any other situation. For instance, reflect on consideration on the activities like, ‘a cattle is grazing in a giant open field’ and ‘a cattle is grazing in a house backyard’. In video processing the place the heritage get subtracted, each are the equal events; besides the situation the place the motion of grazing is being performed. An essential aspect that separates both activities aside is the normal frequency of their occurrences. The grazing is common action and occurs often in an open grass field unlike the residence backyard, which makes it normal tournament for that situation. When the grazing is repeatedly performed many instances in the house outside (probably in suburb) then it becomes regular pastime and have to be classified as the ordinary event. Hence anomalous activities happen enormously infrequently. After performing experimental results for clustering performance evaluation on artificial dataset show that the clustering outperforms the other clustering methods, for clustering the single dominant class from the dataset.


Human Activity Recognition, Detection of Human Actions, Machine Learning Techniques, Action Recognition.

Full Text:



Andrey Ignatov, “Real-time human activity recognition from accelerometer data using Convolutional Neural Networks”, Applied Soft Computing, Elsevier, Volume 62, January 2018, Pages 915-922.

Ch.Shravya, K. Pravalika, Shaik Subhani, Prediction of Breast Cancer Using Supervised Machine Learning Techniques, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-6, April 2019

Charissa Ann Ronao, Sung-Bae Cho, “Human activity recognition with smart phone sensors using deep learning neural networks”, Expert Systems with Applications, Elsevier, Volume 59, October 2016, Pages 235-244.

Deepika Singh, Erinc Merdivan, Ismini Psychoula, Johannes Kropf, Sten Hanke, Matthieu Geist, and Andreas Holzinger, “Human Activity Recognition Using Recurrent Neural Networks”, International Federation for Information Processing, Springer, May 2017, Pages 267-274.

Dona Sara Jacob, Rakhi Viswan, V Manju, L PadmaSuresh, Shine Raj, “A Survey on Breast Cancer Prediction Using Data Mining Techniques”, Proc. IEEE Conference on Emerging Devices and Smart Systems (ICEDSS 2018) 2-3 March 2018, Mahendra Engineering College, Tamilnadu, India

Lina Yao, Quan Z. Sheng, Xue Li, Tao Gu, Mingkui Tan, Xianzhi Wang, Sen Wang, and Wenjie Ruan, “Compressive Representation for Device-Free Activity Recognition with Passive RFID Signal Strength”, IEEE Transactions on Mobile Computing, Volume 17, Issue No.2, February 2018, Pages 293 – 306.

Linlin Guo , Lei Wang , Jialin Liu, Wei Zhou, and Bingxian Lu, “HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data”, Hindawi Wireless Communications and Mobile Computing, Volume 2018, May 2018,Pages 1-15.

Mohamed Adel, Ahmed Kotb, Omar Farag, M. Saeed Darweesh, Hassan Mostafa Institute of Aviation Engineering and Technology, Giza, Egypt, Breast Cancer Diagnosis Using Image Processing and Machine Learning for Elastography Images, 2019 8th International Conference on Modern Circuits and Systems Technologies (MOCAST)

Mohammed Mehedi Hassan, Md. Zia Uddin, Amr Mohamed, Ahmad Almogren, “A robust human activity recognition system using smartphone sensors and deep learning”, Future Generation Computer Systems, Elsevier, Volume 81, April 2018, Pages 307-313.

N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines. Cambridge University Press, 2000.

S. Gaglio, G. Re, and M. Morana, “Human activity recognition process using 3-d posture data”, IEEE Transactions on Human- Machine Systems, Volume 45, Issue No.5, October 2015, Pages 586–597.

T.M. Mitchel, The Discipline of Machine Learning, CMU-ML-06-108, 2006

Taiwo Oladipupo Ayodele, Types of Machine Learning Algorithms, New Advances in Machine Learning, Yagang Zhang (Ed.), InTech, 2010

Yaqiang Yao, Yan Liu, Zhenyu Liu, Huanhuan Chen, “Human Activity Recognition with Posture Tendency Descriptors on Action Snippets”, IEEE Transactions on Big Data, Volume PP, Issue No.99, February 2018, Pages 1-13.

Zohaib Mushtaq, Akbari Yaqub, Ali Hassan, Shun Feng Su, Performance Analysis of Supervised Classifiers using PCA based Techniques on Breast Cancer, 2019 IEEE


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.