A human activity recognition using CNN and long term short term memory
Keywords:
human activity recognition, convolutional neural network, deep learning, long-term short-term memory, machine learning, skeletonAbstract
Human activity recognition aims to work out the activities performed by someone in a picture or video. Examples of actions are running, sitting, sleeping, and standing. Complex movement patterns and harmful occurrences like falling may be a part of these activities. The suggested ConvLSTM network can be created by successively combining fully connected layers, long immediate memory (LSTM) networks, and convolutional neural networks (CNN). The acquisition system will pre calculate skeleton coordinates using human detection and pose estimation from the image/video sequence. The ConvLSTM model builds new controlled features from the raw skeleton coordinates and their distinctive geometric and kinematic properties. Raw skeleton coordinates are utilized to generate geometric and kinematic properties supported by relative joint position values, joint differences, and their angular velocities. By utilizing a multi-player trained CNN-LSTM combination, novel spatiotemporal directed features can be obtained. The classification head with completely connected layers is then utilized. The suggested model was tested using the KinectHAR dataset, which consists of 130,000 samples with 81 attribute variables and was compiled using the Kinect (v2) sensor Experimental data is used to compare the performance of independent CNN and LSTM networks.
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A. P. N and C. D. Guruprakash, "A Relay Node Scheme for Energy Redeemable and Network Lifespan Enhancement," 2018 4th International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Mangalore, India, 2018, pp. 266-274.
Achyutha Prasad, N., Guruprakash, C.D., 2019. A relay mote wheeze for energy saving and network longevity enhancement in WSN. International Journal of Recent Technology and Engineering 8, 8220–8227. doi:10.35940/ijrte.C6707.098319.
Achyutha Prasad, N., Guruprakash, C.D., 2019. A relay node scheme of energy redeemable and network lifespan enhancement for wireless sensor networks and its analysis with standard channel models. International Journal of Innovative Technology and Exploring Engineering 8, 605–612.
Achyutha Prasad, N., Guruprakash, C.D., 2019. A two hop relay battery aware mote scheme for energy redeemable and network lifespan improvement in WSN. International Journal of Engineering and Advanced Technology 9, 4785–4791. doi:10.35940/ijeat.A2204.109119.
Achyutha, P. N., Hebbale, S., & Vani, V. (2022). Real time COVID-19 facemask detection using deep learning. International Journal of Health Sciences, 6(S4), 1446–1462. https://doi.org/10.53730/ijhs.v6nS4.6231.
Agarwal, P.; Alam, M. A lightweight deep learning model for human activity recognition on edge devices. Procedia Comput. Sci.2020, 167, 2364–2373.
Akram Bayat, Marc Pomplun, Duc A. Tran, "A Study on Human Activity Recognition Using Accelerometer Data from Smartphones", ELSEVIER 2014.
Bo Tang, Jin Xu, Haibo Heand Hong Man," ADL: Active Dictionary Learning for Sparse Representation", IEEE 2017.
C. Ronao, and S. Cho. Human activity recognition with smartphone sensors using deep learning neural networks. Expert systems with applications 59 (2016): 235-244.
Christian Debes, Andreas Merentitis, Sergey Sukhanov,Maria Niessen, Nicolaos Frangiadakis, and Alexander Bauer, "Monitoring Activities of Daily Living in Smart Homes", IEEE 2016.
D. Anguita, et al. Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. International workshop on ambient assisted living. Springer, Berlin, Heidelberg, 2012.
Dr.Balakrishna R, Piyush Kumar Pareek et al, ’Study on Six Sigma approach to improve the quality of process outputs in business processes in Small & Medium Level Software Firms’ Springer AISC Series/ SCOUPS INDEXED JOURNAL, Paper Id : IT -221-ICPCIT2015 , June 2015.
Dr.Piyush Kumar Pareek et al, ‘A survey on approaches for predicting performance of students’,International Journal of Engineering Research and Science, ISSN No.2395-6992 Paper Id:IJOER-Jun-2016-25.
Dr.Piyush Kumar Pareek et al, ‘A survey on Long term product planning and requirements prioritization to customer value creation’, International Journal of Engineering Research and Science, ISSN No.2395-6992 Paper Id: IJOER-Jun-2016-27.
Dr.Piyush Kumar Pareek et al, ‘Education Data Mining –Perspectives of Engineering Students ’, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), ISSN: 2347-5552, Volume-4, Issue-5, September-2016.
Eunju Kim, Sumi Helal, and Diane Cook," Human Activity Recognition and Pattern Discovery", IEEE 2010.
F. Friedrich, M. Smeja, and J. Fahrenberg. Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring, Computers in human behavior 15.5 (1999): 571-583.
Hassan, M.M.; Uddin, Z.M.; Mohamed, A.; Almogren, A. A robust human activity recognition system using smartphone sensors and deep learning. Future Gener. Comput. Syst. 2018, 81, 307–313.
Hebbale, S., Marndi, A., Achyutha, P. N., Manjula, G., Mohan, B. R., & Jagadeesh, B. N. (2022). Automated medical image classification using deep learning. International Journal of Health Sciences, 6(S5), 1650–1667. https://doi.org/10.53730/ijhs.v6nS5.9153
Hebbale, S., Marndi, A., Manjunatha Kumar, B. H., Mohan, B. R. ., Achyutha, P. N., & Pareek, P. K. (2022). A survey on automated medical image classification using deep learning. International Journal of Health Sciences, 6(S1), 7850–7865. https://doi.org/10.53730/ijhs.v6nS1.6791
Jipeng, T., Neelagar, M. B., & Rekha, V. S. (2021). Design of an embedded control scheme for control of remote appliances. Journal of Advanced Research in Instrumentation and Control Engineering, 7(3 & 4), 5-8.
K. Nakano, and B. Chakraborty. Effect of dynamic feature for human activity recognition using smartphone sensors. 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST), IEEE, 2017.
Kalshetty, J. N., Achyutha Prasad, N., Mirani, D., Kumar, H., & Dhingra, H. (2022). Heart health prediction using web application. International Journal of Health Sciences, 6(S2), 5571–5578. https://doi.org/10.53730/ijhs.v6nS2.6479.
Lee, S.-M.; Yoon, S.M.; Cho, H. Human activity recognition from accelerometer data using convolutional neural network. In Proceedings of the IEEE International Conference on Big Data and Smart Computing (BigComp), Jeju, Korea, 13–16 February 2017; pp. 131–134.
Liming Chen, Member, IEEE, Jesse Hoey, Chris D. Nugent, Member, IEEE, Diane J. Cook, and Zhiwen Yu, "SensorBased Activity Recognition", IEEE, VOL. 42, NO. 6, NOVEMBER 2012.
Lin Fan, and Zhongmin Wang. (2014)” Human activity recognition model based on decision tree.” International Conference on Advance Cloud and Big Data, pp. 64-68.
M. Humayun Kabir, M. Robiul Hoque, Keshav Thapa, and Sung-Hyun Yang. (2016) “Two-Layer Hidden Markov Model for Human Activity Recognition in Home Environments.” International Journal of Distributed Sensor Networks, Volume: 12 issue: 1.
Meisam razaviyayn hung-wei tseng zhi-quan luo, "Dictionary learning for sparse representation: complexity and algorithms", IEEE international conference 2014.
Mr. Piyush Kumar Pareek, Dr. A. N. Nandakumar, ’Lean software development Survey on Agile and Lean usage in small and medium level firms in Bangalore’ , International Journal of Advanced Research in Computer Science and Software Engineering , Volume 4, Issue 12, December 2014 , ISSN: 2277 128X .pp 1-7 Impact Factor : 2.08.
Mr.Piyush Kumar Pareek, Dr. A. N. Nandakumar, ’Lean software development Survey on Benefits and challenges in Agile and Lean usage in small and medium level firms in Bangalore’ , International Journal of Advanced Research in Computer Science and Software Engineering , Volume 4, Issue 12, December 2014 , ISSN: 2277 128X .pp 1-11 Impact Factor : 2.08.
Murthy, R. K., Dhanraj, S., Manjunath, T. N., Achyutha, P. N., Prasad, A. N., & Gangambika, G. (2022). A survey on human activity recognition using CNN and LSTM. International Journal of Health Sciences, 6(S7), 3408–3417. https://doi.org/10.53730/ijhs.v6nS7.12479
N. A. Prasad and C. D. Guruprakash, "An ephemeral investigation on energy proficiency mechanisms in WSN," 2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Tumkur, 2017, pp. 180-185.
N. G and G. C. D, "Unsupervised Machine Learning Based Group Head Selection and Data Collection Technique," 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), 2022, pp. 1183-1190, doi: 10.1109/ICCMC53470.2022.9753995.
O´ scar D. Lara and Miguel A. Labrador, "A Survey on Human Activity Recognition using Wearable Sensors", IEEE communications surveys & tutorials, 2013.
Palwasha Afsar, Paulo Cortez, Henrique Santos, "Automatic Human Action Recognition from Video using Hidden Markov Model", IEEE 2015.
Piyush Kumar Pareek & Dr. A. N. Nandakumar, ’To Implement Lean software development frame- work for minimizing waste in terms of non-value added activities’, Research Publishing, Jain University ICISTSI-15 , Innovative Partners for Publishing Solutions, Singapore (May 2015).
Piyush Kumar Pareek & Dr.A.N.Nandakumar, ’Failure Mode Effective Analysis of Requirements Phase in small software Firms’, Paper ID: ICSTM/YMCA/2015/292, International Conference on Science, Technology and Management (ICSTM-2015). International Journal of Advance Research in Science and Engineering (IJARSE, ISSN- 2319-8354, Impact Factor- 1.142) [www.ijarse.com], Special Issue Jan2015.
Piyush Kumar Pareek & Dr.A.N.Nandakumar, ’Identifying Wastes in software, International Journal of Engineering Studies and Technical Approach’. January Issue 2015.
Piyush Kumar Pareek , Dr.Praveen Gowda , et al ’Ergonomics in a Foundry in Bangalore to improve productivity’,International Journal of Engineering and Social Science , ISSN: 2249- 9482 ,Volume 2,Issue 5 (May 2012) , pp 1-6.
Piyush Kumar Pareek , Dr.Praveen Gowda, et al ’FMEA Implementation in a Foundry in Ban- galore to Improve Quality and Reliability’,International Journal of Mechanical Engineering and Robotics Research, ISSN :2278-0149,Volume 1,Issue 2(June 2012),pp 81-87.
Piyush Kumar Pareek et al, ‘Survey on Challenges in Devops ’, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), ISSN: 2347-5552, Volume-4, Issue-5, September-2016.
Piyush Kumar Pareek, Dr. A. N. Nandakumar, et al ’Methodology and Functioning of Project Management Techniques in Agile Software Development Process’, International Journal of Research in IT, Management and Engineering, ISSN: 2249-1619, Volume2, Issue12 (December2012), pp 76-85.
Piyush Kumar Pareek, Dr. Vasanth Kumar S A , et al ’Reduction of Cycle Time By Implementation of a Lean Model Carried Out In a Manufacturing Industry’, International Journal of Engineering and Social Science , ISSN: 2249- 9482,Volume 2,Issue 5 (May 2012) , pp 114-123.
Piyush Kumar Pareek, Dr.Vasanth Kumar S A , et al ’Implementation of a Lean Model for Carrying out Value Stream Mapping in a Manufacturing Industry’, International Journal of Mechanical Engineering and Robotics Research, ISSN :2278-0149,Volume 1,Issue 2(June 2012),pp 88-95.
Prakash, N. C. P., Narasimhaiah, A. P., Nagaraj, J. B., Pareek, P. K., Maruthikumar, N. B., & Manjunath, R. I. (2022). Implementation of NLP based automatic text summarization using spacy. International Journal of Health Sciences, 6(S5), 7508–7521. https://doi.org/10.53730/ijhs.v6nS5.10574
Prakash, N. C., Narasimhaiah, A. P., Nagaraj, J. B., Pareek, P. K., Sedam, R. V., & Govindhaiah, N. (2022). A survey on NLP based automatic extractive text summarization using spacy. International Journal of Health Sciences, 6(S8), 1514–1525. https://doi.org/10.53730/ijhs.v6nS8.10526
Prasad N. Achyutha, Sushovan Chaudhury, Subhas Chandra Bose, Rajnish Kler, Jyoti Surve, Karthikeyan Kaliyaperumal, "User Classification and Stock Market-Based Recommendation Engine Based on Machine Learning and Twitter Analysis", Mathematical Problems in Engineering, vol. 2022, Article ID 4644855, 9 pages, 2022. https://doi.org/10.1155/2022/4644855.
Pubali De , Amitava Chatterjee, and Anjan Rakshit ," Recognition of Human Behavior for Assisted living using Dictionary Learning Approach", IEEE SENSORS JOURNAL, VOL. 18, NO. 6, MARCH 15, 2018.
R. V S and Siddaraju, "Defective Motes Uncovering and Retrieval for Optimized Network," 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), 2022, pp. 303-313, doi: 10.1109/ICCMC53470.2022.9754109.
Ramkrishna, S., Srinivas, C., Narasimhaiah, A. P., Muniraju, U., Maruthikumar, N. B., & Manjunath, R. I. (2022). A survey on blockchain security for cloud and IoT environment. International Journal of Health Sciences, 6(7), 28–43. https://doi.org/10.53730/ijhs.v6n7.10692
Rekha VS, Siddaraju., “An Ephemeral Analysis on Network Lifetime Improvement Techniques for Wireless Sensor Networks”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, issue 9, 2278-3075, pp. 810–814, 2019.
Rong liu, ting chen, lu huang," Research on human activity recognition based on active learning", IEEE 2010.
S. A. Taghanaki, K. Abhishek, J. P. Cohen, J. Cohen-Adad, and G. Hamarneh, “Deep semantic segmentation of natural and medical images: A review,” Artif. Intell. Rev., pp. 1–42, 2020.
S. Chaudhury, N. Achyutha Prasad, S. Chakrabarti, C. A. Kumar and M. A. Elashiri, "The Sentiment Analysis of Human Behavior on Products and Organizations using K-Means Clustering and SVM Classifier," 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), 2022, pp. 610-615, doi: 10.1109/ICIEM54221.2022.9853128
Sarah Fallmann and Johannes Kropf. (2016) “Human activity recognition of continuous data using Hidden Markov Models and the aspect of including discrete data.” Intl IEEE Conferences 121-126.
Shagun Shaily, Veenu Mangat, "The Hidden Markov Model and its Application to Human Activity Recognition", IEEE 2015.
Uriel Martinez-Hernandez, Imran Mahmood, and Abbas A. Dehghani-Sanij,"Simultaneous Bayesian Recognition of Locomotion and Gait Phases With Wearable Sensors", IEEE SENSORS JOURNAL, VOL. 18, NO. 3, FEBRUARY 1, 2018.
W. Wu, et al. Classification accuracies of physical activities using smartphone motion sensors. Journal of medical Internet research 14.5 (2012).
Xia, K.; Huang, J.; Wang, H. LSTM-CNN Architecture for Human Activity Recognition. IEEE Access 2020, 8, 56855–56866.
Y. Zhang, and K. M. Ramachandran. Human Activity Recognition with Streaming Smartphone Data. 2019 Global Conference for Advancement in Technology (GCAT). IEEE, 2019.
Yan Wang, Shuang Cang2, Hongnian Yu1, "A noncontactsensor surveillance system towards assisting independent living for older people", Research gate, December 2017.
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