A human activity recognition system based on convolutional neural networks to classify six activitieswalking, running, walking upstairs, walking downstairs, standing and sittingfrom accelerometer data is presented. IEEE, 473479. - 103.215.136.47. Recently, recognizing everyday life activities becomes one of the challenges for pervasive computing. In this work, we developed and evaluated algorithms for . 2017. Comput. arXiv preprint arXiv:1809.08113 (2018). Mingtao Dong, Jindong Han, Yuan He, and Xiaojun Jing. These keywords were added by machine and not by the authors. In Proceedings of the 24th International Joint Conference on Artificial Intelligence. Is attention interpretable? Technol. IEEE, 522529. IEEE Trans IndInf 16(7):46704680. Proc. Active deep learning for activity recognition with context aware annotator selection. In Proceedings of the IEEE International Conference on Big Data and Smart Computing (BigComp17). Activity Recognition is an emerging field of research, born from the larger fields of ubiquitous computing, context-aware computing and multimedia. Yiqiang Chen, Jindong Wang, Meiyu Huang, and Han Yu. Efficient dense labelling of human activity sequences from wearables using fully convolutional networks. Neurocomputing 171 (2016), 754767. Harideep Nair, Cathy Tan, Ming Zeng, Ole J. Mengshoel, and John Paul Shen. Kaixuan Chen, Lina Yao, Dalin Zhang, Bin Guo, and Zhiwen Yu. Elnaz Soleimani and Ehsan Nazerfard. Incorporating unsupervised learning in activity recognition. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence. SensoryGANs: An effective generative adversarial framework for sensor-based human activity recognition. 2010. Thus, it is aimed to capture the pattern inherent in the activity and due to preserving the continuous structure of the movement. 2010. IEEE, 7176. Subjects were asked to perform a sequence of everyday tasks but not told specifically where or how to do them. Int. Part of Springer Nature. Your file of search results citations is now ready. J. A. Krse. 2011. Remote Sens. In [4], [16], [17], where human activity recognition was performed using accelerometer data from one device, the authors learned feature maps for x-, y- and z-accelerometer channels separately that is similar to how an RGB image is typically processed by CNN. Cardiol. Making sense of spatio-temporal preserving representations for EEG-based human intention recognition. ACM, 5663. 1, 4 (2018), 157. Henry Friday Nweke, Ying Wah Teh, Mohammed Ali Al-Garadi, and Uzoma Rita Alo. Springer, Heidelberg (2002), Mantyjarvi, J., Himberg, J., Seppanen, T.: Recognizing human motion with multiple acceleration sensors. This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. Activity recognition ts within the bigger framework of context awareness. 2018. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. Unable to display preview. In: Proceedings of 2001 IEEE Conference on Control Applications (CCA 2001), pp. ACM SIGKDD Explor. A symbolic representation of time series, with implications for streaming algorithms. In 39th IEEE Conference on Computer Communications (INFOCOM20). B., eds., Seewald, A. K. 2002. Chen Chen, Roozbeh Jafari, and Nasser Kehtarnavaz. Action classification in soccer videos with long short-term memory recurrent neural networks. Belkacem Chikhaoui and Frank Gouineau. Springer, 649661. Researchers established lifelogging monitoring by using data from a wearable accelerometer and gyroscope [6, 7, 9].Lee et al. 1992. 3: Standing (standing). Towards multimodal deep learning for activity recognition on mobile devices. 2015. Activity and location recognition using wearable sensors. 2016. 2019. PubMedGoogle Scholar. In Proceedings of the IEEE International Conference on Big Data. Medical & Biological Engineering & Computing37(3), 304308 (1999), CrossRef IEEE Trans. 119 (2019), 311. In: 2020 Advanced computing and communication technologies for high performance applications (ACCTHPA), Cochin, India, pp 206210. 2016. Jeffrey C. Schlimmer and Richard H. Granger. In. C., and Muller, H. 2000. https://doi.org/10.1007/s00521-018-03973-1, Article ACM, 17631766. MathSciNet In Proceedings of the International Joint Conference on Neural Networks. 2023 Springer Nature Switzerland AG. IEEE, 698703. arXiv preprint arXiv:1706.01399 (2017). Alireza Abedin Varamin, Ehsan Abbasnejad, Qinfeng Shi, Damith C. Ranasinghe, and Hamid Rezatofighi. https://doi.org/10.1007/s11063-019-10040-w, Zhang W, Yan Z, Xiao G et al (2019) Learning distance metric for support vector machine: a multiple kernel learning approach. 2023 Springer Nature Switzerland AG. In Proceedings of the International Conference on Neural Information Processing. Neural Netw. The ACM Digital Library is published by the Association for Computing Machinery. 2019. 2018. 2018. 2015. Jun-Ho Choi and Jong-Seok Lee. In Proceedings of the International Conference on Acoustics, Speech and Signal Processing. In Proceedings of the ACM International Joint Conference and International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. International World Wide Web Conferences Steering Committee, 351360. Yuwen Chen, Kunhua Zhong, Ju Zhang, Qilong Sun, and Xueliang Zhao. Multicolumn bidirectional long short-term memory for mobile devices-based human activity recognition. A deep learning approach to on-node sensor data analytics for mobile or wearable devices. Smartphones are present in most peoples daily lives. IEEE Press, Los Alamitos (2002), Welk, G., Differding, J.: The utility of the Digi-Walker step counter to assess daily physical activity patterns. Time series classification using multi-channels deep convolutional neural networks. 2020. https://doi.org/10.1109/MM.2020.2974843, Tan HX, Aung NN, Tian J, Chua MCH, Yang YO (2019) Time series classification using a modified LSTM approach from accelerometer-based data: a comparative study for gait cycle detection. Activity recognition using dual-ConvLSTM extracting local and global features for SHL recognition challenge. Published under licence by IOP Publishing Ltd 2018. M.Eng. ProcediaComputSci 98:522527, Ravi N, Dandekar N, Mysore P, Littman ML (2005) Activity recognition from accelerometer data. 2019. Copyright 2023 ACM, Inc. Activity recognition from accelerometer data, Bao, L., and Intille, S. S. 2004. IEEE Access 7 (2019), 98939902. Performance Analysis of Deep Learning based Human Activity Recognition Methods Mst. In Proceedings of the 6th International Conference on Mobile Computing, Applications and Services. 1324. 2019. 143151. Exp. pp Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. Hangwei Qian, Sinno Jialin Pan, and Chunyan Miao. Gautham Krishna Gudur, Prahalathan Sundaramoorthy, and Venkatesh Umaashankar. ACM Interact., Mob., Wear. 15331540. 2016. DOI:https://doi.org/10.1145/2499621. Polytechnica IEEE Commun. 2016. ICST, 232235. IEEE Trans IndInf 16(12):74697478. 2004. arXiv preprint arXiv:1702.01638 (2017). Yue Zheng, Yi Zhang, Kun Qian, Guidong Zhang, Yunhao Liu, Chenshu Wu, and Zheng Yang. Fuqiang Gu, Kourosh Khoshelham, Shahrokh Valaee, Jianga Shang, and Rui Zhang. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. Ming Zeng, Tong Yu, Xiao Wang, Le T. Nguyen, Ole J. Mengshoel, and Ian Lane. Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, etal. Sojeong Ha, Jeong-Min Yun, and Seungjin Choi. Collecting complex activity datasets in highly rich networked sensor environments. Lett. IEEE Access 7 (2019), 9915299160. ACM, 246253. Avrim Blum and Tom M. Mitchell. The authors had declared no interest conflict. 2018. 2011 ). Ricardo Chavarriaga, Hesam Sagha, Alberto Calatroni, Sundara Tejaswi Digumarti, Gerhard Trster, Jos del R. Milln, and Daniel Roggen. AttriNet: Learning mid-level features for human activity recognition with deep belief networks. In: Ferscha, A., Mattern, F. (eds) Pervasive Computing. ACM Interact., Mob., Wear. Coll. Ming Zeng, Haoxiang Gao, Tong Yu, Ole J. Mengshoel, Helge Langseth, Ian Lane, and Xiaobing Liu. Human activity recognition from accelerometer data using Convolutional Neural Network. Multi-sensor mobile platform for the recognition of activities of daily living and their environments based on artificial neural networks. - 45.118.132.247. Rui Yao, Guosheng Lin, Qinfeng Shi, and Damith C. Ranasinghe. Protecting sensory data against sensitive inferences. This paper describes how to recognize certain types of human physical activities using acceleration data generated by a user's cell phone. Kaixuan Chen, Lina Yao, Dalin Zhang, Xianzhi Wang, Xiaojun Chang, and Feiping Nie. 2019. Akhil Mathur, Tianlin Zhang, Sourav Bhattacharya, Petar Velikovi, Leonid Joffe, Nicholas D. Lane, Fahim Kawsar, and Pietro Li. HAR is utilized in different applications where valuable information about an individual's functional ability and lifestyle is needed namely human . 2019. In this work, to face this challenge, we propose a convolutional neural network architecture along with two methods for transforming sensor data stream into images, and two recurrent neural networks, a long short time memory network and a gated recurrent unit network. Understanding and improving deep neural network for activity recognition. Proc. Whilst a low sampling rate saves considerable energy, as well as transmission bandwidth and storage capacity, it is also prone to omitting relevant signal details that are . With the spread of wearable sensors, the solutions to the task of activity recognition by using the data obtained from the sensors have become widespread. Movements are often typical activities performed indoors, such as walking, talking, standing, and sitting. IN: 2020 59th Annual conference of the society of instrument and control engineers of Japan (SICE), Chiang Mai, Thailand, 2020, pp 10161021, Paydarfar AJ, Prado A, Agrawal SK (2020) Human activity recognition using recurrent neural network classifiers on raw signals from insole piezoresistors. Ambulatory monitoring of behavior in daily life by accelerometers set at both-near-sides of the joint. Lett. Sensors 18:122. Lett. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. Ready for use: Subject-independent movement intention recognition via a convolutional attention model. Machine Learning, Intelligent data analysis and Data Mining Abdel-rahman Mohamed, George E. Dahl, and Geoffrey Hinton. Learn. In MacIntyre, B., and Iannucci. 59 (2016), 235244. 2016. IEEE Internet Things J. This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. Abstract and Figures Aiming at the problem of activity a recognition method based on a convolutional neural network was proposed in this papaer, which can effectively classify 6 types of human. In: 2016 IEEE SENSORS, Ordez F, Roggen D (2016) Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. IEEE Trans. This process is experimental and the keywords may be updated as the learning algorithm improves. Learn more about Institutional subscriptions, Tateno S, Meng F, Qian R, Li T (2020) Human motion detection based on low resolution infrared array sensor. Sensors 16(1):115, Hassan MM, Uddin MdZ, Mohamed A, Almogren A (2018) A robust human activity recognition system using smartphone sensors and deep learning. Inf. 2016. 56145620. Yuta Yuki, Junto Nozaki, Kei Hiroi, Katsuhiko Kaji, and Nobuo Kawaguchi. By continuing to use this site you agree to our use of cookies. Deep dilated convolution on multimodality time series for human activity recognition. 2018. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI19). IEEE Access 8:6832068332. Proc. 2018. 33. 2018. In Proceedings of the IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN15). Cascade and parallel convolutional recurrent neural networks on EEG-based intention recognition for brain computer interface. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers. In Proceedings of the ACM International Symposium on Wearable Computers. In Proceedings of the 12th IEEE International Symposium on Wearable Computers. 2016. Comput. Experiments with a new boosting algorithm. 2014. In Proceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction. IEEE, 15. Yusuke Iwasawa, Kotaro Nakayama, Ikuko Yairi, and Yutaka Matsuo. In Proceedings of ICML Workshop on Unsupervised and Transfer Learning. Syst. 2, 2 (2018), 74. In Proceedings of the IEEE International Symposium on Signal Processing and Information Technology (ISSPIT18). ACM, 13071310. In Proceedings of the 4th International Conference on Learning Representations Workshop. In Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare. Measurement 163:107964, ISSN 0263-2241. https://doi.org/10.1016/j.measurement.2020.107964, Li Z, Li C, Li S, Cao X (2020) A fault-tolerant method for motion planning of industrial redundant manipulator. IEEE Press, 200211. 4956. Easy handling, affordable price, and respect for user privacy have led to the widespread use of wearable IMU sensors in products (Hou, 2020).Many researches have used raw data from these sensors to train machine learning methods while it has also been . Fully convolutional networks for semantic segmentation. AROMA: A deep multi-task learning based simple and complex human activity recognition method using wearable sensors. These results are better than others previously published in the literature with the same dataset. In the remaining discussion, we refer to the problem of HAR exclusively as the recog-nition of activities from sensor data through the use of machine learning models. Convolutional neural networks for human activity recognition using mobile sensors. In. IEEE, 708715. Multimodal deep learning for activity and context recognition. https://doi.org/10.1007/s41050-021-00028-8, DOI: https://doi.org/10.1007/s41050-021-00028-8. Combining labeled and unlabeled data with Co-Training. 2018. Surv. Dalin Zhang, Kaixuan Chen, Debao Jian, and Lina Yao. 2019. In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN16). Ensembles of deep LSTM learners for activity recognition using wearables. Acoustic modeling using deep belief networks. A tutorial on human activity recognition using body-worn inertial sensors. 2019. Allan Stisen, Henrik Blunck, Sourav Bhattacharya, Thor Siiger Prentow, Mikkel Baun Kjrgaard, Anind Dey, Tobias Sonne, and Mads Mller Jensen. Incorporating unsupervised learning in activity recognition. 2014. Privacy-preserving collaborative deep learning with application to human activity recognition. 2018. Detecting unseen falls from wearable devices using channel-wise ensemble of autoencoders. DeepSense: Device-free human activity recognition via autoencoder long-term recurrent convolutional network. We use cookies to ensure that we give you the best experience on our website. 2018. 2017. 2019. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. R, and Dunn, S. 2001. 57805786. Audio, Speech, Lang. IEEE, 197205. Proc. Google Scholar, Tatbul N, Lee TJ, Zdonik S, Alam M, Gottschlich J (2018) Precision and recall for time series. Imagenet large scale visual recognition challenge. Priyantha, N. B.; Chakraborty, A.; and Balakrishnan, H. 2000. ACM, 14. Check if you have access through your login credentials or your institution to get full access on this article. Harter. Self-gated recurrent neural networks for human activity recognition on wearable devices. 13441350. The results show that although some activities are recognized well with subject-independent training data, others appear to require subject-specific training data. LabelForest: Non-parametric semi-supervised learning for activity recognition. In Proceedings of the 7th International Conference on Networked Sensing Systems (INSS10). Ubiq. Language generation with recurrent generative adversarial networks without pre-training. Andrej Karpathy, Justin Johnson, and Li Fei-Fei. A survey on deep learning: Algorithms, techniques, and applications. Syst. Proc AAAI ConfArtifIntell 33(01):14091416, Ramirez A, Iriarte J (2019) Event recognition on time series frac data using machine learning. Jessica Lin, Eamonn Keogh, Stefano Lonardi, and Bill Chiu. Technical report, MIT Media Laboratory (2001), Foerster, F., Smeja, M., Fahrenberg, J.: Detection of posture and motion by accelerometry: a validation in ambulatory monitoring.
Superior 1 2 Hp Submersible Pump, Versace Dylan Blue Pour Femme, Importers Of Surgical Instruments In Germany, Collins Diary Week To View, Articles A