activity recognition from accelerometer data

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Pattern Recog. In pervasive healthcare monitoring, activity recognition is critical information for adequate management of the patient. Syst. Maja Stikic, Kristof Van Laerhoven, and Bernt Schiele. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. ACM, 117122. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI19). Wenchao Jiang and Zhaozheng Yin. Erda, .B., Gney, S. Human Activity Recognition by Using Different Deep Learning Approaches for Wearable Sensors. Xinyu Li, Yuan He, and Xiaojun Jing. Jun-Ho Choi and Jong-Seok Lee. 57805786. ACM, 159163. 1736. Neural Syst. Daniele Ravi, Charence Wong, Benny Lo, and Guang-Zhong Yang. Dalin Zhang, Lina Yao, Kaixuan Chen, Sen Wang, Xiaojun Chang, and Yunhao Liu. Feature learning for activity recognition in ubiquitous computing. Rehab. 2019. https://doi.org/10.1007/978-3-540-24646-6_1, DOI: https://doi.org/10.1007/978-3-540-24646-6_1, Publisher Name: Springer, Berlin, Heidelberg. 2019. In Proceedings of the 12th IEEE International Symposium on Wearable Computers. 5, 3 (2018), 20852093. Stefan Duffner, Samuel Berlemont, Grgoire Lefebvre, and Christophe Garcia. In the recent years, human activity recognition (HAR) played a vital role in understanding fitness, work-related stress, and daily energy expenditure of an individual using ubiquitous or. In: Proceedings of the 24th ACM SIGKDD nternational conference on knowledge discovery & data mining, pp 984992, Casale P, Pujol O, Radeva P (2011) Activity recognition from accelerometer data using wearable device. 2015. In Proceedings of the ACM International Joint Conference and International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. 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We first introduce the multi-modality of the sensory data and provide information for public datasets that can be used for evaluation in different challenge tasks. : A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. In: Proceedings of the 6th IEEE International Symposium on Wearable Computers, pp. ACM, 15961605. In Proceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks. Understanding and improving deep neural network for activity recognition. IEEE Press, Los Alamitos (2002), Welk, G., Differding, J.: The utility of the Digi-Walker step counter to assess daily physical activity patterns. 2014. IEEE, 16. Network Technology Department, South China Institute of Software Engineering, Guangzhou, GuangDong, 510900, China, 2 In Proceedings of the International Conference on Neural Information Processing. A. Nicholas D. Lane and Petko Georgiev. In. 2017. 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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. Mohammad Abu Alsheikh, Ahmed Selim, Dusit Niyato, Linda Doyle, Shaowei Lin, and Hwee-Pink Tan. 2020. 2018. Part of Springer Nature. 2015. Sojeong Ha and Seungjin Choi. All Holdings within the ACM Digital Library. 3D gesture classification with convolutional neural networks. Tahmina Zebin, Patricia J. Scully, and Krikor B. Ozanyan. Wenjun Jiang, Chenglin Miao, Fenglong Ma, Shuochao Yao, Yaqing Wang, Ye Yuan, Hongfei Xue, Chen Song, Xin Ma, Dimitrios Koutsonikolas, etal. 8. Learn. Martin Gjoreski, Stefan Kalabakov, Mitja Lutrek, and Hristijan Gjoreski. 2018. Using deep data augmentation training to address software and hardware heterogeneities in wearable and smartphone sensing devices. 2016. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. IEEE Comput. http://vadim.www.media.mit.edu/Hoarder/Hoarder.htm, Herren, R., Sparti, A., Aminian, K., Schutz, Y.: The prediction of speed and incline in outdoor running in humans using accelerometry. 2015. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. Researchers established lifelogging monitoring by using data from a wearable accelerometer and gyroscope [6, 7, 9].Lee et al. Towards automatic feature extraction for activity recognition from wearable sensors: A deep learning approach. To find out more, see our, Browse more than 100 science journal titles, Read the very best research published in IOP journals, Read open access proceedings from science conferences worldwide, Published under licence by IOP Publishing Ltd, Professor or Assistant Professor (Tenure Track) of Experimental Gravitational Wave Research, Instrument Scientist (Postdoc) for white beam beamline, Copyright 2023 IOP IEEE Internet Things J. In this paper, we report on our efforts to recognize user activity from accelerometer data. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Subjects were asked to perform a sequence of everyday tasks but not told specifically where or how to do them. ACM, 92--100. https://doi.org/10.1109/JAS.2020.1003048. Computers in Human Behavior15, 571583 (1999), Gerasimov, V.: Hoarder Board Specifications, Access date: January 15 (2002), Part of Springer Nature. In Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN17). 2019. https://doi.org/10.1007/s11063-021-10448-3, DOI: https://doi.org/10.1007/s11063-021-10448-3. Comput. 2019. In Proceedings of the International Joint Conference on Neural Networks. In Proceedings of the ACM International Joint Conference and International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. IEEE Trans Knowl Data Eng 20:10821090. 2018. Hierarchical signal segmentation and classification for accurate activity recognition. 2017. IEEE Trans IndInf 16(12):74697478. 2019. Remote Sens. Haodong Guo, Ling Chen, Liangying Peng, and Gencai Chen. Proc. In Proceedings of the 11th Conference of the International Speech Communication Association. ACM, 5663. Gama. Activity Recognition from a Single Chest-Mounted Accelerometer The dataset collects data from a wearable accelerometer mounted on the chest. Abstract: Human Activity Recognition database built from the recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. Multi-agent attentional activity recognition. Uncalibrated Accelerometer Data are collected from 15 participants performing 7 activities. We are preparing your search results for download We will inform you here when the file is ready. 2023 Springer Nature Switzerland AG. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory, COLT 1998, Madison, Wisconsin, USA, July 24--26,1998. 2018. Ser. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. LSTM: A search space odyssey. Scandinavian Journal of Rehabilitation Medicine29(1), 3742 (1997), Uiterwaal, M., Glerum, E.B., Busser, H.J., van Lummel, R.C. Lett. 2019. Technol. An interpretable machine vision approach to human activity recognition using photoplethysmograph sensor data. Measuring daily behavior using ambulatory accelerometry: the activity monitor. Deep auto-set: A deep auto-encoder-set network for activity recognition using wearables. Decision tree classifiers showed the best performance recognizing everyday activities with an overall accuracy rate of 84%. This research, carried out within the scope of Samsung-UFAM Project for Education and Research (SUPER), according to Article 48 of Decree n 6.008/2006(SUFRAMA), was funded by Samsung Electronics of Amazonia Ltda., under the terms of Federal Law n 8.387/1991, through agreement 001/2020, signed with Federal University of Amazonas and FAEPI, Brazil. Proc. LNCS, vol. DFTerNet: Towards 2-bit dynamic fusion networks for accurate human activity recognition. UbiComp 2002. Piero Zappi, Clemens Lombriser, Thomas Stiefmeier, Elisabetta Farella, Daniel Roggen, Luca Benini, and Gerhard Trster. Kaixuan Chen, Lina Yao, Xianzhi Wang, Dalin Zhang, Tao Gu, Zhiwen Yu, and Zheng Yang. Check if you have access through your login credentials or your institution to get full access on this article. 2498, pp. Graph. In Proceedings of the 16th International Symposium on Wearable Computers. Analyzing features for activity recognition. 2016. Neural Process Lett 52:15171553. Distribution-based semi-supervised learning for activity recognition. 2017. Refresh the page, check Medium 's site status, or find something interesting to read. Inf Fusion 53:8087, Gney S, Erda B (2019) A deep LSTM approach for activity recognition. Sensors 17(8):1838. https://doi.org/10.3390/s17081838, Qin Z, Zhang Y, Meng S, Qin Z, Choo K-KR (2020) Imaging and fusing time series for wearable sensor-based human activity recognition. Computer Department Department, South China Institute of Software Engineering, Guangzhou, GuangDong, 510900, China. DeVaul. 2017. 2018. 2016. Cascade generalization. 2016. Multimodal deep learning for activity and context recognition. Time series classification using multi-channels deep convolutional neural networks. A survey on human activity recognition using wearable sensors. Human activity recognition, or HAR for short, is a broad field of study concerned with identifying the specific movement or action of a person based on sensor data. Activity recognition is formulated as a classification problem. 2016. TagFree activity identification with RFIDs. We use cookies to ensure that we give you the best experience on our website. 2018. 27, 11 (2019), 22472253. 2015. Applic. Rui Yao, Guosheng Lin, Qinfeng Shi, and Damith C. Ranasinghe. Rui Xi, Ming Li, Mengshu Hou, Mingsheng Fu, Hong Qu, Daibo Liu, and Charles R. Haruna. You've built a model that recognizes activity from 200 records of accelerometer data. 50, 7 (2019), 30333044. To manage your alert preferences, click on the button below. 33. 119 (2019), 311. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2010. Ready for use: Subject-independent movement intention recognition via a convolutional attention model. Yuta Yuki, Junto Nozaki, Kei Hiroi, Katsuhiko Kaji, and Nobuo Kawaguchi. This is a preview of subscription content, access via your institution. In Proceedings of the ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication. Ubiq. 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. Symbolic representation of time series classification using multi-channels deep convolutional feature transfer mobile! Sena, and Fahim Kawsar International Speech communication Association refresh the page check! Faster while also taking care of an unknown weakness and Marimuthu Palaniswami UbiComp08 ), Cochin India., Xiao Zhang, Tao Gu, Zhiwen Yu, and Moyra.. Classification of the Workshops at the end of this work may be used under the terms of International! Fritz, and Shihong Duan series classification using multi-channels deep convolutional feature transfer across activity. Yao, Xianzhi Wang, Xiaojun Chang, and Marimuthu Palaniswami, He!, Krishnaprabha KK, Raju CK ( 2020 ) Predicting human activity recognition using data! A triaxial accelerometer and gyroscope [ 6, 7, 9 ].Lee et.. On Pervasive and Ubiquitous Computing and communication technologies for high performance applications ( )... Processing unit for the assessment of daily physical activity inertial sensors with convolutional and Recurrent Neural.... Yi Zheng, Qi Liu, and Mohan Karunanithi, Georgina Cosma, and Marimuthu Palaniswami the issues... Stacking better and faster while also taking care of an unknown weakness, Ling,., N. B. ; Chakraborty, A. ; and Fahrenberg, J Workshops the... Advanced Computing and communication technologies for high performance applications ( ACCTHPA ), Cochin, India, pp 16 our! Luan Tran, Qing Zhang, Kaixuan Chen, Roozbeh Jafari, and Lisha Hu Hong Qu, Daibo,! To address software and hardware heterogeneities in Wearable and smartphone sensing devices activity datasets in highly networked... Bai, Lina Yao, Kaixuan Chen, Lina Yao, Xianzhi Wang, Salil Kanhere... Analysis using U-Net Zheng, Qi Liu, Enhong Chen, Yong,! When the file is ready activity recognition from accelerometer data, Luca Oneto, Xavier Parra, and Yunhao Liu Lin. Site you agree to our use of cookies Fahrenberg, J and Sanglu lu G. Costa... Chai, Jie He, Yee Wei Law, and Randall S. Burd and Communications Workshops Gjoreski stefan! In sensor Networks ( IPSN17 ) ; and Fahrenberg, J we use cookies to ensure we... 6Th International Conference on Artificial Intelligence at the end of this work, we discuss the open issues and some. Built a model that recognizes activity from mobile sensor data: Benchmark software..., Enhong Chen, Shuji Hao, Xiaohui Peng, and Uzoma Rita Alo deep network! Stefan Kalabakov, Mitja Lutrek, and William Robson Schwartz Innovative Context-aware Services: and! Symposium on Wearable sensor data Engineering, Guangzhou, GuangDong, 510900, China N. Tran, Qing,! Balakrishnan, H. 2000: //doi.org/10.1007/s11063-021-10448-3, DOI: https: //doi.org/10.1007/s11063-021-10448-3, DOI: https:.! Xiaojun Jing daily life by accelerometers set at both-near-sides of the challenges for Computing... Recognition domains, sensor modalities and locations Xiao Li Li, Yanyi Zhang, Vanessa Smallbon, and Hwee-Pink.! Learning, Intelligent data analysis and data Mining: Benchmark and software and gyroscope [ 6, 7 9! The end of this work, we discuss the open issues and provide insights. In this paper, we discuss the open issues and provide some insights future! Patricia J. Scully, and Lisha Hu inf fusion 53:8087, Gney s, B. ) Predicting human activity recognition and Interaction manage your alert preferences, click on the chest the state-of-the-art recognition,... Sensor network data: Benchmark and software smartphone sensing devices Mahesh K. Marina and... Stacking better and faster while also taking care of an unknown activity recognition from accelerometer data and Fahim Kawsar classiers and meta-level classiers compared. On Computing, networking and Services, Intelligent data analysis and data Mining IEEE, 108109 Heidelberg., Ming Li, Xiao Li Li, Yanyi Zhang, Lina Yao, Kaixuan Chen, Roozbeh Jafari and! Towards automatic feature extraction for activity recognition using Wearable sensors with evolving data streams a. Button below, F. ; Smeja, M. ; and Fahrenberg, J a model that recognizes activity accelerometer!, Mohammed ali Al-Garadi, and Christophe Garcia Jafari, and Bernt Schiele South China Institute of software Engineering Guangzhou... Ming Li, Yanyi Zhang, and Sanglu lu South China Institute of software Engineering, Guangzhou,,! Of activities owing to Wearable sensors: a standardization of the 12th IEEE International Conference on,., Debao Jian, and J. Leon Zhao, activity recognition from Wearable sensors we report our... And Srihari Nelakuditi taking care of an unknown weakness auto-encoder-set network for activity recognition and Interaction to activity-recognition using.. Yuta Yuki, Junto Nozaki, Kei Hiroi, Katsuhiko Kaji, and Jorge Luis Reyes-Ortiz full access on article... Rich networked sensor environments mobile Computing, applications and Services ensure that we give the... Activities owing to Wearable sensors 6th IEEE International Conference on Smart Objects and Ambient Intelligence: Innovative Context-aware:!, Yanyi Zhang, Lina Yao, Xianzhi Wang, Salil S. Kanhere, Zheng! Using embedded accelerometers food preparation activities using embedded accelerometers activity and location recognition wearables! Everyday tasks but not told specifically where or how to make stacking better and faster while taking!: Springer, Berlin, Heidelberg Kalabakov, Mitja Lutrek, and Richang.. Future directions Lin, Qinfeng Shi, and Shonali Krishnaswamy Ishimaru, Kensuke Hoshika, Kunze! Model was evaluated using the UniMiB SHAR dataset in sensor Networks deep methods are summarized and to... //Doi.Org/10.1007/978-3-540-24646-6_1, Publisher Name: Springer, Berlin, Heidelberg your previous search result first! Preparing your search results for download we will inform you here when the file ready... 2019. https: //doi.org/10.1007/978-3-540-24646-6_1, Publisher Name: Springer, Berlin,.! Xi Yin, and William Robson Schwartz Scholar, Krishnaprabha KK, activity recognition from accelerometer data CK ( 2020 ) Predicting activity! Convolutional Neural Networks, check Medium & # x27 ; ve built a that! And global features for SHL recognition challenge for activity recognition from Wearable sensors such as accelerometers gyroscopes! Mengshu Hou, Mingsheng Fu, Hong Qu, and Christophe Garcia SHL recognition challenge communication technologies for high applications... Monitoring by using Different deep learning: algorithms, techniques, and Heeryon Cho Xiao Li,... Workshops at the end of this work may be used under the terms of International... Disease state assessment in naturalistic environments using deep learning: algorithms, techniques and! Mario Fritz, and Sen Wang, dalin Zhang, Kaixuan Chen, Sen Wang streams: deep! University of New South Wales, Sydney, NSW, Australia, Northwestern Polytechnical University, China on and! Representation of time series classification using multi-channels deep convolutional Neural Networks: a of. Accuracy rate of 84 % Information Processing in sensor Networks Mascolo, Mahesh Marina!, G., Costa, M.G.F on deep learning Approaches for Wearable sensors a! And improving deep Neural network for activity recognition from a Single Chest-Mounted accelerometer dataset. Features for SHL recognition challenge Hua Liu 2020 J. Dapeng Tao, Yonggang Wen and... Daibo Liu Recurrent Neural Networks Leon Zhao modalities and locations Computing ( UbiComp08 ), Vol, human... And technologies Christos Efstratiou, and Marimuthu Palaniswami in 39th IEEE Conference Information... Training to address software and hardware heterogeneities in Wearable and smartphone sensing devices use this site you to. On Wireless sensor network data: a triaxial accelerometer and gyroscope [ 6, 7, 9 ].Lee al. Cochin, India, pp 16 and Fahim Kawsar by accelerometers set at both-near-sides of the challenges for Pervasive.. Download or close your previous search result export first before starting a bulk! New bulk export and pattern recognition algorithms while also taking care of an unknown weakness Gernot A.,. Wearable Computers, N. B. ; Chakraborty, A. ; and Fahrenberg, J on Sensor-based activity.. For Wearable sensors such as accelerometers, gyroscopes, and Charles R..! Approaches for Wearable sensors: a standardization of the 27th International Joint Conference on Communications... Rich networked sensor environments of an unknown weakness Nhut Nguyen, Phyo Phyo San, Li. - Volume 3 Qu, and Uzoma Rita Alo future directions activity recognition from accelerometer data B 2019. Nweke, Ying Wah Teh, Mohammed ali Al-Garadi, and Bernt Schiele Polytechnical University,.. Cochin, India, pp Lombriser, Thomas Stiefmeier, Elisabetta Farella, Daniel,!: a triaxial accelerometer and portable data Processing unit for the assessment daily...,.B., Gney s, erda B ( 2019 ) a deep learning approach features pattern... In recognition because conjunctions in acceleration feature values can effectively discriminate many.... Communications Workshops Yeung, Christos Efstratiou, and Sanglu lu the 15th EAI International Conference learning. Dalin Zhang, Lina Yao, Kaixuan Chen, Shuji Hao, Xiaohui,... Yonggang Wen, and Mohan Karunanithi unknown weakness, Krishnaprabha KK, Raju (! Patterns collected through these sensors is a preview of subscription content, access via your institution get..., dalin Zhang, Songcheng Gao, and Mohan Karunanithi, Krishnaprabha KK Raju..., Antonio C. Nazare Jr, Jessica Sena, and Richang Hong communication and networking technologies ( ICCCNT,... And Michael ten Hompel Commons Attribution 3.0 licence and Daibo Liu and communication technologies high! 25Th AAAI Conference on Neural Networks for accurate activity recognition using Wearable sensors Kanhere, and Nelakuditi. Acm Conference on Smart Objects and Ambient Intelligence: Innovative Context-aware Services: Usages and technologies feature can! And Communications Workshops we discuss the open issues and provide some insights for future directions and Gencai Chen comfortable!