Newslett. In: European Symposium on Ambient Intelligence, EUSAI (2003), Krause, A., Siewiorek, D.P., Smailagic, A., Farringdon, J.: Unsupervised, dynamic identification of physiological and activity context in wearable computing. Dzeroski, S., and Zenko, B. 20, 1 (2011), 1422. Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Refresh the page, check Medium 's site status, or find something interesting to read. http://vadim.www.media.mit.edu/Hoarder/Hoarder.htm, https://doi.org/10.1007/978-3-540-24646-6_1, Shipping restrictions may apply, check to see if you are impacted, Tax calculation will be finalised during checkout. AccelPrint: Imperfections of accelerometers make smartphones trackable. 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 International Conference on Artificial Neural Networks. IEEE Trans IndInf 16(12):74697478. 50, 7 (2019), 30333044. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers. A public domain dataset for human activity recognition using smartphones. 2018. 2017. 2013. 2018. In: Proceedings of CHI 2004 Connect: Conference on Human Factors in Computing Systems, ACM Press, New York (2004), Kern, N., Schiele, B., Schmidt, A.: Multi-sensor activity context detection for wearable computing. 2020. Proc. Biomed. IEEE, 15. Artur Jordao, Antonio C. Nazare Jr, Jessica Sena, and William Robson Schwartz. 2010. Marc Bachlin, Meir Plotnik, Daniel Roggen, Inbal Maidan, Jeffrey M. Hausdorff, Nir Giladi, and Gerhard Troster. Deep convolutional neural networks for human activity recognition with smartphone sensors. DOI 10.1088/1742-6596/1550/3/032161, 1 2018. In Proceedings of the International Conference on Communications (ICC18). In Proceedings of the ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication. Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units. LSTM networks for mobile human activity recognition. https://doi.org/10.1007/s11063-020-10364-y, Tran DP, Hoang VD (2019) Adaptive learning based on tracking and reidentifying objects using convolutional neural network. 2019. Makikawa, M.; Kurata. Harter. Google Scholar, Krishnaprabha KK, Raju CK (2020) Predicting human activity from mobile sensor data using CNN architecture. The ternary activity recognition performance of the 1D CNN-based method showed 92.71% accuracy outperformed the baseline random forest approach of 89.10%. Jessica Lin, Eamonn Keogh, Stefano Lonardi, and Bill Chiu. 39, 2 (2019), 1423. Lei Bai, Lina Yao, Xianzhi Wang, Salil S. Kanhere, and Yang Xiao. 21, 1 (2016), 5664. In Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications. An automatic detection and recognition of different activities using just one axis from an accelerometer sensor, and simple features and pattern matching algorithm leading to computationally inexpensive and memory efficient system suitable for resource-constrained wearable devices is described. Part of Springer Nature. Lina Yao, Feiping Nie, Quan Z. Sheng, Tao Gu, Xue Li, and Sen Wang. Privacy issues regarding the application of DNNs to activity-recognition using wearables and its countermeasures by use of adversarial training. 33213328. : Measuring daily behavior using ambulatory accelerometry: the Activity Monitor. Medical & Biological Engineering & Computing37(3), 304308 (1999), CrossRef Donald S. Baim, Wilson S. Colucci, E. Scott Monrad, Harton S. Smith, Richard F. Wright, Alyce Lanoue, Diane F. Gauthier, Bernard J. Ransil, William Grossman, and Eugene Braunwald. A symbolic representation of time series, with implications for streaming algorithms. In. IEEE Pervasive Computing1(3), 2432 (2002), Lukowicz, P., Junker, H., Stager, M., Buren, T.V., Troster, G.: WearNET: a distributed multi-sensor system for context aware wearables. The cricket location-support system. LSTM: A search space odyssey. Efficient dense labelling of human activity sequences from wearables using fully convolutional networks. Andrej Karpathy, Justin Johnson, and Li Fei-Fei. In Proceedings of the International Conference on Neural Information Processing. Xinyu Li, Yuan He, and Xiaojun Jing. 2005. Ubiq. R, and Dunn, S. 2001. Deep dilation on multimodality time series for human activity recognition. 2016. In Proceedings of the International Joint Conference on Neural Networks. Department of Computer Science, Rutgers University, Piscataway, NJ. Real-world deployments of accelerometer-based human activity recognition systems need to be carefully configured regarding the sampling rate used for measuring acceleration. 2010. 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. In Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AgeingWell16). SensoryGANs: An effective generative adversarial framework for sensor-based human activity recognition. Springer, 298310. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI19). 2019. Exploring semi-supervised and active learning for activity recognition. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers. 2018. Privacy-preserving collaborative deep learning with application to human activity recognition. Springer, Heidelberg (2002), Mantyjarvi, J., Himberg, J., Seppanen, T.: Recognizing human motion with multiple acceleration sensors. 4, 2 (2020), 122. Cardiol. Machine Learning, Intelligent data analysis and Data Mining In Proceedings of the CHI Conference on Human Factors in Computing Systems. IEEE, 131134. In: IEEE Micro, vol 40, no 2, pp 816, 1 MarchApril. Journal of Medical Engineering & Technology22(4), 168172 (1998), Van Laerhoven, K., Cakmakci, O.: What shall we teach our pants? Jonathan Long, Evan Shelhamer, and Trevor Darrell. In Proceedings of the IEEE International Conference on Computer and Information Technology (CIT16). 57805786. 2019. Han Zou, Yuxun Zhou, Jianfei Yang, Hao Jiang, Lihua Xie, and Costas J. Spanos. 2018. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. Eoin Brophy, Jos Juan Dominguez Veiga, Zhengwei Wang, Alan F. Smeaton, and Tomas E. Ward. How to make stacking better and faster while also taking care of an unknown weakness. Learn. In. Why does unsupervised pre-training help deep learning? Technical report, MIT Media Laboratory (2001), Foerster, F., Smeja, M., Fahrenberg, J.: Detection of posture and motion by accelerometry: a validation in ambulatory monitoring. IEEE, 566--575. University of New South Wales, Sydney, NSW, Australia, Northwestern Polytechnical University, China. arXiv preprint arXiv:1706.01399 (2017). 2020. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. Health Inform. Akhil Mathur, Tianlin Zhang, Sourav Bhattacharya, Petar Velikovi, Leonid Joffe, Nicholas D. Lane, Fahim Kawsar, and Pietro Li. Technol. Hierarchical signal segmentation and classification for accurate activity recognition. Lecture Notes in Computer Science, vol 11730. 2017. 15, 3 (2013), 11921209. ACM, 158165. https://doi.org/10.1007/s41050-021-00028-8, https://doi.org/10.1007/s00521-018-03973-1, https://doi.org/10.1109/ACCESS.2020.2986246, https://doi.org/10.1038/s41598-019-47765-6, https://doi.org/10.1007/s11390-011-9430-9. ACM, 10361043. Recurrent neural network based language model. Part of Springer Nature. In Proceedings of the International Conference on Web-age Information Management. - 159.65.154.158. Sensor-based activity recognition via learning from distributions. Zero-effort cross-domain gesture recognition with Wi-Fi. In Proceedings of the International Conference on Pervasive Computing and Communications. Activity recognition is performed based on information received from different sensors (Yang et al. IEEE Access 8:179028179038. Fully convolutional networks for semantic segmentation. 8. Shinya Matsui, Nakamasa Inoue, Yuko Akagi, Goshu Nagino, and Koichi Shinoda. Push the limit of acoustic gesture recognition. In the present study, we investigated the potential advantage of coupling activity and intensity, namely, Activity-Intensity, in . 2019. Proc AAAI ConfArtifIntell 33(01):14091416, Ramirez A, Iriarte J (2019) Event recognition on time series frac data using machine learning. In Proceedings of ICML Workshop on Unsupervised and Transfer Learning. Multi-modal convolutional neural networks for activity recognition. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Gierad Laput and Chris Harrison. Imagenet classification with deep convolutional neural networks. TagFree activity identification with RFIDs. ACM, 704711. 2018. The best macro average accuracy for classification of 17 types of activities, with 5-fold-cross-validation method, 95.49% was obtained with a gated recurrent unit network. 2016. Aminian, K., Robert, P., Buchser, E.E., Rutschmann, B., Hayoz, D., Depairon, M.: Physical activity monitoring based on accelerometry: validation and comparison with video observation. In Proceedings of the ACM on Conference on Information and Knowledge Management. https://doi.org/10.1109/ACCESS.2020.2986246, Micucci D, Mobilio M, Napoletano P (2017) UniMiB SHAR: a dataset for human activity recognition using acceleration data from smartphones. 2012. Cascade generalization. IEEE Press, Los Alamitos (2002), Welk, G., Differding, J.: The utility of the Digi-Walker step counter to assess daily physical activity patterns. ProcediaComputSci 98:522527, Ravi N, Dandekar N, Mysore P, Littman ML (2005) Activity recognition from accelerometer data. Supplemental movie, appendix, image and software files for, Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges, and Opportunities, Communication hardware, interfaces and storage. Sensors 18:122. 2017. ICST, 232235. We propose a one-dimensional (1D) Convolutional Neural Network (CNN)-based method for recognizing human activity using triaxial accelerometer data collected from users' smartphones. 2018. In: 2009 21st IEEE nternational conference on tools with artificial ntelligence, Newark, NJ, pp 5966. Behavior Research Methods, Instruments, & Computers33(3), 349356 (2001), Chambers, G.S., Venkatesh, S., West, G.A.W., Bui, H.H. CarA deep learning structure for concurrent activity recognition. Chihiro Ito, Xin Cao, Masaki Shuzo, and Eisaku Maeda. Ofir Press, Amir Bar, Ben Bogin, Jonathan Berant, and Lior Wolf. Shoya Ishimaru, Kensuke Hoshika, Kai Kunze, Koichi Kise, and Andreas Dengel. Although there are several applications in the literature, differently in this study, deep learning algorithms such as Convolutional Neural Networks, Convolutional LSTM, and 3D Convolutional Neural Networks fed by Convolutional LSTM have been used in human activity recognition task by feeding with data obtained from accelerometer sensor. IEEE, 18. Is combining classifiers with stacking better than selecting the best one? Context awareness by analysing accelerometer data. In Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems. Towards automatic feature extraction for activity recognition from wearable sensors: A deep learning approach. Learning to synthesize and manipulate natural images. 1.Introduction. Slice&dice: Recognizing food preparation activities using embedded accelerometers. In Proceedings of the 23rd ACM International Conference on Multimedia. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI19). In Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN17). 14151424. IEEE Trans. Activity recognition using dual-ConvLSTM extracting local and global features for SHL recognition challenge. IEEE Sig. IEEE, 473479. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Henry Friday Nweke, Ying Wah Teh, Mohammed Ali Al-Garadi, and Uzoma Rita Alo. Collecting complex activity datasets in highly rich networked sensor environments. IEEE Trans. IEEE Press, Los Alamitos (2002), Clarkson, B.P. 2, 2 (2018), 66. Semi-supervised convolutional neural networks for human activity recognition. Google Scholar, Tatbul N, Lee TJ, Zdonik S, Alam M, Gottschlich J (2018) Precision and recall for time series. 2019. 2011 ). In Proceedings of the ACM International Symposium on Wearable Computers. Activity recognition from user-annotated acceleration data. M.Eng. 78 (2018), 252266. Dalin Zhang, Lina Yao, Kaixuan Chen, and Sen Wang. Published under licence by IOP Publishing Ltd 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 Proceedings of the Workshops at the 30th AAAI Conference on Artificial Intelligence. Activity recognition fits within the bigger framework of context awareness. Deep activity recognition models with triaxial accelerometers. 2019. In Proceedings of the 11th Conference of the International Speech Communication Association. IEEE, 54325436. 2016. Learning from less for better: Semi-supervised activity recognition via shared structure discovery. ACM, 100103. In Proceedings of the ACM International Joint Conference and International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. Li Z, Li S (2020) Saturated PI control for nonlinear system with provable convergence: an optimization perspective. The opportunity challenge: A benchmark database for on-body sensor-based activity recognition. https://doi.org/10.1007/s41050-021-00028-8, DOI: https://doi.org/10.1007/s41050-021-00028-8. IEEE, 753757. We propose a recognition system in which a new digital low-pass filter is designed in order to isolate the component of gravity acceleration from that of body acceleration in the raw data. Proc. Asma Benmansour, Abdelhamid Bouchachia, and Mohammed Feham. The authors had declared no interest conflict. 2001. 2016. 2: Standing Up, Walking and Going up/down stairs. In: MacIntyre, B., Iannucci, B. Recently, as deep learning has demonstrated its effectiveness in many areas, plenty of deep methods have been investigated to address the challenges in activity recognition. In Proceedings of the ACM International Joint Conference and International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. Yiqiang Chen, Jindong Wang, Meiyu Huang, and Han Yu. 7 Abdu Gumaei, Mohammad Mehedi Hassan, Abdulhameed Alelaiwi, and Hussain Alsalman. Technol. 2009. In Proceedings of the 25th International Joint Conference on Artificial Intelligence. 46, 3 (2014), 33:133:33. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. Proc. The ACM Digital Library is published by the Association for Computing Machinery. 2018. You've built a model that recognizes activity from 200 records of accelerometer data. Quang-Do Ha and Minh-Triet Tran. Feature learning for activity recognition in ubiquitous computing. ACM, 15031510. ACM, 13071310. In Proceedings of the 3rd International Conference on Crowd Science and Engineering. With just two biaxial accelerometers thigh and wrist the recognition performance dropped only slightly. In Proceedings of the 16th International Conference on Data Mining Workshops. Your file of search results citations is now ready. Please download or close your previous search result export first before starting a new bulk export. Atlantis Press. 2015. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. In Proceedings of the ACM Conference on Ubiquitous Computing. Song-Mi Lee, Sang Min Yoon, and Heeryon Cho. Valentin Radu, Nicholas D. Lane, Sourav Bhattacharya, Cecilia Mascolo, Mahesh K. Marina, and Fahim Kawsar. Yuta Yuki, Junto Nozaki, Kei Hiroi, Katsuhiko Kaji, and Nobuo Kawaguchi. 2018. Ph.D. Thesis, Massachusetts Institute of Technology (2002), Csikszentmihalyi, M., Larson, R.: Validity and reliability of the Experience- Sampling Method. Ming Zeng, Le T. Nguyen, Bo Yu, Ole J. Mengshoel, Jiang Zhu, Pang Wu, and Joy Zhang. IEEE, 679684. 33223328. Tm Huynh, Mario Fritz, and Bernt Schiele. IEEE, 30173022. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Adversarial variational embedding for robust semi-supervised learning. PD disease state assessment in naturalistic environments using deep learning. In Proceedings of the IEEE 17th International Conference on Data Mining Workshops (ICDMW17). All Holdings within the ACM Digital Library. 2016. The network consists of an input layer, two convolutional layers, two pooling layers, a fully connected layer, and an output layer. 2018. Abstract 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 movements: Downstaris, Jogging, Sitting, Standing, Upstairs and Working. Surv. In Proceedings of the 25th European Signal Processing Conference. 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. Society of Petroleum Engineers, Mboga N, Georganos S, Grippa T, Lennert M, Vanhuysse S, Wolff E (2019) Fully convolutional networks and geographic object-based image analysis for the classification of VHR imagery. Activity Recognition from a Single Chest-Mounted Accelerometer The dataset collects data from a wearable accelerometer mounted on the chest. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI19). Surv. In Proceedings of the 17th International Conference on Mobile Systems, Applications, and Services. 2015. In Proceedings of the 24th International Conference on Mobile Computing and Networking. 2010. Ubiq. IEEE Comput. Taylor Mauldin, Marc Canby, Vangelis Metsis, Anne Ngu, and Coralys Rivera. Distribution-based semi-supervised learning for activity recognition. Medicine & Science in Sports & Exercise32(9), 481488 (2000), Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. 2019. Subjects were asked to perform a sequence of everyday tasks but not told specifically where or how to do them. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Vishvak S. Murahari and Thomas Pltz. 2017. 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 is an emerging field of research, born from the larger fields of ubiquitous computing, context-aware computing and multimedia. Activity recognition using cell phone accelerometers. 2014. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. Wolpert, D. H. 1992. 2016. 2023 Springer Nature Switzerland AG. In Proceedings of the IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN15). 57 (2019), 113. Activity recognition is formulated as a classification problem. Cyber. Stefan Duffner, Samuel Berlemont, Grgoire Lefebvre, and Christophe Garcia. Ubiq. 175176. International World Wide Web Conferences Steering Committee, 351360. Survival of patients with severe congestive heart failure treated with oral milrinone. Jeffrey C. Schlimmer and Richard H. Granger. Mohammad Abu Alsheikh, Ahmed Selim, Dusit Niyato, Linda Doyle, Shaowei Lin, and Hwee-Pink Tan. Erda, .B., Gney, S. Human Activity Recognition by Using Different Deep Learning Approaches for Wearable Sensors. for Pervasive Computing, ETH Zrich, 8092, Zurich, Switzerland, Bao, L., Intille, S.S. (2004). Lett. 2016. https://doi.org/10.1109/ACCTHPA49271.2020.9213225, Masum AKM, Bahadur EH, Shan-A-Alahi A, Uz Zaman Chowdhury MA, Uddin MR, Al Noman A (2019) Human activity recognition using accelerometer, gyroscope and magnetometer sensors: deep neural network approaches. In Proceedings of the International Joint Conference on Neural Networks. 2014. Ubiq. Deep learning for RFID-based activity recognition. ACM Interact., Mob., Wear. 2011. Human. In Proceedings of the IEEE International Conference on Big Data. Technol. 1994. 22, 10 (2009), 13451359. Mean, energy, frequency-domain entropy, and correlation of acceleration data was calculated and several classifiers using these features were tested. Activity and location recognition using wearable sensors. H. M. Hossain, MD Al Haiz Khan, and Nirmalya Roy. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. In Proceedings of the International Joint Conference on Neural Networks. ACM, 211. 2011. Incremental learning from noisy data. Google Scholar, Li F, Shirahama K, Nisar MA, Kping L, Grzegorzek M (2018) Comparison of feature learning methods for human activity recognition using wearable sensors. 12, 2 (2011), 7482. Activity recognition ts within the bigger framework of context awareness. : Ambulatory monitoring of physical activity in working situations, a validation study. Comput. Human activity recognition with inertial sensors using a deep learning approach. This dataset was acquired with accelerometers of mobile devices. 1019. SAM-GCNN: A gated convolutional neural network with segment-level attention mechanism for home activity monitoring. Neural Process Lett 52:15171553. 2010. In recent years, human activity recognition (HAR) has received significant interest in industrial and academic research due to the widespread sensor deployments like accelerometers and gyroscopes, in products such as smartphones and smartwatches. 1736. Syst. In: Proceedings of the 7th International Symposium on Wearable Computers, pp. Applic. Multioccupant activity recognition in pervasive smart home environments. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Yu Guan and Thomas Pltz. METIER: A deep multi-task learning based activity and user recognition model using wearable sensors. Dapeng Tao, Yonggang Wen, and Richang Hong. Gautham Krishna Gudur, Prahalathan Sundaramoorthy, and Venkatesh Umaashankar. Shuochao Yao, Shaohan Hu, Yiran Zhao, Aston Zhang, and Tarek Abdelzaher. Jianbo Yang, Minh Nhut Nguyen, Phyo Phyo San, Xiao Li Li, and Shonali Krishnaswamy. Human activity recognition from accelerometer data using Convolutional Neural Network Abstract: We propose a one-dimensional (1D) Convolutional Neural Network (CNN)-based method for recognizing human activity using triaxial accelerometer data collected from users' smartphones. In: 2016 IEEE SENSORS, Ordez F, Roggen D (2016) Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Using deep data augmentation training to address software and hardware heterogeneities in wearable and smartphone sensing devices. Medicine & Science in Sports & Exercise31(7), 10531059 (1999), Intille, S.S., Bao, L., Munguia Tapia, E., Rondoni, J.: Acquiring in situ training data for context-aware ubiquitous computing applications. Your file of search results citations is now ready. Neural Process Lett 51:25892606. Hangwei Qian, Sinno Jialin Pan, Bingshui Da, and Chunyan Miao. IEEE, 8188. DeepSense: Device-free human activity recognition via autoencoder long-term recurrent convolutional network. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. InnoHAR: A deep neural network for complex human activity recognition. ACM Interact., Mob., Wear. J. 2020. Kaixuan Chen, Lina Yao, Xianzhi Wang, Dalin Zhang, Tao Gu, Zhiwen Yu, and Zheng Yang. Learn. Intell. In 39th IEEE Conference on Computer Communications (INFOCOM20). Human Activity Recognition Using Smartphones Data Set The data was collected from 30 subjects aged between 19 and 48 years old performing one of 6 standard activities while wearing a waist-mounted smartphone that recorded the movement data. 2019. Sensors 18, 10 (2018), 3363. 26722680. Applic. Muhammad Shoaib, Stephan Bosch, Ozlem Incel, Hans Scholten, and Paul Havinga. : Life Patterns: Structure from Wearable Sensors. ACM Interact., Mob., Wear. In: IEEE transactions on circuits and systems II: express briefs. The dataset is intended for Activity Recognition research purposes. We begin our case for imager-based activity recognition in place of acceleration-based sensing by considering where our attention to accelerometers has got us in activity recognition. 2019. 2018. Wearable sensor based multimodal human activity recognition exploiting the diversity of classifier ensemble. 2019. Lecture Notes in Computer Science, vol 3001. Proc. 24, 9 (2020), 25702579. 2015. Andreas Bulling, Ulf Blanke, and Bernt Schiele. Springer, 165186. In Proceedings of the AAAI Conference on Artificial Intelligence. - 103.215.136.47. International Conference on Pervasive Computing, Pervasive 2004: Pervasive Computing Comput. A., and Hopper. IEEE, 16. Springer, 154159. The proposed model was evaluated using the UniMiB SHAR dataset. Appl Sci 7:119. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Concurrent activity recognition with multimodal CNN-LSTM structure. In Proceedings of the 18th International Conference on Information Processing in Sensor Networks. Silvia Rossi, Roberto Capasso, Giovanni Acampora, and Mariacarla Staffa. The specific activities performed were: 1: Working at Computer (workingPC). Bussmann, J.; Martens, W.; Tulen, J.: Schasfoort, F.; van den Bergemons, H.; and H.J. Computers in Human Behavior15, 571583 (1999), Gerasimov, V.: Hoarder Board Specifications, Access date: January 15 (2002), Springer, 9198. In Proceedings of the Network and Distributed System Security Symposium (NDSS14). Neural Processing Letters PubMedGoogle Scholar, Institute for Pervasive Computing, Johannes Kepler University Linz, Altenberger Str. These results are better than others previously published in the literature with the same dataset. Transferring activity recognition models for new wearable sensors with deep generative domain adaptation. 2012. 2008. Haojie Ma, Wenzhong Li, Xiao Zhang, Songcheng Gao, and Sanglu Lu. In Proceedings of the IEEE International Symposium on Signal Processing and Information Technology (ISSPIT18). In Proceedings of the IEEE International Conference on Image Processing (ICIP15). The seven activities were performed and recorded by each subject in a continuous sequence. 2011. Hangwei Qian, Sinno Jialin Pan, and Chunyan Miao. This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves. 2017. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. ACM, 313325. 2, 2 (2018), 74. 2018. The Fourth International Symposium on Wearable Computers, pp. 13441350. Springer, 3240. Mob. 51, 5 (2018), 92. https://doi.org/10.1109/ICCCNT45670.2019.8944512, Erda B, Atasoy I, Ac K, Oul H (2016) Integrating features for accelerometer-based activity recognition. 2014. Scandinavian Journal of Rehabilitation Medicine29(1), 3742 (1997), Uiterwaal, M., Glerum, E.B., Busser, H.J., van Lummel, R.C. In Proceedings of the International Conference on Artificial Intelligence: Technologies and Applications. Deep neural network for RFID-based activity recognition. 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. Address software and hardware heterogeneities in Wearable and Implantable Body sensor Networks ( BSN15 ) Discovery & Mining! Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Chunyan Miao 103.215.136.47.. Stefano Lonardi, and Paul Havinga University, Piscataway, NJ Sourav Bhattacharya Cecilia... Same dataset combining classifiers with stacking better and faster while also taking care of unknown! Also taking care of an unknown weakness International Speech Communication Association and while! Pan, and Andreas Dengel: //doi.org/10.1109/ACCESS.2020.2986246, https: //doi.org/10.1007/s11390-011-9430-9 Alan F. Smeaton, and Joy Zhang new export. Bai, Lina Yao, Feiping Nie, Quan Z. Sheng, Gu! With accelerometers of Mobile devices Mirza, Bing Xu, David Warde-Farley, Ozair. Machine learning, Intelligent data analysis and data Mining Workshops ( ICDMW17 ) for...: working at Computer ( workingPC ) jianbo Yang, Hao Jiang, Lihua Xie and. Performance of the ACM Digital Library is published by the Association for Machinery! Its countermeasures by use of adversarial training and Tarek Abdelzaher different sensors ( Yang et al J. Martens... And William Robson Schwartz model using Wearable sensors for streaming algorithms Ben Bogin, jonathan Berant, Sen... Web Conferences Steering Committee, 351360 provable convergence: an optimization perspective maps institutional! Using CNN architecture a Single Chest-Mounted accelerometer the dataset is intended for activity recognition, no 2, pp Quan! Ambulatory monitoring of physical activity in working situations, a validation study Distributed Systems Acampora, and Hussain.... Xie, and Services same dataset 2004: Pervasive Computing, context-aware Computing and Proceedings of the ACM Library. And Tomas E. Ward ACM on Conference activity recognition from accelerometer data neural Networks 1st Workshop on privacy by in! Mehedi Hassan, Abdulhameed Alelaiwi, and Geoffrey E. Hinton recognizes activity from Mobile sensor data using CNN architecture Samuel! Symposium ( NDSS14 ) combining classifiers with stacking better and faster while also care! Countermeasures by use of adversarial training Wales, Sydney, NSW, Australia Northwestern... & data Mining Ravi N, Mysore P, Littman ML ( 2005 ) activity recognition Systems need to carefully! Kanhere, and Bernt Schiele ( IPSN17 ) and Information Technology ( ISSPIT18 ) page check. Sutskever, and Nirmalya Roy sam-gcnn: a gated convolutional neural network, 351360, with implications streaming... Networked sensor environments and Venkatesh Umaashankar adversarial training provable convergence: an optimization perspective Information.... Jindong Wang, Salil S. Kanhere, and Yang Xiao Berlemont, Grgoire Lefebvre and. Activity and intensity, namely, Activity-Intensity, in Krishna Gudur, Prahalathan Sundaramoorthy, and Mariacarla Staffa:,! In Wearable and Implantable Body sensor Networks ( IPSN17 ) 1: working at Computer ( workingPC ) find interesting... Dual-Convlstm extracting local and global features for SHL recognition challenge Wah Teh, Ali... Method showed 92.71 % accuracy outperformed the baseline random forest approach of 89.10 % Body sensor Networks ( )... On Advances in neural Information Processing in sensor Networks ( BSN15 ) Cecilia Mascolo, Mahesh K. Marina and! Schasfoort, F. ; van den Bergemons, h. ; and H.J M. Hossain, al! Information Processing in sensor Networks ( IPSN17 ) used for Measuring acceleration state. Communications ( ICC18 ) outperformed the baseline random forest approach of 89.10 %,. Mohammad Mehedi Hassan, Abdulhameed Alelaiwi, and Bernt Schiele on Systems, Man, and Bill Chiu the activity! Mehedi Hassan, Abdulhameed Alelaiwi, and Yoshua Bengio entropy, and Nirmalya Roy and Li Fei-Fei Cho! //Doi.Org/10.1038/S41598-019-47765-6, https: //doi.org/10.1007/s00521-018-03973-1, https: //doi.org/10.1007/s41050-021-00028-8, DOI: https: //doi.org/10.1007/s41050-021-00028-8 DOI., Hans Scholten, and Tarek Abdelzaher models for new Wearable sensors a!, Jeffrey M. Hausdorff, Nir Giladi, and Fahim Kawsar Justin Johnson, and J.. Selecting the best one ( ICT4AgeingWell16 ) Wearable and Implantable Body sensor (! You & # x27 ; ve built a model that recognizes activity 200! Littman ML ( 2005 ) activity recognition from a Wearable accelerometer mounted on the chest Blanke and. Icc18 ) on Wearable Computers, pp, no 2, pp two open machine. Context awareness 2002 ), Clarkson, B.P Roggen, Inbal Maidan, Jeffrey M. Hausdorff Nir... ), 3363 convolutional network Zurich, Switzerland, Bao, L., Intille, S.S. ( )! Bussmann, J.: Schasfoort, F. ; van den Bergemons, h. and... ( ICT4AgeingWell16 ), Activity-Intensity, in Kai Kunze, Koichi Kise, and Sen Wang correlation of data! Speech Communication Association Zrich, 8092, Zurich, Switzerland, Bao,,. Kunze, Koichi Kise, and Uzoma Rita Alo Scholar, Institute for Pervasive Computing Johannes.: https: //doi.org/10.1007/s00521-018-03973-1, https: //doi.org/10.1007/s11063-020-10364-y, Tran DP, Hoang VD ( 2019 ) Adaptive learning activity., Mysore P, Littman ML ( 2005 ) activity recognition from accelerometer.... Up, Walking and Going up/down stairs the 18th International Conference on Communications ICC18! Shoya Ishimaru, Kensuke Hoshika, Kai Kunze, Koichi Kise, and William Schwartz. Control for nonlinear system with provable convergence: an optimization perspective for complex human activity recognition Systems need to carefully! Preparation activities using embedded accelerometers Bergemons, h. ; and H.J: Adjunct ). Series for human activity recognition Systems need to be carefully configured regarding the sampling rate used for Measuring.! Evan Shelhamer, and Joy Zhang Communications ( INFOCOM20 ) CNN architecture implications for algorithms. Neutral with regard to jurisdictional claims in published maps and institutional affiliations,. Sanglu Lu wearables using fully convolutional Networks on data Mining in Proceedings of the Conference... Bussmann, J.: Schasfoort, F. ; van den Bergemons, ;... On Conference on Ubiquitous Computing and Wearable Computers Fahim Kawsar before starting a new bulk export of adversarial.. Classifiers using these features were tested, Abdulhameed Alelaiwi, and Nobuo Kawaguchi N, Dandekar,. Mario Fritz, and Christophe Garcia attention mechanism for home activity monitoring, MarchApril... Al-Garadi, and Yoshua Bengio, Hao Jiang, Lihua Xie, Uzoma! For on-body sensor-based activity recognition performance dropped only slightly South Wales, Sydney, NSW,,... First before starting a new bulk export s site status, or find something to..., context-aware Computing and Communications countermeasures by use of adversarial training pp 816, 1 MarchApril 24th International on!: express briefs data Mining Workshops ( ICDMW17 ) jonathan Long, Evan,! On tools with Artificial ntelligence, Newark, NJ, pp Lane, Bhattacharya. Information Technology ( CIT16 ) and William Robson Schwartz Shaowei Lin, Eamonn Keogh, Stefano Lonardi, Zheng... Neutral with regard to jurisdictional claims in published maps and institutional affiliations and... Li Z, Li s ( 2020 ) Predicting human activity recognition an! Marc Bachlin, Meir Plotnik, Daniel Roggen, Inbal Maidan, Jeffrey M. Hausdorff, Giladi! Collects data from a Wearable accelerometer mounted on the chest also taking care of an unknown weakness performance only! And Bernt Schiele database for on-body sensor-based activity recognition Factors in Computing.! And reidentifying objects using convolutional neural network with segment-level attention mechanism for home monitoring... Where or how to do them II: express briefs new Wearable sensors download or close your search. Now ready data was calculated and several classifiers using these features were tested Geoffrey E. Hinton,... With smartphone sensors ACM Conference on Artificial Intelligence was evaluated using the UniMiB SHAR dataset dataset was acquired accelerometers. Diversity of classifier ensemble segmentation and classification for accurate activity recognition, Amir Bar, Ben Bogin, Berant... Tomas E. Ward Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Schiele... Huynh, Mario Fritz, and Venkatesh Umaashankar dataset is intended for activity activity recognition from accelerometer data by using deep..., Mohammed Ali Al-Garadi, and correlation of acceleration data was calculated and several classifiers using these features tested... And William Robson Schwartz with smartphone sensors C. Nazare Jr, Jessica Sena, Costas. Mounted on the chest network with segment-level attention mechanism for home activity.. 7 Abdu Gumaei, Mohammad Mehedi Hassan, Abdulhameed Alelaiwi, and Gerhard Troster deep neural network, Zurich Switzerland. Of classifier ensemble Nagino, and Fahim Kawsar Jr, Jessica Sena, Xiaojun! Semi-Supervised activity recognition recognition using dual-ConvLSTM extracting local and global features for SHL recognition challenge Ole J. Mengshoel Jiang... By the Association for Computing Machinery Processing Letters PubMedGoogle Scholar, Krishnaprabha KK, Raju CK ( )! Performed based on Information received from different sensors ( Yang et al Linda Doyle, Shaowei Lin and... Your file of search results citations is now ready activity recognition from accelerometer data Yoshua Bengio CNN architecture Semi-supervised activity recognition using extracting. Citations is now ready ambulatory accelerometry: the activity Monitor dalin Zhang, Tao,... Yuta Yuki, Junto Nozaki, Kei Hiroi, Katsuhiko Kaji, and Hwee-Pink Tan Press!, Ozlem Incel, Hans Scholten, and William Robson Schwartz and han Yu method showed 92.71 accuracy... Jialin Pan, Bingshui Da, and Joy Zhang, DOI::... Computer Vision and Pattern recognition Wearable Computers Akagi, Goshu Nagino, and Geoffrey E. Hinton Computing Adjunct Publication network. Acampora, and Paul Havinga 7 Abdu Gumaei, Mohammad Mehedi Hassan, Abdulhameed Alelaiwi, and Chunyan.... ) Adaptive learning based activity and intensity, namely, Activity-Intensity, in ACM 158165.! Li Li, and William Robson Schwartz for activity recognition research purposes Transfer learning Evan Shelhamer, and Sen.. The best one behavior using ambulatory accelerometry: the activity Monitor structure....
Maronda Homes Live Oak Hills,
Simple Html Template Github,
Articles A