{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T13:38:15Z","timestamp":1782913095494,"version":"3.54.5"},"reference-count":32,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T00:00:00Z","timestamp":1658966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2020YFB1313803"],"award-info":[{"award-number":["2020YFB1313803"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Identifying the massage techniques of the masseuse is a prerequisite for guiding robotic massage. It is difficult to recognize multiple consecutive massage maps with a time series for current human action recognition algorithms. To solve the problem, a method combining a convolutional neural network, long-term neural network, and attention mechanism is proposed to identify the massage techniques in this paper. First, the pressure distribution massage map is collected by a massage glove, and the data are enhanced by the conditional variational auto-encoder. Then, the features of the massage map group in the spatial domain and timing domain are extracted through the convolutional neural network and the long- and short-term memory neural network, respectively. The attention mechanism is introduced into the neural network, giving each massage map a different weight value to enhance the network extraction of data features. Finally, the massage haptic dataset is collected by a massage data acquisition system. The experimental results show that a classification accuracy of 100% is achieved. The results demonstrate that the proposed method could identify sequential massage maps, improve the network overfitting phenomenon, and enhance the network generalization ability effectively.<\/jats:p>","DOI":"10.3390\/s22155632","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T22:43:26Z","timestamp":1659048206000},"page":"5632","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Recognition Method of Massage Techniques Based on Attention Mechanism and Convolutional Long Short-Term Memory Neural Network"],"prefix":"10.3390","volume":"22","author":[{"given":"Shengding","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingtao","family":"Lei","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-3620-4318","authenticated-orcid":false,"given":"Dongdong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1097\/JCN.0000000000000309","article-title":"Effectiveness of Chinese Hand Massage on Anxiety among Patients Awaiting Coronary Angiography: A Randomized Controlled Trial","volume":"32","author":"Mei","year":"2017","journal-title":"J. Cardiovasc. Nurs."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hsiao, C.-P., Li, R., Yan, X., and Do, E.Y.-L. (2015, January 15\u201319). Tactile Teacher: Sensing Finger Tapping in Piano Playing. Proceedings of the Ninth International Conference on Tangible, Embedded, and Embodied Interaction, Stanford, CA, USA.","DOI":"10.1145\/2677199.2680554"},{"key":"ref_3","unstructured":"Li, R., Wang, Y., Hsiao, C.-P., Davis, N., Hallam, J., and Do, E. (March, January 27). Tactile Teacher: Enhancing Traditional Piano Lessons with Tactile Instructions. Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion, San Francisco, CA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yeo, J.C., Lee, C., Wang, Z., and Lim, C.T. (2016, January 20\u201322). Tactile Sensorized Glove for Force and Motion Sensing. Proceedings of the 2016 IEEE Sensors, Catania, Italy.","DOI":"10.1109\/ICSENS.2016.7808596"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1109\/TNSRE.2020.2986222","article-title":"Wearable Assistive Tactile Communication Interface Based on Integrated Touch Sensors and Actuators","volume":"28","author":"Ozioko","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Maiolino, P., Denei, S., Mastrogiovanni, F., and Cannata, G. (2013, January 26\u201329). A Sensorized Glove for Experiments in Cloth Manipulation. Proceedings of the 2013 IEEE RO-MAN, Gyeongju, Korea.","DOI":"10.1109\/ROMAN.2013.6628484"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhong, Y., Sun, Y., and Li, X. (2014, January 5\u201310). A Flexible Capacitive Tactile Sensing Array for Pressure Measurement. Proceedings of the 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), Bali, Indonesia.","DOI":"10.1109\/ROBIO.2014.7090688"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bianchi, M., Haschke, R., B\u00fcscher, G., Ciotti, S., Carbonaro, N., and Tognetti, A. (2016). A Multi-Modal Sensing Glove for Human Manual-Interaction Studies. Electronics, 5.","DOI":"10.3390\/electronics5030042"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9350","DOI":"10.1109\/TIE.2019.2893840","article-title":"Towards Real-Time Advancement of Underwater Visual Quality with GAN","volume":"66","author":"Chen","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1016\/j.specom.2011.06.004","article-title":"Emotion Recognition Using a Hierarchical Binary Decision Tree Approach","volume":"53","author":"Lee","year":"2011","journal-title":"Speech Commun."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Supratak, A., Wu, C., Dong, H., Sun, K., and Guo, Y. (2016). Survey on Feature Extraction and Applications of Biosignals. Machine Learning for Health Informatics, Springer.","DOI":"10.1007\/978-3-319-50478-0_8"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/S0034-4257(03)00132-9","article-title":"An Assessment of the Effectiveness of Decision Tree Methods for Land Cover Classification","volume":"86","author":"Pal","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"20","DOI":"10.38094\/jastt20165","article-title":"Classification Based on Decision Tree Algorithm for Machine Learning","volume":"2","author":"Charbuty","year":"2021","journal-title":"J. Appl. Sci. Technol. Trends"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Han, E.-H.S., Karypis, G., and Kumar, V. (2001, January 16\u201318). Text Categorization Using Weight Adjusted K-Nearest Neighbor Classification. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Hong Kong, China.","DOI":"10.1007\/3-540-45357-1_9"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.neuroimage.2007.05.018","article-title":"Multi-Spectral Brain Tissue Segmentation Using Automatically Trained k-Nearest-Neighbor Classification","volume":"37","author":"Vrooman","year":"2007","journal-title":"Neuroimage"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1177\/0272989X9301300409","article-title":"Markov Models in Medical Decision Making: A Practical Guide","volume":"13","author":"Sonnenberg","year":"1993","journal-title":"Med. Decis. Making"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1109\/TGRS.2004.830170","article-title":"Texture Feature Analysis Using a Gauss-Markov Model in Hyperspectral Image Classification","volume":"42","author":"Rellier","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1016\/S0893-6080(99)00032-5","article-title":"Improving Support Vector Machine Classifiers by Modifying Kernel Functions","volume":"12","author":"Amari","year":"1999","journal-title":"Neural Netw."},{"key":"ref_19","unstructured":"Mangasarian, O.L., and Wild, E.W. (2001, January 26\u201329). Proximal Support Vector Machine Classifiers. Proceedings of the KDD-2001: Knowledge Discovery and Data Mining, Citeseer, San Francisco, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1109\/TITB.2009.2037317","article-title":"SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results","volume":"14","author":"Fleury","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1049\/el.2015.0436","article-title":"Decision-Tree-Based Human Activity Classification Algorithm Using Single-Channel Foot-Mounted Gyroscope","volume":"51","author":"McCarthy","year":"2015","journal-title":"Electron. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7257","DOI":"10.1007\/s11042-015-2643-0","article-title":"Human Activity Recognition Using Quasiperiodic Time Series Collected from a Single Tri-Axial Accelerometer","volume":"75","author":"Ignatov","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3D Convolutional Neural Networks for Human Action Recognition","volume":"35","author":"Ji","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yue-Hei Ng, J., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., and Toderici, G. (2015, January 7\u201312). Beyond Short Snippets: Deep Networks for Video Classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299101"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, X., Chen, Z., Wang, X., Zhao, Q., and Liang, B. (2018, January 21\u201323). A Comprehensive Evaluation of Moving Static Gesture Recognition with Convolutional Networks. Proceedings of the 2018 3rd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), Singapore.","DOI":"10.1109\/ACIRS.2018.8467228"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8553","DOI":"10.1109\/JIOT.2019.2920283","article-title":"IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment","volume":"6","author":"Bianchi","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1038\/s41586-019-1234-z","article-title":"Learning the Signatures of the Human Grasp Using a Scalable Tactile Glove","volume":"569","author":"Sundaram","year":"2019","journal-title":"Nature"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Koch, P., Dreier, M., Maass, M., B\u00f6hme, M., Phan, H., and Mertins, A. (2019, January 23\u201327). A Recurrent Neural Network for Hand Gesture Recognition Based on Accelerometer Data. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8856844"},{"key":"ref_29","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-Encoding Variational Bayes. arXiv."},{"key":"ref_30","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"277","DOI":"10.5194\/isprs-archives-XLII-3-277-2018","article-title":"Alexnet feature extraction and multi-kernel learning for objectorientedclassification","volume":"XLII\u20133","author":"Ding","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/22\/15\/5632\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:57:54Z","timestamp":1760140674000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/22\/15\/5632"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,28]]},"references-count":32,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22155632"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/s22155632","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,28]]}}}