{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:44:14Z","timestamp":1760150654032,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Foundation of Education Bureau of Hunan Province","award":["21B0424"],"award-info":[{"award-number":["21B0424"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human Activity Recognition (HAR) systems have made significant progress in recognizing and classifying human activities using sensor data from a variety of sensors. Nevertheless, they have struggled to automatically discover novel activity classes within massive amounts of unlabeled sensor data without external supervision. This restricts their ability to classify new activities of unlabeled sensor data in real-world deployments where fully supervised settings are not applicable. To address this limitation, this paper presents the Novel Class Discovery (NCD) problem, which aims to classify new class activities of unlabeled sensor data by fully utilizing existing activities of labeled data. To address this problem, we propose a new end-to-end framework called More Reliable Neighborhood Contrastive Learning (MRNCL), which is a variant of the Neighborhood Contrastive Learning (NCL) framework commonly used in visual domain. Compared to NCL, our proposed MRNCL framework is more lightweight and introduces an effective similarity measure that can find more reliable k-nearest neighbors of an unlabeled query sample in the embedding space. These neighbors contribute to contrastive learning to facilitate the model. Extensive experiments on three public sensor datasets demonstrate that the proposed model outperforms existing methods in the NCD task in sensor-based HAR, as indicated by the fact that our model performs better in clustering performance of new activity class instances.<\/jats:p>","DOI":"10.3390\/s23239529","type":"journal-article","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T09:39:12Z","timestamp":1701337152000},"page":"9529","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["More Reliable Neighborhood Contrastive Learning for Novel Class Discovery in Sensor-Based Human Activity Recognition"],"prefix":"10.3390","volume":"23","author":[{"given":"Mingcong","family":"Zhang","sequence":"first","affiliation":[{"name":"The School of Computer Science, University of South China, Hengyang 421001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5879-5980","authenticated-orcid":false,"given":"Tao","family":"Zhu","sequence":"additional","affiliation":[{"name":"The School of Computer Science, University of South China, Hengyang 421001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2720-0470","authenticated-orcid":false,"given":"Mingxing","family":"Nie","sequence":"additional","affiliation":[{"name":"The School of Computer Science, University of South China, Hengyang 421001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenyu","family":"Liu","sequence":"additional","affiliation":[{"name":"The School of Computer Science, University of South China, Hengyang 421001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2514012","DOI":"10.1109\/TIM.2023.3273673","article-title":"A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation","volume":"72","author":"Duan","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Chen, Y., Nie, M., Zhu, T., Liu, Z., and Liu, C. (2023). Exploring LoRa and Deep Learning-Based Wireless Activity Recognition. Electronics, 12.","DOI":"10.3390\/electronics12030629"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7341","DOI":"10.1109\/JSEN.2015.2475626","article-title":"Wellness sensor networks: A proposal and implementation for smart home for assisted living","volume":"15","author":"Ghayvat","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8413","DOI":"10.1109\/JSEN.2018.2871203","article-title":"Activity recognition for cognitive assistance using body sensors data and deep convolutional neural network","volume":"19","author":"Uddin","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, X., Cui, S., Zhu, T., Chen, L., Zhou, F., and Ning, H. (2023). CASL: Capturing Activity Semantics through Location Information for enhanced activity recognition. IEEE ACM Trans. Comput. Biol. Bioinform., 22.","DOI":"10.1109\/TCBB.2023.3238064"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/JSEN.2016.2628346","article-title":"A survey on activity detection and classification using wearable sensors","volume":"17","author":"Cornacchia","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3623","DOI":"10.1109\/JSEN.2020.3028561","article-title":"CNN-based multistage gated average fusion (MGAF) for human action recognition using depth and inertial sensors","volume":"21","author":"Ahmad","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_8","unstructured":"Hammerla, N.Y., Halloran, S., and Pl\u00f6tz, T. (2016, January 9\u201315). Deep, convolutional, and recurrent models for human activity recognition using wearables. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1109\/JIOT.2019.2949715","article-title":"A novel IoT-perceptive human activity recognition (HAR) approach using multihead convolutional attention","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.future.2022.09.024","article-title":"MultiCNN-FilterLSTM: Resource-efficient sensor-based human activity recognition in IoT applications","volume":"139","author":"Park","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"13029","DOI":"10.1109\/JSEN.2021.3069927","article-title":"Human activity recognition with smartphone and wearable sensors using deep learning techniques: A review","volume":"21","author":"Ramanujam","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_12","first-page":"100142","article-title":"Transfer learning enhanced vision-based human activity recognition: A decade-long analysis","volume":"3","author":"Ray","year":"2023","journal-title":"Int. J. Inf. Manag. Data Insights"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3563948","article-title":"Transfer learning for human activity recognition using representational analysis of neural networks","volume":"4","author":"An","year":"2023","journal-title":"ACM Trans. Comput. Healthc."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, D., and Abdelzaher, T. (2021, January 14\u201316). Semi-supervised contrastive learning for human activity recognition. Proceedings of the 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS), Virtual.","DOI":"10.1109\/DCOSS52077.2021.00019"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"119679","DOI":"10.1016\/j.eswa.2023.119679","article-title":"Context-aware mutual learning for semi-supervised human activity recognition using wearable sensors","volume":"219","author":"Qu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2021.04.062","article-title":"Continual learning in sensor-based human activity recognition: An empirical benchmark analysis","volume":"575","author":"Jha","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6067","DOI":"10.1016\/j.eswa.2014.04.037","article-title":"Unsupervised learning for human activity recognition using smartphone sensors","volume":"41","author":"Kwon","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_18","first-page":"304","article-title":"Unsupervised Activity Recognition Using Trajectory Heatmaps from Inertial Measurement Unit Data","volume":"2","author":"Konak","year":"2022","journal-title":"Proc. ICAART"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3448074","article-title":"Unsupervised human activity representation learning with multi-task deep clustering","volume":"5","author":"Ma","year":"2021","journal-title":"Proc. Acm Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Abedin, A., Motlagh, F., Shi, Q., Rezatofighi, H., and Ranasinghe, D. (2020, January 12\u201316). Towards deep clustering of human activities from wearables. Proceedings of the 2020 ACM International Symposium on Wearable Computers, Virtual.","DOI":"10.1145\/3410531.3414312"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Amrani, H., Micucci, D., and Napoletano, P. (2022, January 12\u201318). Unsupervised Deep Learning-based clustering for Human Activity Recognition. Proceedings of the 2022 IEEE 12th International Conference on Consumer Electronics (ICCE), Berlin, Germany.","DOI":"10.1109\/ICCE-Berlin56473.2022.9937141"},{"key":"ref_22","first-page":"1","article-title":"Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities","volume":"54","author":"Chen","year":"2021","journal-title":"ACM Comput. Surv. CSUR"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"109363","DOI":"10.1016\/j.asoc.2022.109363","article-title":"A survey on unsupervised learning for wearable sensor-based activity recognition","volume":"127","author":"Ige","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2499621","article-title":"A tutorial on human activity recognition using body-worn inertial sensors","volume":"46","author":"Bulling","year":"2014","journal-title":"ACM Comput. Surv. CSUR"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tian, Y., Krishnan, D., and Isola, P. (2020, January 23\u201328). Contrastive multiview coding. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK.","DOI":"10.1007\/978-3-030-58621-8_45"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Fini, E., Roy, S., Luo, Z., Ricci, E., and Sebe, N. (2021, January 20\u201325). Neighborhood contrastive learning for novel class discovery. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01072"},{"key":"ref_27","unstructured":"Rahutomo, F., Kitasuka, T., and Aritsugi, M. (2012, January 29\u201330). Semantic cosine similarity. Proceedings of the The 7th International Student Conference on Advanced Science and Technology ICAST, Seoul, Republic of Korea."},{"key":"ref_28","unstructured":"Han, K., Vedaldi, A., and Zisserman, A. (November, January 27). Learning to discover novel visual categories via deep transfer clustering. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., and Zisserman, A. (2021, January 11\u201317). With a little help from my friends: Nearest-neighbor contrastive learning of visual representations. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00945"},{"key":"ref_30","first-page":"1","article-title":"Learning the k in k-means","volume":"16","author":"Hamerly","year":"2003","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., and Alshurafa, N. (2022). Deep learning in human activity recognition with wearable sensors: A review on advances. Sensors, 22.","DOI":"10.3390\/s22041476"},{"key":"ref_32","unstructured":"M\u00fcllner, D. (2011). Modern hierarchical, agglomerative clustering algorithms. arXiv."},{"key":"ref_33","unstructured":"Tang, C.I., Perez-Pozuelo, I., Spathis, D., and Mascolo, C. (2020). Exploring contrastive learning in human activity recognition for healthcare. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"22994","DOI":"10.1109\/JSEN.2022.3214198","article-title":"Sensor Data Augmentation by Resampling in Contrastive Learning for Human Activity Recognition","volume":"22","author":"Wang","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Qian, H., Tian, T., and Miao, C. (2022, January 14\u201318). What makes good contrastive learning on small-scale wearable-based tasks?. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA.","DOI":"10.1145\/3534678.3539134"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fei, G., and Liu, B. (2015, January 17\u201321). Social media text classification under negative covariate shift. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1282"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.patrec.2016.09.006","article-title":"The use of Lorentzian distance metric in classification problems","volume":"84","author":"Kerimbekov","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.ins.2015.02.024","article-title":"Learning similarity with cosine similarity ensemble","volume":"307","author":"Xia","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1109\/TPAMI.2012.256","article-title":"Toward open set recognition","volume":"35","author":"Scheirer","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, Z., and Liu, B. (2018). Lifelong Machine Learning, Springer.","DOI":"10.1007\/978-3-031-01581-6"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020, January 13\u201319). Momentum contrast for unsupervised visual representation learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref_42","first-page":"18661","article-title":"Supervised contrastive learning","volume":"33","author":"Khosla","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"857","DOI":"10.2307\/2528823","article-title":"A general coefficient of similarity and some of its properties","volume":"27","author":"Gower","year":"1971","journal-title":"Biometrics"},{"key":"ref_44","unstructured":"Jaffe, A. (2013). Lorentz transformations, rotations, and boosts. Lect. Notes."},{"key":"ref_45","unstructured":"Deza, M.M., and Deza, E. (2006). Dictionary of Distances, Elsevier."},{"key":"ref_46","first-page":"547","article-title":"\u00c9tude comparative de la distribution florale dans une portion des Alpes et des Jura","volume":"37","author":"Jaccard","year":"1901","journal-title":"Bull. Soc. Vaudoise Sci. Nat."},{"key":"ref_47","first-page":"64","article-title":"Measuring of Interspecific Association and Similarity Between Communities","volume":"3","author":"Morishita","year":"1959","journal-title":"Mem. Fac. Sci. Kyushu Univ. Ser. E Biol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/1964897.1964918","article-title":"Activity recognition using cell phone accelerometers","volume":"12","author":"Kwapisz","year":"2011","journal-title":"ACM SigKDD Explor. Newsl."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bulbul, E., Cetin, A., and Dogru, I.A. (2018, January 19\u201321). Human activity recognition using smartphones. Proceedings of the 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey.","DOI":"10.1109\/ISMSIT.2018.8567275"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhang, M., and Sawchuk, A.A. (2012, January 5\u20138). USC-HAD: A daily activity dataset for ubiquitous activity recognition using wearable sensors. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370438"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1002\/nav.3800020109","article-title":"The Hungarian method for the assignment problem","volume":"2","author":"Kuhn","year":"1955","journal-title":"Nav. Res. Logist. Q."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"10833","DOI":"10.1109\/JIOT.2023.3239945","article-title":"Negative selection by clustering for contrastive learning in human activity recognition","volume":"10","author":"Wang","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_53","first-page":"3221","article-title":"Accelerating t-SNE using tree-based algorithms","volume":"15","year":"2014","journal-title":"J. Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9529\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:34:55Z","timestamp":1760132095000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9529"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"references-count":53,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23239529"],"URL":"https:\/\/doi.org\/10.3390\/s23239529","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,11,30]]}}}