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Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2025,6,9]]},"abstract":"<jats:p>In the many years since the inception of wearable sensor-based Human Activity Recognition (HAR), a wide variety of methods have been introduced and evaluated for their ability to recognize activities. Substantial gains have been made since the days of hand-crafting heuristics as features, yet, progress has seemingly stalled on many popular benchmarks, with performance falling short of what may be considered 'sufficient'-despite the increase in computational power and scale of sensor data, as well as rising complexity in techniques being employed. The HAR community approaches a new paradigm shift, this time incorporating world knowledge from foundational models. In this paper, we take stock of sensor-based HAR - surveying it from its beginnings to the current state of the field, and charting its future. This is accompanied by a hands-on tutorial, through which we guide practitioners in developing HAR systems for real-world application scenarios. We provide a compendium for novices and experts alike, of methods that aim at finally solving the activity recognition problem.<\/jats:p>","DOI":"10.1145\/3729467","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T21:21:56Z","timestamp":1750281716000},"page":"1-44","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Past, Present, and Future of Sensor-based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0545-6504","authenticated-orcid":false,"given":"Harish","family":"Haresamudram","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7766-3337","authenticated-orcid":false,"given":"Chi Ian","family":"Tang","sequence":"additional","affiliation":[{"name":"Nokia Bell Labs, Cambridge, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3723-1980","authenticated-orcid":false,"given":"Sungho","family":"Suh","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0320-6656","authenticated-orcid":false,"given":"Paul","family":"Lukowicz","sequence":"additional","affiliation":[{"name":"RPTU Kaiserslautern-Landau and DFKI, Kaiserslautern, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1243-7563","authenticated-orcid":false,"given":"Thomas","family":"Pl\u00f6tz","sequence":"additional","affiliation":[{"name":"School of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, GA, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"International Joint Conference on Artificial Intelligence","author":"Abedin Alireza","year":"2019","unstructured":"Alireza Abedin, S Hamid Rezatofighi, Qinfeng Shi, and Damith C Ranasinghe. 2019. 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Curran Associates, Inc., 8780--8794. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2021\/file\/49ad23d1ec9fa4bd8d77d02681df5cfa-Paper.pdf"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3594738.3611366"},{"key":"e_1_2_1_44_1","volume-title":"Transfer learning in human activity recognition: A survey. arXiv preprint arXiv:2401.10185","author":"Dhekane Sourish Gunesh","year":"2024","unstructured":"Sourish Gunesh Dhekane and Thomas Ploetz. 2024. Transfer learning in human activity recognition: A survey. arXiv preprint arXiv:2401.10185 (2024)."},{"key":"e_1_2_1_45_1","first-page":"30150","article-title":"Genie: Higher-order denoising diffusion solvers","volume":"35","author":"Dockhorn Tim","year":"2022","unstructured":"Tim Dockhorn, Arash Vahdat, and Karsten Kreis. 2022. Genie: Higher-order denoising diffusion solvers. 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[Accessed 28-10-2024]."},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01457"},{"key":"e_1_2_1_62_1","volume-title":"The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=jQj-_rLVXsj","author":"Gong Shansan","year":"2023","unstructured":"Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and Lingpeng Kong. 2023. DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=jQj-_rLVXsj"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3356250.3360020"},{"key":"e_1_2_1_64_1","volume-title":"Deep Learning","author":"Goodfellow Ian","unstructured":"Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. 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LLaSA: Large Multimodal Agent for Human Activity Analysis Through Wearable Sensors. arXiv preprint arXiv:2406.14498 (2024)."},{"key":"e_1_2_1_93_1","doi-asserted-by":"publisher","DOI":"10.1145\/3517246"},{"key":"e_1_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-10600-0"},{"key":"e_1_2_1_95_1","volume-title":"Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al.","author":"Jiang Albert Q","year":"2023","unstructured":"Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023. Mistral 7B. arXiv preprint arXiv:2310.06825 (2023)."},{"key":"e_1_2_1_96_1","first-page":"20067","article-title":"Motiongpt: Human motion as a foreign language","volume":"36","author":"Jiang Biao","year":"2023","unstructured":"Biao Jiang, Xin Chen, Wen Liu, Jingyi Yu, Gang Yu, and Tao Chen. 2023. 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In International Conference on Learning Representations."},{"key":"e_1_2_1_99_1","doi-asserted-by":"publisher","DOI":"10.1109\/INDIN41052.2019.8972135"},{"key":"e_1_2_1_100_1","doi-asserted-by":"publisher","DOI":"10.1145\/3325424.3329662"},{"key":"e_1_2_1_101_1","doi-asserted-by":"publisher","DOI":"10.1145\/3550285"},{"key":"e_1_2_1_102_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11045-020-00731-2"},{"key":"e_1_2_1_103_1","volume-title":"DiffWave: A Versatile Diffusion Model for Audio Synthesis. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=a-xFK8Ymz5J","author":"Kong Zhifeng","year":"2021","unstructured":"Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro. 2021. DiffWave: A Versatile Diffusion Model for Audio Synthesis. 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Advances in neural information processing systems 25 (2012)."},{"key":"e_1_2_1_107_1","doi-asserted-by":"publisher","DOI":"10.1145\/3267242.3267258"},{"key":"e_1_2_1_108_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21248337"},{"key":"e_1_2_1_109_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411841"},{"key":"e_1_2_1_110_1","doi-asserted-by":"publisher","DOI":"10.1145\/3478096"},{"key":"e_1_2_1_111_1","doi-asserted-by":"publisher","DOI":"10.1145\/2493432.2493492"},{"key":"e_1_2_1_112_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300568"},{"key":"e_1_2_1_113_1","volume-title":"A survey on human activity recognition using wearable sensors","author":"Lara Oscar D","year":"2012","unstructured":"Oscar D Lara and Miguel A Labrador. 2012. A survey on human activity recognition using wearable sensors. IEEE communications surveys & tutorials 15, 3 (2012), 1192--1209."},{"key":"e_1_2_1_114_1","volume-title":"Deep learning. nature 521, 7553","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature 521, 7553 (2015), 436--444."},{"key":"e_1_2_1_115_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2993818"},{"key":"e_1_2_1_116_1","doi-asserted-by":"publisher","DOI":"10.1145\/3678545"},{"key":"e_1_2_1_117_1","doi-asserted-by":"publisher","DOI":"10.1145\/3594738.3611361"},{"key":"e_1_2_1_118_1","doi-asserted-by":"publisher","DOI":"10.1145\/3267242.3267246"},{"key":"e_1_2_1_119_1","doi-asserted-by":"publisher","DOI":"10.1145\/3410530.3414367"},{"key":"e_1_2_1_120_1","volume-title":"SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition. arXiv preprint arXiv:2410.10624","author":"Li Zechen","year":"2024","unstructured":"Zechen Li, Shohreh Deldari, Linyao Chen, Hao Xue, and Flora D Salim. 2024. SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition. arXiv preprint arXiv:2410.10624 (2024)."},{"key":"e_1_2_1_121_1","volume-title":"Location-based activity recognition. Advances in neural information processing systems 18","author":"Liao Lin","year":"2005","unstructured":"Lin Liao, Dieter Fox, and Henry Kautz. 2005. Location-based activity recognition. Advances in neural information processing systems 18 (2005)."},{"key":"e_1_2_1_122_1","volume-title":"Self-supervised learning is more robust to dataset imbalance. arXiv preprint arXiv:2110.05025","author":"Liu Hong","year":"2021","unstructured":"Hong Liu, Jeff Z HaoChen, Adrien Gaidon, and Tengyu Ma. 2021. Self-supervised learning is more robust to dataset imbalance. arXiv preprint arXiv:2110.05025 (2021)."},{"key":"e_1_2_1_123_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02484"},{"key":"e_1_2_1_124_1","volume-title":"Visual instruction tuning. Advances in neural information processing systems 36","author":"Liu Haotian","year":"2024","unstructured":"Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2024. Visual instruction tuning. Advances in neural information processing systems 36 (2024)."},{"key":"e_1_2_1_125_1","doi-asserted-by":"publisher","DOI":"10.1145\/3699767"},{"key":"e_1_2_1_126_1","doi-asserted-by":"publisher","DOI":"10.1145\/3596711.3596800"},{"key":"e_1_2_1_127_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01117"},{"key":"e_1_2_1_128_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gltp.2022.04.020"},{"key":"e_1_2_1_129_1","doi-asserted-by":"publisher","DOI":"10.3233\/FAIA200236"},{"key":"e_1_2_1_130_1","doi-asserted-by":"publisher","DOI":"10.1145\/3195258.3195260"},{"key":"e_1_2_1_131_1","first-page":"788","article-title":"User-independent recognition of sports activities from a single wrist-worn accelerometer: A template-matching-based approach","volume":"63","author":"Margarito Jenny","year":"2015","unstructured":"Jenny Margarito, Rim Helaoui, Anna M Bianchi, Francesco Sartor, and Alberto G Bonomi. 2015. 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PMLR, 8162--8171."},{"key":"e_1_2_1_141_1","volume-title":"ICML 2017 times series workshop. 6--11","author":"Ohashi Hiroki","year":"2017","unstructured":"Hiroki Ohashi, M Al-Nasser, Sheraz Ahmed, Takayuki Akiyama, Takuto Sato, Phong Nguyen, Katsuyuki Nakamura, and Andreas Dengel. 2017. Augmenting wearable sensor data with physical constraint for DNN-based human-action recognition. In ICML 2017 times series workshop. 6--11."},{"key":"e_1_2_1_142_1","doi-asserted-by":"publisher","DOI":"10.3390\/app132011154"},{"key":"e_1_2_1_143_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_2_1_144_1","doi-asserted-by":"publisher","DOI":"10.3390\/s16010115"},{"key":"e_1_2_1_145_1","doi-asserted-by":"publisher","DOI":"10.1017\/S000711451500269X"},{"key":"e_1_2_1_146_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_2_1_147_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.278"},{"key":"e_1_2_1_148_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISWC.2005.22"},{"key":"e_1_2_1_149_1","doi-asserted-by":"publisher","DOI":"10.1145\/988672.988750"},{"key":"e_1_2_1_150_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103219"},{"key":"e_1_2_1_151_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459666"},{"key":"e_1_2_1_152_1","doi-asserted-by":"publisher","DOI":"10.1109\/PerComWorkshops56833.2023.10150267"},{"key":"e_1_2_1_153_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISWC.2012.15"},{"key":"e_1_2_1_154_1","unstructured":"Thomas Pl\u00f6tz Nils Y Hammerla and Patrick L Olivier. 2011. Feature learning for activity recognition in ubiquitous computing. In Twenty-second international joint conference on artificial intelligence."},{"key":"e_1_2_1_155_1","volume-title":"Activity recognition and healthier food preparation. Activity Recognition in Pervasive Intelligent Environments","author":"Pl\u00f6tz Thomas","year":"2011","unstructured":"Thomas Pl\u00f6tz, Paula Moynihan, Cuong Pham, and Patrick Olivier. 2011. Activity recognition and healthier food preparation. Activity Recognition in Pervasive Intelligent Environments (2011), 313--329."},{"key":"e_1_2_1_156_1","volume-title":"What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks? arXiv preprint arXiv:2202.05998","author":"Qian Hangwei","year":"2022","unstructured":"Hangwei Qian, Tian Tian, and Chunyan Miao. 2022. What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks? arXiv preprint arXiv:2202.05998 (2022)."},{"key":"e_1_2_1_157_1","volume-title":"Single motion diffusion. arXiv preprint arXiv:2302.05905","author":"Raab Sigal","year":"2023","unstructured":"Sigal Raab, Inbal Leibovitch, Guy Tevet, Moab Arar, Amit H Bermano, and Daniel Cohen-Or. 2023. Single motion diffusion. arXiv preprint arXiv:2302.05905 (2023)."},{"key":"e_1_2_1_158_1","volume-title":"International conference on machine learning. PMLR, 8748--8763","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PMLR, 8748--8763."},{"key":"e_1_2_1_159_1","volume-title":"2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 461--464","author":"Swarup Ray Lala Shakti","year":"2024","unstructured":"Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh, Lars Krupp, Vitor Fortes Rey, and Paul Lukowicz. 2024. Text me the data: Generating Ground Pressure Sequence from Textual Descriptions for HAR. In 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 461--464."},{"key":"e_1_2_1_160_1","volume-title":"2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 484--489","author":"Swarup Ray Lala Shakti","year":"2023","unstructured":"Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh, and Paul Lukowicz. 2023. Pressim: An end-to-end framework for dynamic ground pressure profile generation from monocular videos using physics-based 3d simulation. In 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 484--489."},{"key":"e_1_2_1_161_1","volume-title":"Kamalveer Kaur Garewal, and Paul Lukowicz","author":"Rey Vitor Fortes","year":"2020","unstructured":"Vitor Fortes Rey, Kamalveer Kaur Garewal, and Paul Lukowicz. 2020. Yet it moves: Learning from generic motions to generate imu data from youtube videos. arXiv preprint arXiv:2011.11600 (2020)."},{"key":"e_1_2_1_162_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341162.3345590"},{"key":"e_1_2_1_163_1","doi-asserted-by":"publisher","DOI":"10.1109\/INSS.2010.5573462"},{"key":"e_1_2_1_164_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"e_1_2_1_165_1","volume-title":"U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015:18th International Conference","author":"Ronneberger Olaf","year":"2015","unstructured":"Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015:18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer, 234--241."},{"key":"e_1_2_1_166_1","doi-asserted-by":"publisher","DOI":"10.1145\/3328932"},{"key":"e_1_2_1_167_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2021.06.188"},{"key":"e_1_2_1_168_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3596234","article-title":"ConvBoost: Boosting ConvNets for sensor-based activity recognition","volume":"7","author":"Shao Shuai","year":"2023","unstructured":"Shuai Shao, Yu Guan, Bing Zhai, Paolo Missier, and Thomas Pl\u00f6tz. 2023. ConvBoost: Boosting ConvNets for sensor-based activity recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, 2 (2023), 1--21.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_2_1_169_1","doi-asserted-by":"publisher","DOI":"10.1109\/IWBF57495.2023.10157482"},{"key":"e_1_2_1_170_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397330"},{"key":"e_1_2_1_171_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_2_1_172_1","first-page":"1","article-title":"Leveraging the Large Language Model for Activity Recognition","volume":"2024","author":"Shoumi Milyun Ni'ma","year":"2024","unstructured":"Milyun Ni'ma Shoumi and Sozo Inoue. 2024. Leveraging the Large Language Model for Activity Recognition: A Comprehensive Review. International Journal of Activity and Behavior Computing 2024, 2 (2024), 1--27.","journal-title":"A Comprehensive Review. International Journal of Activity and Behavior Computing"},{"key":"e_1_2_1_173_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10447226"},{"key":"e_1_2_1_174_1","volume-title":"International conference on machine learning. PMLR, 2256--2265","author":"Sohl-Dickstein Jascha","year":"2015","unstructured":"Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning. PMLR, 2256--2265."},{"key":"e_1_2_1_175_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.10.056"},{"key":"e_1_2_1_176_1","volume-title":"Denoising Diffusion Implicit Models. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=St1giarCHLP","author":"Song Jiaming","year":"2021","unstructured":"Jiaming Song, Chenlin Meng, and Stefano Ermon. 2021. Denoising Diffusion Implicit Models. 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