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Context-aware Human Activity Recognition (HAR) is a hot research area in mobile computing, and the most effective solutions in the literature are based on supervised deep learning models.
Mining models of human activities from the web
Mike Perkowitz, Matthai Philipose, Kenneth Fishkin, and Donald J Patterson · 2004
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Cosar: hybrid reasoning for context-aware activity recognition
Daniele Riboni and Claudio Bettini · 2011
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From textual instructions to sensor-based recognition of user behaviour
Kristina Yordanova · 2016
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Activity recognition with evolving data streams: A review
Zahraa S Abdallah, Mohamed Medhat Gaber, Bala Srinivasan, and Shonali Krishnaswamy · 2018
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Extrasensory app: Data collection in-the-wild with rich user interface to self-report behavior
Yonatan Vaizman, Katherine Ellis, Gert Lanckriet, and Nadir Weibel · 2018
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Deep learning for sensor-based activity recognition: A survey
Jindong Wang, Yiqiang Chen, Shuji Hao, Xiaohui Peng, and Lisha Hu · 2019
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Sensor-based activity recognition: One picture is worth a thousand words
Daniele Riboni and Marta Murtas · 2019
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Shades of knowledge-infused learning for enhancing deep learning
Amit Sheth, Manas Gaur, Ugur Kursuncu, and Ruwan Wickramarachchi · 2019
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Knowledge infused learning (k-il): Towards deep incorporation of knowledge in deep learning
Ugur Kursuncu, Manas Gaur, and Amit Sheth · 2019
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Caviar: Context-driven active and incremental activity recognition
Claudio Bettini, Gabriele Civitarese, and Riccardo Presotto · 2020
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Context-aware data association for multi-inhabitant sensor-based activity recognition
Luca Arrotta, Claudio Bettini, Gabriele Civitarese, and Riccardo Presotto · 2020
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Neuroplex: learning to detect complex events in sensor networks through knowledge injection
Tianwei Xing, Luis Garcia, Marc Roig Vilamala, Federico Cerutti, Lance Kaplan, Alun Preece, and Mani Srivastava · 2020
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
Federico Cruciani, Anastasios Vafeiadis, Chris Nugent, Ian Cleland, Paul McCullagh, Konstantinos Votis, Dimitrios Giakoumis, Dimitrios Tzovaras, Liming Chen, and Raouf Hamzaoui · 2020
Cited alongside, same era.
Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities
Kaixuan Chen, Dalin Zhang, Lina Yao, Bin Guo, Zhiwen Yu, and Yunhao Liu · 2021
Cited alongside, same era.
A survey on deep learning for human activity recognition
Fuqiang Gu, Mu-Huan Chung, Mark Chignell, Shahrokh Valaee, Baoding Zhou, and Xue Liu · 2021
Sensor event sequence prediction for proactive smart home support using autoregressive language model
Naoto Takeda, Roberto Legaspi, Yasutaka Nishimura, Kazushi Ikeda, Atsunori Minamikawa, Thomas Plötz, and Sonia Chernova · 2023
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Moritz A Graule and Volkan Isler · 2023
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Tent: Connect language models with iot sensors for zero-shot activity recognition
Yunjiao Zhou, Jianfei Yang, Han Zou, and Lihua Xie · 2023
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Toward pioneering sensors and features using large language models in human activity recognition
Haru Kaneko and Sozo Inoue · 2023
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Unsupervised human activity recognition through two-stage prompting with chatgpt
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Cited alongside, same era.
Neuro-symbolic artificial intelligence: Current trends
Md Kamruzzaman Sarker, Lu Zhou, Aaron Eberhart, and Pascal Hitzler · 2021
Cited alongside, same era.
Knowledge infusion for context-aware sensor-based human activity recognition
Luca Arrotta, Gabriele Civitarese, and Claudio Bettini · 2022
Cited alongside, same era.
Assessing the state of self-supervised human activity recognition using wearables
Harish Haresamudram, Irfan Essa, and Thomas Plötz · 2022
Cited alongside, same era.
A review of some techniques for inclusion of domain-knowledge into deep neural networks
Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja, and Ashwin Srinivasan · 2022
Cited alongside, same era.
Generating diverse and natural 3d human motions from text
Chuan Guo, Shihao Zou, Xinxin Zuo, Sen Wang, Wei Ji, Xingyu Li, and Li Cheng · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Cited alongside, same era.
Qingxin Xia, Takuya Maekawa, and Takahiro Hara · 2023
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Zikang Leng, Hyeokhyen Kwon, and Thomas Plötz · 2023
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Chatgpt and large language models in academia: opportunities and challenges
Jesse G Meyer, Ryan J Urbanowicz, Patrick CN Martin, Karen O’Connor, Ruowang Li, Pei-Chen Peng, Tiffani J Bright, Nicholas Tatonetti, Kyoung Jae Won, Graciela Gonzalez-Hernandez, et al · 2023
Later among the works it cites.
Domino: A dataset for context-aware human activity recognition using mobile devices
Luca Arrotta, Gabriele Civitarese, Riccardo Presotto, and Claudio Bettini · 2023
Later among the works it cites.
Transfer learning in human activity recognition: A survey
Sourish Gunesh Dhekane and Thomas Ploetz · 2024
Closest in time.
Semantic loss: A new neuro-symbolic approach for context-aware human activity recognition
Luca Arrotta, Gabriele Civitarese, and Claudio Bettini · 2024
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