{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-learning-for-sensor-based-activity","title":"Deep Learning for Sensor-based Activity Recognition: A Survey","arxiv_id":"1707.03502","date":"2017-07-12","proceeding":null,"authors":["Jindong Wang","Yiqiang Chen","Shuji Hao","Xiaohui Peng","Lisha Hu"],"abstract":"Sensor-based activity recognition seeks the profound high-level knowledge\nabout human activities from multitudes of low-level sensor readings.\nConventional pattern recognition approaches have made tremendous progress in\nthe past years. However, those methods often heavily rely on heuristic\nhand-crafted feature extraction, which could hinder their generalization\nperformance. Additionally, existing methods are undermined for unsupervised and\nincremental learning tasks. Recently, the recent advancement of deep learning\nmakes it possible to perform automatic high-level feature extraction thus\nachieves promising performance in many areas. Since then, deep learning based\nmethods have been widely adopted for the sensor-based activity recognition\ntasks. This paper surveys the recent advance of deep learning based\nsensor-based activity recognition. We summarize existing literature from three\naspects: sensor modality, deep model, and application. We also present detailed\ninsights on existing work and propose grand challenges for future research.","url_abs":"http://arxiv.org/abs/1707.03502v2","url_pdf":"http://arxiv.org/pdf/1707.03502v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-learning-for-sensor-based-activity","repo_url":"https://github.com/jindongwang/deep-learning-activity-recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"incremental-learning","task_name":"Incremental Learning"},{"task_slug":"survey","task_name":"Survey"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.03502","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1707.03502"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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