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To facilitate researchers in collecting more extensive datasets quickly and efficiently we developed SenseCollect. We did a survey and interviewed student researchers in this area to identify what barriers are making it difficult to collect on-body sensor-based HAR data from human subjects. Every interviewee identified data collection as the hardest part of their research, stating it was laborious, consuming and error-prone. To improve HAR data resources we need to address that barrier, but we need a better understanding of the complicating factors to overcome it. To that end we conducted a series of control variable experiments that tested several protocols to ascertain their impact on data collection. SenseCollect studied 240+ human subjects in total and presented the findings to develop a data collection guideline. We also implemented a system to collect data, created the two largest on-body sensor-based human activity datasets, and made them publicly available.<\/jats:p>","DOI":"10.1145\/3478119","type":"journal-article","created":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T22:48:23Z","timestamp":1631659703000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":34,"title":["SenseCollect"],"prefix":"10.1145","volume":"5","author":[{"given":"Wenqiang","family":"Chen","sequence":"first","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shupei","family":"Lin","sequence":"additional","affiliation":[{"name":"VibInt AI Limited"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elizabeth","family":"Thompson","sequence":"additional","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Stankovic","sequence":"additional","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,9,14]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"[n.d.]. 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Incentives for privacy tradeoff in community sensing . In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing , Vol. 1 . Adish Singla and Andreas Krause. 2013. Incentives for privacy tradeoff in community sensing. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, Vol. 1."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/2750858.2807545"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3376897.3377867"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3136755.3136817"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/2632048.2632054"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2020.2976007"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971711"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2015.7146509"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3264954"},{"key":"e_1_2_1_54_1","volume-title":"How transferable are features in deep neural networks? arXiv preprint arXiv:1411.1792","author":"Yosinski Jason","year":"2014","unstructured":"Jason Yosinski , Jeff Clune , Yoshua Bengio , and Hod Lipson . 2014. 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