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Given a terrain as input, our optimization-based approach automatically generates feasible paths on the terrain which users can bike to perform body training in virtual reality. The approach considers exertion properties such as the total work and the perceived level of path difficulty in generating the paths. To verify our approach, we applied it to generate paths on a variety of terrains with different exertion targets and constraints. To conduct our user studies, we built an exercise bike whose force feedback was controlled by the elevation angle of the generated path over the terrain. Our user study results showed that users found exercising with our generated paths in virtual reality more enjoyable compared to traditional exercising approaches. Their energy expenditure in biking the generated paths also matched with the specified targets, validating the efficacy of our approach.<\/jats:p>","DOI":"10.1145\/3386569.3392393","type":"journal-article","created":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T11:44:27Z","timestamp":1597232667000},"update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["Exertion-aware path generation"],"prefix":"10.1145","volume":"39","author":[{"given":"Wanwan","family":"Li","sequence":"first","affiliation":[{"name":"George Mason University"}]},{"given":"Biao","family":"Xie","sequence":"additional","affiliation":[{"name":"University of Massachusetts Boston"}]},{"given":"Yongqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"George Mason University"}]},{"given":"Walter","family":"Meiss","sequence":"additional","affiliation":[{"name":"University of Massachusetts Boston"}]},{"given":"Haikun","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Massachusetts Boston"}]},{"given":"Lap-Fai","family":"Yu","sequence":"additional","affiliation":[{"name":"George Mason University"}]}],"member":"320","published-online":{"date-parts":[[2020,8,12]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Martin De Lasa, and Aaron Hertzmann","author":"Borno Mazen Al","year":"2012","unstructured":"Mazen Al Borno, Martin De Lasa, and Aaron Hertzmann. 2012. 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