{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T17:47:52Z","timestamp":1767116872398,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,4,23]],"date-time":"2017-04-23T00:00:00Z","timestamp":1492905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MOTIE, Korea","award":["10049079"],"award-info":[{"award-number":["10049079"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Activity recognition through smartphones has been proposed for a variety of applications. The orientation of the smartphone has a significant effect on the recognition accuracy; thus, researchers generally propose using features invariant to orientation or displacement to achieve this goal. However, those features reduce the capability of the recognition system to differentiate among some specific commuting activities (e.g., bus and subway) that normally involve similar postures. In this work, we recognize those activities by analyzing the vibrations of the vehicle in which the user is traveling. We extract natural vibration features of buses and subways to distinguish between them and address the confusion that can arise because the activities are both static in terms of user movement. We use the gyroscope to fix the accelerometer to the direction of gravity to achieve an orientation-free use of the sensor. We also propose a correction algorithm to increase the accuracy when used in free living conditions and a battery saving algorithm to consume less power without reducing performance. Our experimental results show that the proposed system can adequately recognize each activity, yielding better accuracy in the detection of bus and subway activities than existing methods.<\/jats:p>","DOI":"10.3390\/s17040931","type":"journal-article","created":{"date-parts":[[2017,4,24]],"date-time":"2017-04-24T13:10:11Z","timestamp":1493039411000},"page":"931","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Smartphone Location-Independent Physical Activity Recognition Based on Transportation Natural Vibration Analysis"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7458-3745","authenticated-orcid":false,"given":"Taeho","family":"Hur","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Kyung Hee University, (Global Campus), 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3675-2258","authenticated-orcid":false,"given":"Jaehun","family":"Bang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kyung Hee University, (Global Campus), 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dohyeong","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kyung Hee University, (Global Campus), 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5434-4253","authenticated-orcid":false,"given":"Oresti","family":"Banos","sequence":"additional","affiliation":[{"name":"Telemedicine Cluster of the Biomedical Signals and Systems Group, University of Twente, Enschede 7500AE, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sungyoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kyung Hee University, (Global Campus), 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MPRV.2010.7","article-title":"Human activity recognition and pattern discovery","volume":"9","author":"Kim","year":"2010","journal-title":"IEEE Pervasive Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/SURV.2012.110112.00192","article-title":"A survey on human activity recognition using wearable sensors","volume":"15","author":"Lara","year":"2013","journal-title":"IEEE Commun. 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