{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:23:59Z","timestamp":1760235839123,"version":"build-2065373602"},"reference-count":6,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:00:00Z","timestamp":1632960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hitachi (United Kingdom)","award":["TIML 2020\/1"],"award-info":[{"award-number":["TIML 2020\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>We present a unique, comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus, or a taxi. The measurements are carried out by embedded sensor accelerometers in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in varied styles as they perform the five activities through vibrations acquired by accelerometers. The dataset contains corresponding time stamps and vibrations in three directions longitudinal, horizontal, and vertically stored in an Excel Macro-enabled Workbook (xlsm) format that can be used to train an AI model in a smartphone which has the potential to collect people\u2019s vibration data and decide what movement is being conducted. Moreover, with more data received, the database can be updated and used to train the model with a larger dataset. The prevalence of the smartphone opens the door to crowdsensing, which leads to the pattern of people taking public transport being understood. Furthermore, the time consumed in each activity is available in the dataset. Therefore, with a better understanding of people using public transport, services and schedules can be planned perceptively.<\/jats:p>","DOI":"10.3390\/data6100104","type":"journal-article","created":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T10:22:42Z","timestamp":1632997362000},"page":"104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Human Activity Vibrations"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2153-3538","authenticated-orcid":false,"given":"Sakdirat","family":"Kaewunruen","sequence":"first","affiliation":[{"name":"School of Engineering, University of Birmingham, Birmingham B15 2TT, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3692-6422","authenticated-orcid":false,"given":"Jessada","family":"Sresakoolchai","sequence":"additional","affiliation":[{"name":"School of Engineering, University of Birmingham, Birmingham B15 2TT, UK"}]},{"given":"Junhui","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Engineering, University of Birmingham, Birmingham B15 2TT, UK"}]},{"given":"Satoru","family":"Harada","sequence":"additional","affiliation":[{"name":"Hitachi Europe Limited, ERD Office, 12th Floor, 125 London Wall, London EC2Y 5AL, UK"}]},{"given":"Wisinee","family":"Wisetjindawat","sequence":"additional","affiliation":[{"name":"Hitachi Europe Limited, ERD Office, 12th Floor, 125 London Wall, London EC2Y 5AL, UK"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1016\/j.conbuildmat.2016.02.126","article-title":"A contribution for integrated analysis of railway track performance at transition zones and other discontinuities","volume":"111","author":"Fortunato","year":"2016","journal-title":"Constr. Build. Mater."},{"key":"ref_2","unstructured":"Ma, Z., Qiao, Y., Lee, B., and Fallon, E. (2013, January 20\u201321). Experimental evaluation of mobile phone sensors. Proceedings of the 24th IET Irish Signals and Systems Conference (ISSC 2013), Letterkenny, Ireland."},{"key":"ref_3","unstructured":"Mohri, M., Rostamizadeh, A., and Talwalkar, A. (2018). Foundations of Machine Learning, MIT Press."},{"key":"ref_4","first-page":"38","article-title":"Recognizing human activities user-independently on smartphones based on accelerometer data","volume":"1","author":"Siirtola","year":"2012","journal-title":"Int. J. Interact. Multimed. Artif. Intell."},{"key":"ref_5","unstructured":"Frank, A. (2021, January 01). UCI Machine Learning Repository. Available online: http:\/\/archive.ics.uci.edu\/ml."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Carpineti, C., Lomonaco, V., Bedogni, L., Di Felice, M., and Bononi, L. (2018, January 19\u201323). Custom dual transportation mode detection by smartphone devices exploiting sensor diversity. Proceedings of the 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Athens, Greece.","DOI":"10.1109\/PERCOMW.2018.8480119"}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/6\/10\/104\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:07:45Z","timestamp":1760166465000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/6\/10\/104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,30]]},"references-count":6,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["data6100104"],"URL":"https:\/\/doi.org\/10.3390\/data6100104","relation":{},"ISSN":["2306-5729"],"issn-type":[{"type":"electronic","value":"2306-5729"}],"subject":[],"published":{"date-parts":[[2021,9,30]]}}}