{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T18:09:20Z","timestamp":1791137360781,"version":"4.1.0"},"reference-count":61,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2013,11,1]],"date-time":"2013-11-01T00:00:00Z","timestamp":1383264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wearable and accompanied sensors and devices are increasingly being used for user activity recognition. However, typical GPS-based and accelerometer-based (ACC) methods face three main challenges: a low recognition accuracy; a coarse recognition capability, i.e., they cannot recognise both human posture (during travelling) and transportation mode simultaneously, and a relatively high computational complexity. Here, a new GPS and Foot-Force (GPS + FF) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with GPS, e.g., in a mobile phone. User mobility activities that can be recognised include both daily user postures and common transportation modes: sitting, standing, walking, cycling, bus passenger, car passenger (including private cars and taxis) and car driver. The novelty of this work is that our approach provides a more comprehensive recognition capability in terms of reliably recognising both human posture and transportation mode simultaneously during travel. In addition, by comparing the new GPS + FF method with both an ACC method (62% accuracy) and a  GPS + ACC based method (70% accuracy) as baseline methods, it obtains a higher accuracy (95%) with less computational complexity, when tested on a dataset obtained from  ten individuals.<\/jats:p>","DOI":"10.3390\/s131114918","type":"journal-article","created":{"date-parts":[[2013,11,1]],"date-time":"2013-11-01T12:41:02Z","timestamp":1383309662000},"page":"14918-14953","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition"],"prefix":"10.3390","volume":"13","author":[{"given":"Zelun","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronic Engineering and Computer Science, Queen Mary University of London,  Mile End Road, London E1 4NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Poslad","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Computer Science, Queen Mary University of London,  Mile End Road, London E1 4NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2013,11,1]]},"reference":[{"key":"ref_1","unstructured":"Poslad, S. 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