Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition
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 pos...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2013-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/13/11/14918 |
| _version_ | 1798040159512428544 |
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| author | Stefan Poslad Zelun Zhang |
| author_facet | Stefan Poslad Zelun Zhang |
| author_sort | Stefan Poslad |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:03:37Z |
| format | Article |
| id | doaj.art-daf81faa81e04a75b681692728f2816e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:03:37Z |
| publishDate | 2013-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-daf81faa81e04a75b681692728f2816e2022-12-22T04:00:49ZengMDPI AGSensors1424-82202013-11-011311149181495310.3390/s131114918Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity RecognitionStefan PosladZelun ZhangWearable 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.http://www.mdpi.com/1424-8220/13/11/14918mobility profilingactivity recognitionfoot force sensorGPSaccelerometer |
| spellingShingle | Stefan Poslad Zelun Zhang Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition Sensors mobility profiling activity recognition foot force sensor GPS accelerometer |
| title | Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition |
| title_full | Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition |
| title_fullStr | Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition |
| title_full_unstemmed | Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition |
| title_short | Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition |
| title_sort | design and test of a hybrid foot force sensing and gps system for richer user mobility activity recognition |
| topic | mobility profiling activity recognition foot force sensor GPS accelerometer |
| url | http://www.mdpi.com/1424-8220/13/11/14918 |
| work_keys_str_mv | AT stefanposlad designandtestofahybridfootforcesensingandgpssystemforricherusermobilityactivityrecognition AT zelunzhang designandtestofahybridfootforcesensingandgpssystemforricherusermobilityactivityrecognition |