Position Tracking During Human Walking Using an Integrated Wearable Sensing System
Progress has been made enabling expensive, high-end inertial measurement units (IMUs) to be used as tracking sensors. However, the cost of these IMUs is prohibitive to their widespread use, and hence the potential of low-cost IMUs is investigated in this study. A wearable low-cost sensing system con...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/17/12/2866 |
| _version_ | 1798041966617821184 |
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| author | Giulio Zizzo Lei Ren |
| author_facet | Giulio Zizzo Lei Ren |
| author_sort | Giulio Zizzo |
| collection | DOAJ |
| description | Progress has been made enabling expensive, high-end inertial measurement units (IMUs) to be used as tracking sensors. However, the cost of these IMUs is prohibitive to their widespread use, and hence the potential of low-cost IMUs is investigated in this study. A wearable low-cost sensing system consisting of IMUs and ultrasound sensors was developed. Core to this system is an extended Kalman filter (EKF), which provides both zero-velocity updates (ZUPTs) and Heuristic Drift Reduction (HDR). The IMU data was combined with ultrasound range measurements to improve accuracy. When a map of the environment was available, a particle filter was used to impose constraints on the possible user motions. The system was therefore composed of three subsystems: IMUs, ultrasound sensors, and a particle filter. A Vicon motion capture system was used to provide ground truth information, enabling validation of the sensing system. Using only the IMU, the system showed loop misclosure errors of 1% with a maximum error of 4–5% during walking. The addition of the ultrasound sensors resulted in a 15% reduction in the total accumulated error. Lastly, the particle filter was capable of providing noticeable corrections, which could keep the tracking error below 2% after the first few steps. |
| first_indexed | 2024-04-11T22:28:56Z |
| format | Article |
| id | doaj.art-032b0d8b6fb044ec87f8c996148e4e62 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:28:56Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-032b0d8b6fb044ec87f8c996148e4e622022-12-22T03:59:33ZengMDPI AGSensors1424-82202017-12-011712286610.3390/s17122866s17122866Position Tracking During Human Walking Using an Integrated Wearable Sensing SystemGiulio Zizzo0Lei Ren1School of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester M13 9PL, UKSchool of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester M13 9PL, UKProgress has been made enabling expensive, high-end inertial measurement units (IMUs) to be used as tracking sensors. However, the cost of these IMUs is prohibitive to their widespread use, and hence the potential of low-cost IMUs is investigated in this study. A wearable low-cost sensing system consisting of IMUs and ultrasound sensors was developed. Core to this system is an extended Kalman filter (EKF), which provides both zero-velocity updates (ZUPTs) and Heuristic Drift Reduction (HDR). The IMU data was combined with ultrasound range measurements to improve accuracy. When a map of the environment was available, a particle filter was used to impose constraints on the possible user motions. The system was therefore composed of three subsystems: IMUs, ultrasound sensors, and a particle filter. A Vicon motion capture system was used to provide ground truth information, enabling validation of the sensing system. Using only the IMU, the system showed loop misclosure errors of 1% with a maximum error of 4–5% during walking. The addition of the ultrasound sensors resulted in a 15% reduction in the total accumulated error. Lastly, the particle filter was capable of providing noticeable corrections, which could keep the tracking error below 2% after the first few steps.https://www.mdpi.com/1424-8220/17/12/2866Kalman filterpedestrian dead reckoningwearable sensorsIMU navigation |
| spellingShingle | Giulio Zizzo Lei Ren Position Tracking During Human Walking Using an Integrated Wearable Sensing System Sensors Kalman filter pedestrian dead reckoning wearable sensors IMU navigation |
| title | Position Tracking During Human Walking Using an Integrated Wearable Sensing System |
| title_full | Position Tracking During Human Walking Using an Integrated Wearable Sensing System |
| title_fullStr | Position Tracking During Human Walking Using an Integrated Wearable Sensing System |
| title_full_unstemmed | Position Tracking During Human Walking Using an Integrated Wearable Sensing System |
| title_short | Position Tracking During Human Walking Using an Integrated Wearable Sensing System |
| title_sort | position tracking during human walking using an integrated wearable sensing system |
| topic | Kalman filter pedestrian dead reckoning wearable sensors IMU navigation |
| url | https://www.mdpi.com/1424-8220/17/12/2866 |
| work_keys_str_mv | AT giuliozizzo positiontrackingduringhumanwalkingusinganintegratedwearablesensingsystem AT leiren positiontrackingduringhumanwalkingusinganintegratedwearablesensingsystem |