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Pedestrian Tracking with Shoe-Mounted Inertial Sensors
Abstract
A navigation system that tracks the location of a person on foot is useful for finding and rescuing firefighters or other emergency first responders, or for location-aware computing, personal navigation assistance, mobile 3D audio, and mixed or augmented reality applications. One of the main obstacles to the real-world deployment of location-sensitive wearable computing, including mixed reality (MR), is that current position-tracking technologies require an instrumented, marked, or premapped environment. At InterSense, we've developed a system called NavShoe, which uses a new approach to position tracking based on inertial sensing. Our wireless inertial sensor is small enough to easily tuck into the shoelaces, and sufficiently low power to run all day on a small battery. Although it can't be used alone for precise registration of close-range objects, in outdoor applications augmenting distant objects, a user would barely notice the NavShoe's meter-level error combined with any error in the head's assumed location relative to the foot. NavShoe can greatly reduce the database search space for computer vision, making it much simpler and more robust. The NavShoe device provides not only robust approximate position, but also an extremely accurate orientation tracker on the foot.
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Cited by
High-precision, consistent EKF-based visual-inertial odometry
Li, M., & Mourikis, A. I. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913481251 · Cited by 813
Evaluation of zero-velocity detectors for foot-mounted inertial navigation systems
Skog, I., Nilsson, J.-O., & Handel, P. (2010) · 2010 International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin.2010.5646936 · Cited by 213
Measurement of foot placement and its variability with inertial sensors
Rebula, J. R., Ojeda, L. V., Adamczyk, P. G., & Kuo, A. D. (2013) · Gait & Posture · doi:10.1016/j.gaitpost.2013.05.012 · Cited by 180
A wearable system for gait training in subjects with Parkinson's disease
Casamassima, F., Ferrari, A., Milosevic, B., Ginis, P., Farella, E., & Rocchi, L. (2014) · Sensors · doi:10.3390/s140406229 · Cited by 131
Palmieri, F., Ficco, M., Pardi, S., & Castiglione, A. (2016) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2016.02.012 · Cited by 75
Smart Insole-Based Indoor Localization System for Internet of Things Applications
Chen, D., Cao, H., Chen, H., Zhu, Z., Qian, X., Xu, W., & Huang, M.-C. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2915791 · Cited by 51
Jao, C.-S., & Shkel, A. M. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3130120 · Cited by 18
Ziebold, R., Lanca, L., & Romanovas, M. (2017) · European Transport Research Review · doi:10.1007/s12544-016-0217-5 · Cited by 8
Multimodal gait analysis based on wearable inertial and microphone sensors
Wang, C., Wang, X., Long, Z., Yuan, J., Qian, Y., & Li, J. (2017) · 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/uic-atc.2017.8397481 · Cited by 7
Metadata
- Authors
- Foxlin, E.
- Venue
- IEEE Computer Graphics and Applications
- Published
- 2005-11
- Volume
- 25
- Issue
- 6
- Pages
- 38-46
- ISSN
- 0272-1716 (ISSN-L)
- OpenAlex
- W2056427752
- Cited by
- 1,399
- References
- 0
- FWCI
- 11.44
- Type
- journal article
- Language
- en
- Open access
- closed
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