- Fall 2018-present
This project is a part of the Tustworthy Health & Wellness (THaW) project, which is an NSF-funded project that tackles many of the research challenges to provide trustworthy information systems for health and wellness.
Currently, in-shoe force sensors have been widely used for step counting and gait analysis. However, it has not been realized that in-shoe force sensors are also capable of tracking walking paths. In this project, we design an indoor walking path tracking method based on in-shoe force sensors. We show that, based on the force signals from a user’s shoes, it is possible to estimate the walking direction change and the stride length of each step with machine learning techniques. We further apply a particle filter to combine this information with the constraint of barriers in floor maps, and thus can determine the walking path and the current position of the user. To solve the problem of the low accuracy caused by cumulative walking direction errors, we improve the particle filter by designing the direction correction algorithm. Moreover, we propose the weight normalization method to handle the impact of handbags and backpacks. Our current experimental results show that, after a convergence phase, our system achieves the average location error of 0.9-1.3 m. Compared with traditional indoor tracking technologies, our system does not require the installation of wireless anchors, and has good robustness to environment changes such as the magnetic interference.
- Tuo Yu, Haiming Jin, Klara Nahrstedt, “ShoesLoc: In-Shoe Force Sensor-Based Indoor Walking Path Tracking “, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3(1), pp. 1-23, 2019.
This project is supported by the a collaborative award from the National Science Foundation (NSF award numbers CNS-1329686, 1329737,1330142 and 1330491).