AI Article Synopsis
- Gait identification utilizes body movement patterns to recognize individuals from a distance.
- The study involves using an iPhone's accelerometer and gyrometer to gather data on gait characteristics like frequency and symmetry.
- A new weighted voting technique is introduced for identifying gait, supported by experiments that validate its effectiveness through actual data collection and simulations.
Article Abstract
Gait identification is a valuable approach to identify humans at a distance. In this paper, gait characteristics are analyzed based on an iPhone's accelerometer and gyrometer,and a new approach is proposed for gait identification. Specifically, gait datasets are collected by the triaxial accelerometer and gyrometer embedded in an iPhone. Then, the datasets are processed to extract gait characteristic parameters which include gait frequency, symmetry coefficient, dynamic range and similarity coefficient of characteristic curves. Finally, a weighted voting scheme dependent upon the gait characteristic parameters is proposed forgait identification. Four experiments are implemented to validate the proposed scheme. The attitude and acceleration solutions are verified by simulation. Then the gait characteristics are analyzed by comparing two sets of actual data, and the performance of the weighted voting identification scheme is verified by 40 datasets of 10 subjects.
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Source |
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208212 | PMC |
| http://dx.doi.org/10.3390/s140917037 | DOI Listing |
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