Abstract
Gait symmetry is a clinically relevant indicator that distinguishes between normal gait and pathological gait, and it can provide a strong basis for mobility assessment and rehabilitation interventions. This article proposes a comprehensive quantitative assessment method for gait symmetry based on human electrostatic gait signals (EGSs). This study is the first application of EGSs to examine gait symmetry in terms of trend symmetry, amplitude symmetry, and time symmetry in 30 subjects, including healthy subjects (HSs), hemiplegic patients (HPs), and Parkinson's disease patients (PDs). The trend symmetry index ${(}\textit {SI}_{\text {tren}\text {d}}{)}$ compares the two sides of the continuous waveform of the gait waveforms using the eigenvector; the amplitude symmetry indexes ( $\textit {SI}_{\text {A}\_{}{\text {IC}}}, \textit {SI}_{\text {A}\_{}{\text {SE}}}$ ) compare the amplitude of EGSs at the moment of initial contact (IC) with the ground and separation (SE) from the ground; the time symmetry indexes ( $\textit {SI}_{\text {TS}\text {P}}, \textit {SI}_{\text {TSW}}$ ) compare the durations of the support phase and the swing phase. The results showed that the HPs and PDs showed significant asymmetry in gait trends, gait signal amplitude, and gait temporal parameters on both sides compared to HSs. In addition, the HPs showed greater asymmetry than the PDs. The Kruskal-Wallis test was used to verify that the distributions of the five symmetry indexes differed significantly among the three different groups ( ${p} < 0.05$ ); trend symmetry was the most effective feature for distinguishing among the three groups. This article provides a comprehensive and effective EGSs-based analysis of gait symmetry in terms of continuous gait waveforms and discrete gait parameters.
| Original language | English |
|---|---|
| Pages (from-to) | 13422-13432 |
| Number of pages | 11 |
| Journal | IEEE Sensors Journal |
| Volume | 23 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 15 Jun 2023 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Electrostatic field sensing
- electrostatic gait analysis
- gait symmetry
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