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<p>Gait analysis systems are essential for rehabilitation but are often time-consuming and less accessible in low- and middle-income countries. GAITWell was developed to address these challenges with its portable and modular design for automated gait data collection and analysis. This study evaluates its methodological properties. GAITWell uses discrete binary sensors on interconnected plates to capture gait data, analyzed using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, which identifies key reference points like foot contact and toe-off. DBSCAN detects clusters of arbitrary shapes and sizes, separates noise from data, and identifies natural patterns within the data space without prior group knowledge. Each plate measures 44&#xa0;cm × 37&#xa0;cm and has an 11 × 7 sensor array with 4&#xa0;cm spacing. Test–retest reliability was evaluated using the intraclass correlation coefficient (ICC), standard error of the mean (SEM), and Bland–Altman plots. Concurrent validity was assessed by comparing GAITWell measurements to those from the Qualisys Pro-Reflex system. Results: 38 healthy adults participated (average age 33.2&#xa0;years, SD 13.0). Correlations between GAITWell and Qualisys ranged from moderate (right step length) to very high (gait speed, cycle time, right and left step time, left step length, stance time, swing time, right and left cadence, and base of support). Moderate to good agreement was found for gait speed, cycle time, stride length, right and left step length, right and left step time, and stance and swing time, but poor agreement was observed for double support times, right and left cadence, and base of support. Preliminary analysis suggests that increasing sensor resolution could reduce measurement error by 70%. Conclusions: GAITWell is a promising tool, with future research focusing on enhancing sensor accuracy for improved reliability.</p>

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Validity and reliability of GAITWell portable modular system for gait analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月16日

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  • Wellingtânia D. Dias,
  • Renata Kirkwood,
  • Iury C. Brito,
  • Ivo O. Capanema,
  • Meinhard Sesselmann,
  • Frederico Coelho,
  • Claysson Bruno Santos Vimieiro,
  • Rudolf Huebner

摘要

Gait analysis systems are essential for rehabilitation but are often time-consuming and less accessible in low- and middle-income countries. GAITWell was developed to address these challenges with its portable and modular design for automated gait data collection and analysis. This study evaluates its methodological properties. GAITWell uses discrete binary sensors on interconnected plates to capture gait data, analyzed using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, which identifies key reference points like foot contact and toe-off. DBSCAN detects clusters of arbitrary shapes and sizes, separates noise from data, and identifies natural patterns within the data space without prior group knowledge. Each plate measures 44 cm × 37 cm and has an 11 × 7 sensor array with 4 cm spacing. Test–retest reliability was evaluated using the intraclass correlation coefficient (ICC), standard error of the mean (SEM), and Bland–Altman plots. Concurrent validity was assessed by comparing GAITWell measurements to those from the Qualisys Pro-Reflex system. Results: 38 healthy adults participated (average age 33.2 years, SD 13.0). Correlations between GAITWell and Qualisys ranged from moderate (right step length) to very high (gait speed, cycle time, right and left step time, left step length, stance time, swing time, right and left cadence, and base of support). Moderate to good agreement was found for gait speed, cycle time, stride length, right and left step length, right and left step time, and stance and swing time, but poor agreement was observed for double support times, right and left cadence, and base of support. Preliminary analysis suggests that increasing sensor resolution could reduce measurement error by 70%. Conclusions: GAITWell is a promising tool, with future research focusing on enhancing sensor accuracy for improved reliability.