Gait patterns in unstable older patients related with vestibular hypofunction. Preliminary results in assessment with time-frequency analysis.
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| Title: | Gait patterns in unstable older patients related with vestibular hypofunction. Preliminary results in assessment with time-frequency analysis. |
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| Authors: | de Izaguirre, Francisco (AUTHOR), del Castillo, Mariana (AUTHOR), Ferreira, Enrique D. (AUTHOR), Suárez, Hamlet (AUTHOR) |
| Source: | Acta Oto-Laryngologica. Feb2026, Vol. 146 Issue 2, p153-158. 6p. |
| Subjects: | Vestibular apparatus, Research funding, Diagnosis, Gait in humans, Wearable technology, Diagnostic errors, Convolutional neural networks, Support vector machines, Case-control method, Digital image processing, Machine learning, Comparative studies, Data analysis software, Algorithms, Vestibular function tests, Old age |
| Abstract (English): | Background: Gait instability and falls significantly impact life quality and morbi-mortality in elderly populations. Early diagnosis of gait disorders is one of the most effective approaches to minimize severe injuries. Objective: To find a gait instability pattern in older adults through an image representation of data collected by a single sensor. Methods: A sample of 13 older adults (71-85 years old) with instability by vestibular hypofunction is compared to a sample of 19 adults (21-75 years old) without instability and normal vestibular function. Image representations of the gait signals acquired on a specific walk path were generated using a continuous wavelet transform and analyzed as a texture using grey level co-occurrence matrix metrics as features. A support vector machine (SVM) algorithm was used to discriminate subjects. Results: First results show a good classification performance. According to analysis of extracted features, most information relevant to instability is concentrated in the medio-lateral acceleration (X axis) and the frontal plane angular rotation (Z axis gyroscope). Performing a ten-fold cross-validation through the first ten seconds of the sample dataset, the algorithm achieves a 92,3 F1 score corresponding to 12 true-positives, 1 false positive and 1 false negative. Discussion: This preliminary report suggests that the method has potential use in assessing gait disorders in controlled and non-controlled environments. It suggests that deep learning methods could be explored given the availability of a larger population and data samples. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): | 步态不稳和跌倒严重影响老年人的生活质量和死亡率。早期诊断步态障碍是减少严重创伤的最有效方法之一。 通过单个传感器收集的数据生成的图像, 找到老年人的步态不稳模式。 将13名因前庭功能低下而导致步态不稳的老年人(7185岁)与19名无步态不稳并且前庭功能正常的成年人(21至75岁)进行比较。使用连续小波变换生成了在特定步行路径上获取的步态信号的图像表示, 并使用灰度共生矩阵度量作为特征将其作为结构进行分析。使用支持向量机 (SVM) 算法来区分受试者。 初步结果显示分类性能良好。根据对提取特征的分析, 与不稳定性相关的大多数信息集中在内外加速度(X轴)和前平面角旋转(Z轴陀螺仪)中。通过对样本数据集的前十秒进行十倍交叉验证, 用该算法得到了 92.3 F1 评分, 对应于 12 个真阳性、1 个假阳性和 1 个假阴性。 本初步报告表明, 该方法在评估受控和非受控环境中的步态障碍方面具有潜在用途。它表明, 鉴于有更大的人口和数据样本的存在, 可以探索深度学习方法。 [ABSTRACT FROM AUTHOR] |
| Copyright of Acta Oto-Laryngologica is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Background: Gait instability and falls significantly impact life quality and morbi-mortality in elderly populations. Early diagnosis of gait disorders is one of the most effective approaches to minimize severe injuries. Objective: To find a gait instability pattern in older adults through an image representation of data collected by a single sensor. Methods: A sample of 13 older adults (71-85 years old) with instability by vestibular hypofunction is compared to a sample of 19 adults (21-75 years old) without instability and normal vestibular function. Image representations of the gait signals acquired on a specific walk path were generated using a continuous wavelet transform and analyzed as a texture using grey level co-occurrence matrix metrics as features. A support vector machine (SVM) algorithm was used to discriminate subjects. Results: First results show a good classification performance. According to analysis of extracted features, most information relevant to instability is concentrated in the medio-lateral acceleration (X axis) and the frontal plane angular rotation (Z axis gyroscope). Performing a ten-fold cross-validation through the first ten seconds of the sample dataset, the algorithm achieves a 92,3 F1 score corresponding to 12 true-positives, 1 false positive and 1 false negative. Discussion: This preliminary report suggests that the method has potential use in assessing gait disorders in controlled and non-controlled environments. It suggests that deep learning methods could be explored given the availability of a larger population and data samples. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00016489 |
| DOI: | 10.1080/00016489.2025.2450221 |
