Research Group Lifestyle and Health, Utrecht University of Applied Sciences, Utrecht, The Netherlands.
Department of Human Movement Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
PLoS One. 2024 Oct 4;19(10):e0304558. doi: 10.1371/journal.pone.0304558. eCollection 2024.
Variational AutoEncoders (VAE) might be utilized to extract relevant information from an IMU-based gait measurement by reducing the sensor data to a low-dimensional representation. The present study explored whether VAEs can reduce IMU-based gait data of people after stroke into a few latent features with minimal reconstruction error. Additionally, we evaluated the psychometric properties of the latent features in comparison to gait speed, by assessing 1) their reliability; 2) the difference in scores between people after stroke and healthy controls; and 3) their responsiveness during rehabilitation.
We collected test-retest and longitudinal two-minute walk-test data using an IMU from people after stroke in clinical rehabilitation, as well as from a healthy control group. IMU data were segmented into 5-second epochs, which were reduced to 12 latent-feature scores using a VAE. The between-day test-retest reliability of the latent features was assessed using ICC-scores. The differences between the healthy and the stroke group were examined using an independent t-test. Lastly, the responsiveness was determined as the number of individuals who significantly changed during rehabilitation.
In total, 15,381 epochs from 107 people after stroke and 37 healthy controls were collected. The VAE achieved data reconstruction with minimal errors. Five latent features demonstrated good-to-excellent test-retest reliability. Seven latent features were significantly different between groups. We observed changes during rehabilitation for 21 and 20 individuals in latent-feature scores and gait speed, respectively. However, the direction of the change scores of the latent features was ambiguous. Only eleven individuals exhibited changes in both latent-feature scores and gait speed.
VAEs can be used to effectively reduce IMU-based gait assessment to a concise set of latent features. Some latent features had a high test-retest reliability and differed significantly between healthy controls and people after stroke. Further research is needed to determine their clinical applicability.
变分自编码器（VAE）可用于通过将传感器数据降低到低维表示来从基于 IMU 的步态测量中提取相关信息。本研究探讨了 VAE 是否可以将基于 IMU 的中风后步态数据减少到几个潜在特征，而重建误差最小。此外，我们通过评估 1）可靠性；2）中风后患者与健康对照组之间的得分差异；以及 3）康复过程中的反应性，来评估潜在特征与步态速度的心理测量学特性。
我们从临床康复中的中风患者以及健康对照组中收集了使用 IMU 进行的测试-重测和纵向两分钟步行测试数据。将 IMU 数据分成 5 秒的段，然后使用 VAE 将其减少到 12 个潜在特征分数。使用 ICC 评分评估潜在特征的日内测试-重测可靠性。使用独立 t 检验检查健康组和中风组之间的差异。最后，将响应性确定为在康复过程中显著变化的个体数量。
总共从 107 名中风后患者和 37 名健康对照组中收集了 15381 个时段。VAE 实现了数据重建，误差最小。五个潜在特征具有良好到极好的测试-重测可靠性。七个潜在特征在组间存在显著差异。我们观察到在康复过程中，潜在特征得分和步态速度分别有 21 人和 20 人发生变化。然而，潜在特征得分变化的方向并不明确。只有 11 个人在潜在特征得分和步态速度上都有变化。
VAE 可用于有效地将基于 IMU 的步态评估减少到一组简洁的潜在特征。一些潜在特征具有较高的测试-重测可靠性，并且在健康对照组和中风患者之间存在显著差异。需要进一步研究来确定它们的临床适用性。
