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Updated: Jul 28, 2026
Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
Published on: November 7, 2014
Evaluación de la marcha basada en sensores portátiles en la mielopatía espondilótica cervical: Detección de
Zhenxu Li1, Dazhao Tie1, Wenbin Bai1
1From Peking University Third Hospital, Department of Orthopaedics; Engineering Research Center of Bone and Joint Precision Medicine; Beijing Key Laboratory of Spinal Disease Research; 49 North Garden Road, Haidian District, Beijing, 100191.
Background Context:
Current severity assessment methods for cervical spondylotic myelopathy (CSM) overlook asymptomatic gait dysfunction and potential heterogeneous gait impairment. Also, gait alterations for disease progression remain inadequately characterized.
Purpose:
This study aimed to detect asymptomatic gait dysfunction, achieve accurate detection of CSM, and discover heterogeneous gait impairment in CSM using wearable sensor-based gait evaluation.
Study Design:
Cross-sectional study.
Patient Sample:
45 CSM patients and 39 age-matched healthy controls (HC) were recruited for this study.
Outcome Measures:
Waist and lower extremity kinematics and spatiotemporal parameters.
Methods:
This study utilized a seven-sensor wearable inertial measurement unit (IMU) system to obtain spatiotemporal and kinematic (waist and lower extremity joint angle time series in triaxial planes) during 2-minute walk tests (2MWT).Group comparisons were performed using the Kruskal-Wallis test to assess differences in gait parameters between lower extremity asymptomatic CSM patients (CSM-ALE) and healthy controls (HC), and between the overall CSM cohort and HC.. Machine learning was used for asymptomatic gait dysfunction and CSM detection. Graph convolutional network (GCN)-based unsupervised clustering was used for heterogeneous gait impairment recognition. The Pearson correlation coefficient was used to examine the relationships between gait parameters and Japanese Orthopaedic Association (JOA) scores in order to evaluate the applicability of gait metrics for monitoring CSM severity.
Results:
For gait parameters, CSM patients showed significantly longer turn time (3.06 vs. 2.60 s), longer gait cycle time (1.28 vs. 1.21 s), greater waist flexion angle (4.00° vs. 2.95°), shorter step length (0.66 vs. 0.83 m), slower single-step speed (1.03 vs. 1.38 m/s), and reduced hip (76.31 vs. 105.19°/s) and knee angular velocities (146.98 vs. 180.77°/s) compared with healthy controls (HC). Using fused kinematic time-series data, the SVM classifier achieved excellent discrimination between CSM-ALE patients and HC (AUC = 0.996) as well as between all CSM patients and HC (AUC = 0.997), outperforming analyses based on individual gait parameters. Unsupervised GCN clustering revealed two distinct gait impairment patterns. Pattern I was characterized by prolonged turning time (4.03 vs. 2.71 s) and increased waist flexion angle (9.27° vs. 2.99°) compared with Pattern II. In contrast, Pattern II was marked by reduced ankle velocity (81.05 vs. 100.82°/s) but preserved turning time (2.71 vs. 2.60 s vs. HC), suggesting distal motor impairment with relatively maintained balance control. JOA scores was correlated with gait speed (r=0.431, p=0.003), swing phase duration (r=0.444, p=0.002), and knee angular velocity (r=0.373, p=0.012).
Conclusion:
Wearable sensor-based gait evaluation can effectively distinguish CSM patients, including CSM-ALE patients, from HC, and reveal distinct patterns of gait impairment. These findings highlight its clinical value not only for early diagnosis but also for potentially enabling intervention within a therapeutic window before significant functional decline occurs.
