Physics

Trajectory

Gait analysis

Mathematics

Preferred walking speed

Statistics

Astronomy

Engineering

Surgery

Gait

Artificial intelligence

Standard deviation

Cadence

Ankle

Computer science

Pose

Swing

Computer vision

Kinematics

Medicine

Classical mechanics

Mechanical engineering

Convolutional neural network

Physical medicine and rehabilitation

Digital Biomarkers

Recent advancements in deep learning have produced significant progress in markerless human pose estimation, making it possible to estimate human kinematics from single camera videos without the need for reflective markers and specialized labs equipped with motion capture systems. Such algorithms have the potential to enable the quantification of clinical metrics from videos recorded with a handheld camera. Here we used DeepLabCut, an open-source framework for markerless pose estimation, to fine-tune a deep network to track 5 body keypoints (hip, knee, ankle, heel, and toe) in 82 below-waist videos of 8 patients with stroke performing overground walking during clinical assessments. We trained the pose estimation model by labeling the keypoints in 2 frames per video and then trained a convolutional neural network to estimate 5 clinically relevant gait parameters (cadence, double support time, swing time, stance time, and walking speed) from the trajectory of these keypoints. These results were then compared to those obtained from a clinical system for gait analysis (GAITRite®, CIR Systems). Absolute accuracy (mean error) and precision (standard deviation of error) for swing, stance, and double support time were within 0.04 ± 0.11 s; Pearson’s correlation with the reference system was moderate for swing times (&lt;i&gt;r&lt;/i&gt; = 0.4–0.66), but stronger for stance and double support time (&lt;i&gt;r&lt;/i&gt; = 0.93–0.95). Cadence mean error was −0.25 steps/min ± 3.9 steps/min (&lt;i&gt;r&lt;/i&gt; = 0.97), while walking speed mean error was −0.02 ± 0.11 m/s (&lt;i&gt;r&lt;/i&gt; = 0.92). These preliminary results suggest that single camera videos and pose estimation models based on deep networks could be used to quantify clinically relevant gait metrics in individuals poststroke, even while using assistive devices in uncontrolled environments. Such development opens the door to applications for gait analysis both inside and outside of clinical settings, without the need of sophisticated equipment.

Video-Based Pose Estimation for Gait Analysis in Stroke Survivors during Clinical Assessments: A Proof-of-Concept Study