Transforming Gait: Video-Based Spatiotemporal Gait Analysis

TL;DR

This paper presents a neural network that analyzes monocular videos to produce accurate, clinically meaningful gait parameters, enabling remote gait assessment with high precision.

Contribution

It introduces a novel neural network model trained on extensive data to derive detailed gait metrics from monocular videos, bridging the gap between lab-based and video-based gait analysis.

Findings

Accurately estimates gait cycle timing and joint kinematics from video.

Provides cycle-by-cycle gait parameters such as cadence and step length.

Achieves high correlation with traditional lab-based gait measurements.

Abstract

Human pose estimation from monocular video is a rapidly advancing field that offers great promise to human movement science and rehabilitation. This potential is tempered by the smaller body of work ensuring the outputs are clinically meaningful and properly calibrated. Gait analysis, typically performed in a dedicated lab, produces precise measurements including kinematics and step timing. Using over 7000 monocular video from an instrumented gait analysis lab, we trained a neural network to map 3D joint trajectories and the height of individuals onto interpretable biomechanical outputs including gait cycle timing and sagittal plane joint kinematics and spatiotemporal trajectories. This task specific layer produces accurate estimates of the timing of foot contact and foot off events. After parsing the kinematic outputs into individual gait cycles, it also enables accurate cycle-by-cycle estimates of cadence, step time, double and single support time, walking speed and step length.