# Enhancing Gait Symmetry via Intact Limb Kinematic Mapping Control of a Hip Disarticulation Prosthesis

## TL;DR

A new powered hip prosthesis improves gait symmetry by using a neural network to mimic natural limb movement.

## Contribution

A GRU-based kinematic mapping control strategy is introduced to enhance gait symmetry in hip disarticulation prostheses.

## Key findings

- Trajectory similarity reached 98.12% at 3.0 km/h walking speed.
- Hip flexion angle improved by 84.00% with the powered prosthesis.
- Stride symmetry improved by 23.21% (SI) and 19.28% (RII).

## Abstract

Conventional hip disarticulation prostheses often require amputees to produce limited leg-lifting torque through exaggerated pelvic motion, resulting in complex control and pronounced gait abnormalities. To overcome the limitations, we present a mapping control strategy for a powered hip disarticulation prosthesis aimed at improving gait symmetry. A quaternion-based method was implemented to capture hip joint kinematics, while a gated recurrent unit (GRU) neural network was trained to model the kinematic relationship between the intact and prosthetic limbs, enabling biomimetic trajectory control. Validation experiments showed that trajectory similarity between predicted and actual motions increased with walking speed, reaching 98.12% at 3.0 km/h. Comparative walking tests revealed an 84.00% improvement in hip flexion angle with the powered prosthesis over conventional designs. Notable improvements in gait symmetry were observed: stride symmetry (measured by SI and RII) improved by 23.21% and 19.28%, respectively, while hip trajectory symmetry increased by 68.07% (SI) and 47.59% (RII). These results confirm that the GRU-based kinematic mapping model offers robust trajectory prediction and that the powered prosthesis significantly enhances gait symmetry, delivering more natural and biomimetic motion.