# Data-Driven Gait Segmentation for Walking Assistance in a Lower-Limb Assistive Device

## TL;DR

This paper introduces a data-driven algorithm for real-time gait segmentation and hybrid mode detection in walking assistive devices, enabling improved control and assistance without relying on contact sensors or predefined gait models.

## Contribution

The authors develop a novel data-driven method for real-time hybrid mode detection and gait segmentation applicable to assistive devices, without requiring predefined gait phases or contact sensors.

## Key findings

- Successfully modeled hybrid dynamics of a simulated hopper for control.
- Accurately differentiated gait phases and contact events in human walking data.
- Enabled online gait segmentation using only kinematic data from joints.

## Abstract

Hybrid systems, such as bipedal walkers, are challenging to control because of discontinuities in their nonlinear dynamics. Little can be predicted about the systems' evolution without modeling the guard conditions that govern transitions between hybrid modes, so even systems with reliable state sensing can be difficult to control. We propose an algorithm that allows for determining the hybrid mode of a system in real-time using data-driven analysis. The algorithm is used with data-driven dynamics identification to enable model predictive control based entirely on data. Two examples---a simulated hopper and experimental data from a bipedal walker---are used. In the context of the first example, we are able to closely approximate the dynamics of a hybrid SLIP model and then successfully use them for control in simulation. In the second example, we demonstrate gait partitioning of human walking data, accurately differentiating between stance and swing, as well as selected subphases of swing. We identify contact events, such as heel strike and toe-off, without a contact sensor using only kinematics data from the knee and hip joints, which could be particularly useful in providing online assistance during walking. Our algorithm does not assume a predefined gait structure or gait phase transitions, lending itself to segmentation of both healthy and pathological gaits. With this flexibility, impairment-specific rehabilitation strategies or assistance could be designed.