DimRad: A Radar-Based Perception System for Prosthetic Leg Barrier Traversing

TL;DR

This paper introduces DimRad, a radar-based perception system that enables prosthetic legs to autonomously detect and traverse staircases by combining radar sensing, IMU data, and neural network correction for improved obstacle navigation.

Contribution

The paper presents a novel radar-based perception system with neural network error correction for accurate staircase dimensioning in prosthetic legs.

Findings

Achieved 1 cm accuracy in staircase dimension estimation.

Successfully integrated the system into a microcontroller for embedded use.

Demonstrated effective obstacle detection for autonomous stair traversal.

Abstract

Lower extremity amputees face challenges in natural locomotion, which is partially compensated using powered assistive systems, e.g., micro-processor controlled prosthetic leg. In this paper, a radar-based perception system is proposed to assist prosthetic legs for autonomous obstacle traversing, focusing on multiple-step staircases. The presented perception system is composed of a radar module operating with a multiple-input-multiple-output (MIMO) configuration to localize consecutive stair corners. An inertial measurement unit (IMU) is integrated for coordinates correction due to the angular dis-positioning that occurs because of the knee angular motion. The captured information from both sensors is used for staircase dimensioning (depth and height). A shallow neural network (NN) is proposed to model the error due to the hardware limitations and enhance the dimension estimation accuracy (1 cm). The algorithm is implemented on a microcontroller subsystem of the radar kit to qualify the perception system for embedded integration in powered prosthetic legs.