Work

A type-2 fuzzy inference-based approach enables walking speed estimation that adapts to inter-individual gait patterns

Year: 2024

Type: article

Abstract: OBJECTIVES: Individuals change walking speed by regulating step frequency (SF), stride length (SL), or a combination of both (FL combinations). However, existing methods of walking speed estimation ignore this regulatory mechanism. This paper aims to achieve accurate walking speed estimation while enabling adaptation to inter-individual speed regulation strategies. METHODS: We first extracted thigh features closely related to individual speed regulation based on a single thigh mounted IMU. Next, an interval type-2 fuzzy inference system was used to infer and quantify the individuals' speed regulation intentions, enabling speed estimation independent of inter-individual gait patterns. Experiments with five subjects walking on a treadmill at different speeds and with different gait patterns validated our method. RESULTS: The overall root mean square error (RMSE) for speed estimation was 0.0704 ± 0.0087 m/s, and the RMSE for different gait patterns was no more than 0.074 ± 0.005 m/s. CONCLUSIONS: The proposed method provides high-accuracy speed estimation. Moreover, our method can be adapted to different FL combinations without the need for individualised tuning or training of individuals with varying limb lengths and gait habits. We anticipate that the proposed method will help provide more intuitive speed adaptive control for rehabilitation robots, especially intelligent lower limb prostheses.

Source: Biomedizinische Technik/Biomedical Engineering

DOI: 10.1515/bmt-2024-0230

Authors: Linrong Li, Wenxiang Liao, Hongliu Yu

Institution: University of Shanghai for Science and Technology

Language: English

Topic: Prosthetics and Rehabilitation Robotics

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,489