A Gaze-Guided Human Locomotion Intention Recognition and Volitional Control Method for Knee-Ankle Prostheses

This paper presents a novel gaze-guided volitional control method for knee-ankle prostheses, designed to enhance the precision and intuitiveness of prosthetic control in complex locomotion tasks. The method utilizes an eye-tracking system coupled with an RGBD camera to estimate 3D gaze points, which are then used to construct a gaze attention map. This map serves as the basis for predicting human locomotion intentions, enabling the prosthesis to accurately step on the intended foot placement. Experimental validation was conducted with both a healthy subject and an amputee subject, focusing on level-ground walking and stair ascent tasks. The results demonstrated that the prosthesis could achieve precise foot placement, with a mean error of 1.22 cm ± 3.14 cm for the healthy subject and 3.40 cm ± 0.66 cm for the amputee subject during level-ground walking. In stair ascent tasks, the system successfully adapted to various gait patterns, with the prosthetic joint angles closely matching natural human movements. The proposed method also effectively reduced the root mean square error (RMSE) of 3D gaze estimation to 2.70 cm, 2.63 cm, and 1.97 cm along the x, y, and z axes, respectively. These findings indicate that the gaze-guided volitional control method not only enhances the functionality of knee-ankle prostheses but also offers a more intuitive interface for users, aligning prosthetic actions with the user’s visual focus and intentions.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Cognitive and Developmental Systems
Published
2025-04-16
DOI
https://doi.org/10.1109/tcds.2025.3561080
Citations
1
Primary Topic
Gaze Tracking and Assistive Technology
Type
article
Field-Weighted Citation Impact
1.94
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Gaze-Guided Human Locomotion Intention Recognition and Volitional Control Method for Knee-Ankle Prostheses

Chenglong Fu, Xinxing Chen, Yuxuan Wang, Shucong Yin et al.
1 citations
IEEE Transactions on Cognitive and Developmental Systems
Gaze Tracking and Assistive Technology
1.94
article

A Gaze-Guided Human Locomotion Intention Recognition and Volitional Control Method for Knee-Ankle Prostheses

Chenglong Fu, Xinxing Chen, Yuxuan Wang, Shucong Yin, Chuheng Chen, Jian Huang, Zhong Chen
article en
1 citations

Abstract

This paper presents a novel gaze-guided volitional control method for knee-ankle prostheses, designed to enhance the precision and intuitiveness of prosthetic control in complex locomotion tasks. The method utilizes an eye-tracking system coupled with an RGBD camera to estimate 3D gaze points, which are then used to construct a gaze attention map. This map serves as the basis for predicting human locomotion intentions, enabling the prosthesis to accurately step on the intended foot placement. Experimental validation was conducted with both a healthy subject and an amputee subject, focusing on level-ground walking and stair ascent tasks. The results demonstrated that the prosthesis could achieve precise foot placement, with a mean error of 1.22 cm ± 3.14 cm for the healthy subject and 3.40 cm ± 0.66 cm for the amputee subject during level-ground walking. In stair ascent tasks, the system successfully adapted to various gait patterns, with the prosthetic joint angles closely matching natural human movements. The proposed method also effectively reduced the root mean square error (RMSE) of 3D gaze estimation to 2.70 cm, 2.63 cm, and 1.97 cm along the x, y, and z axes, respectively. These findings indicate that the gaze-guided volitional control method not only enhances the functionality of knee-ankle prostheses but also offers a more intuitive interface for users, aligning prosthetic actions with the user’s visual focus and intentions.

IEEE Transactions on Cognitive and Developmental SystemsVol. 18(1)
Shanghai Jiao Tong University (CN), Southern University of Science and Technology (CN), Huazhong University of Science and Technology (CN)
Openalex Percentile: Top 18%
Gaze Tracking and Assistive Technology
1.94
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Gaze-Guided Human Locomotion Intention Recognition and Volitional Control Method for Knee-Ankle Prostheses — Chenglong Fu, Xinxing Chen, et al. · IEEE Transactions on Cognitive and Developmental Systems (2025) | TGRS Research Map | TGRS