A cable-driven parallel rehabilitation robot for active training of supine patients’ lower limbs

• A new admittance model for the under-constrained cable-suspended system was proposed. • Rigid and compliant tunnel models respectively improve strength and control ability. • An autonomous posture identification method was proposed without additional sensors. • Prototype experiments confirmed the effectiveness of the proposed control model. Patients with lower limb motor dysfunction typically require supine rehabilitation during early recovery. Compared to rigid-structure robots, cable-driven supine rehabilitation robots offer distinct safety and cost advantages. However, existing such robots are generally limited to passive training, particularly those with under-constrained cable-suspended structures. This study proposes a novel admittance model for the under-constrained cable-suspended system for active training in supine status, providing resistance along a specified trajectory. To enhance the training experience, two tunnel models orthogonal to the trajectory were proposed, rigid mode and compliant mode. They meet personalized rehabilitation requirements better by adjusting the impedance model parameters. To prove the efficiency of the under-constrained active training method, a cable-driven parallel robot (CDPR) for lower limb supine training was developed, achieving multi-joint lower limb movement via four cables. Considering that positioning of patient relative to the proposed robot is necessary for performing active training, an autonomous patient position identification method is introduced, without extra sensors. Experiments on the prototype confirmed the position identification method’s feasibility and the under-constrained active training control model’s effectiveness in both tunnel modes.

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Publication Details

Journal
Mechanism and Machine Theory
Published
2025-11-24
DOI
https://doi.org/10.1016/j.mechmachtheory.2025.106300
Primary Topic
Stroke Rehabilitation and Recovery
Type
article
Field-Weighted Citation Impact
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article

A cable-driven parallel rehabilitation robot for active training of supine patients’ lower limbs

Lu Bai, Jian S. Dai, Rongjie Kang, Zhibin Song et al.
Mechanism and Machine Theory
Stroke Rehabilitation and Recovery
article

A cable-driven parallel rehabilitation robot for active training of supine patients’ lower limbs

Lu Bai, Jian S. Dai, Rongjie Kang, Zhibin Song, Yuze Zhang
article en

Abstract

• A new admittance model for the under-constrained cable-suspended system was proposed. • Rigid and compliant tunnel models respectively improve strength and control ability. • An autonomous posture identification method was proposed without additional sensors. • Prototype experiments confirmed the effectiveness of the proposed control model. Patients with lower limb motor dysfunction typically require supine rehabilitation during early recovery. Compared to rigid-structure robots, cable-driven supine rehabilitation robots offer distinct safety and cost advantages. However, existing such robots are generally limited to passive training, particularly those with under-constrained cable-suspended structures. This study proposes a novel admittance model for the under-constrained cable-suspended system for active training in supine status, providing resistance along a specified trajectory. To enhance the training experience, two tunnel models orthogonal to the trajectory were proposed, rigid mode and compliant mode. They meet personalized rehabilitation requirements better by adjusting the impedance model parameters. To prove the efficiency of the under-constrained active training method, a cable-driven parallel robot (CDPR) for lower limb supine training was developed, achieving multi-joint lower limb movement via four cables. Considering that positioning of patient relative to the proposed robot is necessary for performing active training, an autonomous patient position identification method is introduced, without extra sensors. Experiments on the prototype confirmed the position identification method’s feasibility and the under-constrained active training control model’s effectiveness in both tunnel modes.

Mechanism and Machine TheoryVol. 218
University of London (GB), Tianjin University (CN), King's College London (GB), Southern University of Science and Technology (CN), University College London (GB)
Openalex Percentile: Top 57%
Stroke Rehabilitation and Recovery
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