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IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026

Cable-Driven Parallel Robot and Force Guidance Method for 3D Spatial Perception Training of Visually Impaired People.

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Chen Dapeng, Lou Juncheng, Ma Zhenjie, Zhuang Zhou, Zeng Hong, Liu Jia, Song Aiguo

Аннотация

For blind or visually impaired (BVI), the acquisition of three-dimensional (3D) spatial relationships highly depends on touch, proprioception, and active movement experience. Existing training methods are mostly based on 2D tactile graphics or static models and therefore have difficulty providing continuous, controllable, and quantifiable 3D body-motion guidance. To address this issue, this study proposes a 3D spatial perception training method based on cable-driven parallel robot (CDPR) position guidance and virtual-constraint feedback. The CDPR serves as a force-feedback interaction platform, integrating visual pose measurement, a virtual-constraint force field, tension mapping, and hybrid force-position control to transform spatial information into continuous and perceivable body-motion guidance. Device performance experiments showed that the 3D tracking root-mean-square errors (RMSEs) for a circular trajectory with a diameter of 100 mm and a square trajectory with a side length of 100 mm were 4.35 mm and 3.31 mm, respectively. When the desired constraint force was 20 N, the measured force-output RMSE was 1.13 N. Furthermore, 24 BVI subjects were recruited for a two-group parallel controlled experiment to compare the CDPR training group and the control group in direction perception, distance perception, 3D endpoint localization, and trajectory reproduction tasks. The results showed that the CDPR training group achieved significantly greater improvements in all four tasks and maintained lower errors in untrained but related transfer tasks. Trajectory-to-object matching results showed that subjects could use CDPR-demonstrated geometric trajectories to identify corresponding physical objects through haptic exploration.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты