Using workspace restrictiveness for adaptive velocity adjustment of assistive robots and upper limb exoskeletons.

Individuals with severe disabilities can benefit from assistive robotic systems (ARS) for performing activities of daily living. However, limited control interfaces are available for individuals who cannot use their hands for the control, and most of these interfaces require high effort to perform simple tasks. Therefore, autonomous and intelligent control strategies were proposed for assisting with the control in complex tasks. In this paper, we presented an autonomous and adaptive method for adjusting an assistive robot's velocity in different regions of its workspace and reducing the robot velocity where fine control is required. Two participants controlled a JACO assistive robot to grasp and lift a bottle with and without the velocity adjustment method. The task was performed 9.1% faster with velocity adjustment. Furthermore, analyzing the robot trajectory showed that the method recognized highly restrictive regions and reduced the robot end-effector velocity accordingly.Clinical relevance- The autonomous velocity adjustment method can ease the control of ARSs and improve their usability, leading to a higher quality of life for individuals with severe disabilities who can benefit from ARSs.

Comparing the Usability of Alternative EEG Devices to Traditional Electrode Caps for SSVEP-BCI Controlled Assistive Robots.

Despite having the potential to improve the lives of severely paralyzed users, non-invasive Brain Computer Interfaces (BCI) have yet to be integrated into their daily lives. The widespread adoption of BCI-driven assistive technology is hindered by its lacking usability, as both end-users and researchers alike find fault with traditional EEG caps. In this paper, we compare the usability of four EEG recording devices for Steady-State Visually Evoked Potentials (SSVEP)-BCI applications: an EEG cap (active gel electrodes), two headbands (passive gel or active dry electrodes), and two adhesive electrodes placed on each mastoid. Ten able-bodied participants tested each device by completing an 8-target SSVEP paradigm. Setup times were recorded, and participants rated their satisfaction with each device. The EEG cap obtained the best classification accuracies (Median = 98.96%), followed by the gel electrode headband (Median = 93.75%), and the dry electrode headband (Median = 91.14%). The mastoid electrodes obtained classification accuracies close to chance level (Med = 29.69%). Unknowing of the classification accuracy, participants found the mastoid electrodes to be the most comfortable and discrete. The dry electrode headband obtained the lowest user satisfaction score and was criticized for being too uncomfortable. Participants also noted that the EEG cap was too conspicuous. The gel-based headband provided a good trade-off between BCI performance and user satisfaction.