Assessing Mode-Switching Strategies for Assistive Robotic Manipulators Using a Preliminary Version of the Novel Non-invasive Tongue-Computer Interface.

The inductive tongue-computer interface allows individuals with tetraplegia to control assistive devices. However, controlling assistive robotic arms often requires more than 14 different commands, which cannot always fit into a single control layout. Previous studies have separated the commands into modes, but few have investigated strategies to switch between them. In this feasibility study, we compare the efficiency of switching modes using buttons, swipe gestures and double taps using a preliminary version of a new non-invasive mouthpiece unit (nMPU), which includes an integrated activation unit and a single sensor board. Three participants controlled a JACO assistive robot to pick up a bottle using different mode-switching strategies. Compared with switching modes with buttons, switching modes with swipes and double taps increased the task completion time by 21% and 58% respectively. Therefore, we recommend that configurations with multiple modes for the non-invasive tongue-computer interface include buttons for mode-switching.Clinical relevance- Cumbersome mode-switching strategies can lower a control interface's responsiveness and contribute to end-user abandonment of assistive technologies. This study showed that using buttons to switch modes is more reliable. Moreover, this study will inform the development of future control layouts with improved usability.

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.

Metabolic alterations in dairy cattle with lameness revealed by untargeted metabolomics of dried milk spots using direct infusion-tandem mass spectrometry and the triangulation of multiple machine learning models.

Lameness is a major challenge in the dairy cattle industry in terms of animal welfare and economic implications. Better understanding of metabolic alteration associated with lameness could lead to early diagnosis and effective treatment, there-fore reducing its prevalence. To determine whether metabolic signatures associated with lameness could be discovered with untargeted metabolomics, we developed a novel workflow using direct infusion-tandem mass spectrometry to rapidly analyse (2 min per sample) dried milk spots (DMS) that were stored on commercially available Whatman® FTA® DMPK cards for a prolonged period (8 and 16 days). An orthogonal partial least squares-discriminant analysis (OPLS-DA) method validated by triangulation of multiple machine learning (ML) models and stability selection was employed to reliably identify important discriminative metabolites. With this approach, we were able to differentiate between lame and healthy cows based on a set of lipid molecules and several small metabolites. Among the discriminative molecules, we identified phosphatidylglycerol (PG 35:4) as the strongest and most sensitive lameness indicator based on stability selection. Overall, this untargeted metabolomics workflow is found to be a fast, robust, and discriminating method for determining lameness in DMS samples. The DMS cards can be potentially used as a convenient and cost-effective sample matrix for larger scale research and future routine screening for lameness.

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.

Biocompatible and bioactive gum Arabic coated iron oxide magnetic nanoparticles.

The surface modification of iron oxide magnetic nanoparticles (MNPs) with gum Arabic (GA) via adsorption and covalent coupling was studied. The adsorption of GA was assessed during MNP chemical synthesis by the co-precipitation method (MNP_GA), and after MNP synthesis on both bare magnetite and MNP_GA. The covalent immobilization of GA at the surface of aldehyde-activated (MNP_GA(APTES)) or aminated MNPs (MNP_GA(EDC)) was achieved through free terminal amino and carboxylate groups from GA. The presence of GA at the surface of the MNPs was confirmed by FTIR and by the quantification of GA by the bicinchoninic acid test. Results indicated that the maximum of GA coating was obtained for the covalent coupling of GA through its free carboxylate groups (MNP_GA(EDC)), yielding a maximum of 1.8g of GA bound/g of dried particles. The hydrodynamic diameter of MNPs modified with GA after synthesis resulted in the lowest values, in opposition to the MNPs co-precipitated with GA which presented the tendency to form larger aggregates of up to 1mum. The zeta potentials indicate the existence of negatively charged surfaces before and after GA coating. The potential of the GA coated MNPs for further biomolecule attachment was assessed through anchorage of a model antibody to aldehyde-functionalized MNP_GA and its subsequent detection with an FITC labeled anti-antibody.