A Versatile 3D-Printable Soft Pneumatic Actuator Design for Multi-Functional Applications in Soft Robotics.

Soft pneumatic actuators (SPAs) play a crucial role in generating movements and forces in soft robotic systems. However, existing SPA designs require significant structural modifications to be used in applications other than their original design. The present article proposes an omni-purpose fully 3D-printable SPA design inspired by membrane type mold and cast SPAs. The design features a spring-like zig-zag structure 3D-printed using an affordable 3D printer with thermoplastic polyurethane and a minimum wall thickness between 0.4 and 0.6 mm. The new SPA can perform unidirectional extension (30% extension) and bidirectional (rotation around same axis) bending (100°), with the ability to exert 10 N blocking force for 350 kPa pressure input. In addition, the design exhibits the capability to be scaled down for the purpose of accommodating limited spaces, while simultaneously enabling the reconfigurable interconnection of multiple SPAs to adapt to larger areas and navigate intricate trajectories that were not originally intended. The SPA's ability to be used in multiple applications without structural modification was validated through testing as a robot end-effector (gripper), artificial muscles in a soft tendon-driven prosthetic hand, a tube/tunnel navigator, and a robot crawler.

A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography.

Electrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human-computer interaction. Nonetheless, EOG signals are often met with skepticism due to the presence of multiple sources of noise interference. These sources include electroencephalography, electromyography linked to facial and extraocular muscle activity, electrical noise, signal artifacts, skin-electrode drifts, impedance fluctuations over time, and a host of associated challenges. Traditional methods of addressing these issues, such as bandpass filtering, have been frequently utilized to overcome these challenges but have the associated drawback of altering the inherent characteristics of EOG signals, encompassing their shape, magnitude, peak velocity, and duration, all of which are pivotal parameters in research studies. In prior work, several model-based adaptive denoising strategies have been introduced, incorporating mechanical and electrical model-based state estimators. However, these approaches are really complex and rely on brain and neural control models that have difficulty processing EOG signals in real time. In this present investigation, we introduce a real-time denoising method grounded in a constant velocity model, adopting a physics-based model-oriented approach. This approach is underpinned by the assumption that there exists a consistent rate of change in the cornea-retinal potential during saccadic movements. Empirical findings reveal that this approach remarkably preserves EOG saccade signals, resulting in a substantial enhancement of up to 29% in signal preservation during the denoising process when compared to alternative techniques, such as bandpass filters, constant acceleration models, and model-based fusion methods.