This paper details the design of a poly(dimethylsiloxane) (PDMS)-shielded waterproof crack-based stretchable strain sensor, in which the electrical characteristics and sensing performance are not influenced by changes in humidity. This results in a higher number of potential applications for the sensor. A previously developed omni-purpose stretchable strain (OPSS) sensor was used as the basis for this work, which utilizes a metal cracking structure and provides a wide sensing range and high sensitivity. Changes in the conductivity of the OPSS sensor, based on humidity conditions, were investigated along with the potential possibility of using the design as a humidity sensor. However, to prevent conductivity variation, which can decrease the reliability and sensing ability of the OPSS sensor, PDMS was utilized as a shielding layer over the OPSS sensor. The PDMS-shielded OPSS sensor showed approximately the same electrical characteristics as previous designs, including in a high humidity environment, while maintaining its strain sensing capabilities. The developed sensor shows promise for use under high humidity conditions and in underwater applications. Therefore, considering its unique features and reliable sensing performance, the developed PDMS-shielded waterproof OPSS sensor has potential utility in a wide range of applications, such as motion monitoring, medical robotics and wearable healthcare devices.

Precise motion control is important in robotic surgery, especially corneal surgery. This paper develops a new tension-measurement system for forceps used in corneal surgery, wherein contact force is applied only to a specific location for precise control, with precise movements detected by attaching a nano-crack sensor to the corresponding part. The nano-crack sensor used here customizes the working range and sensor sensitivity to match the strain rate of the tip of the forceps. Therefore, the tension in the suture can be sufficiently measured even at suture failure. The printed circuit board attached to the bottom of the system is designed to simultaneously collect data from several sensors, visualizing the direction and magnitude of the tension in order to inform the surgeon of how much tension is being applied. This system was verified by performing pig-corneal suturing.

Saddle-shaped superconducting coils are efficient in terms of generating an ideal multipole field inside a cylindrical bobbin which is the typical shape used in particle accelerators, electric motors, nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), and other application. Compared to low-temperature superconductors, rare earth barium copper oxide (ReBCO) high-temperature superconductor (HTS) wires exhibit excellent electromechanical behavior, high current carrying capacity to generate high magnetic fields and high thermal stability to reduce cooling costs. Despite such advantages, attempts to wind a saddle-shaped coil by using the coated HTS conductor have been rarely reported due to a hard-way bending characteristics of the thin tape-shaped HTS conductor. Here, we introduce the concept of a dedicated winding machine capable of winding saddle-shaped coils including the numerical modelling and visualization. To simplify the implementation of the winding machine, we utilized one universal joint and two 2D linear stages. We also derived control variables that can be directly applied in practical operations.

In this study, we introduce a novel design for a three-dimensional (3D) controller, which incorporates the omni-purpose stretchable strain sensor (OPSS sensor). This sensor exhibits both remarkable sensitivity, with a gauge factor of approximately 30, and an extensive working range, accommodating strain up to 150%, thereby enabling accurate 3D motion sensing. The 3D controller is structured such that its triaxial motion can be discerned independently along the X, Y, and Z axes by quantifying the deformation of the controller through multiple OPSS sensors affixed to its surface. To ensure precise and real-time 3D motion sensing, a machine learning-based data analysis technique was implemented for the effective interpretation of the multiple sensor signals. The outcomes reveal that the resistance-based sensors successfully and accurately track the 3D controller’s motion. We believe that this innovative design holds the potential to augment the performance of 3D motion sensing devices across a diverse range of applications, encompassing gaming, virtual reality, and robotics.