An optoelectronic muscle contraction sensor for prosthetic hand application

Validations of various in-hand object manipulation strategies employing a novel tactile sensor developed for an under-actuated robot hand

Prisma Hand II is an under-actuated prosthetic hand developed at the University of Naples, Federico II to study in-hand manipulations during grasping activities. 3 motors equipped on the robotic hand drive 19 joints using elastic tendons. The operations of the hand are achieved by combining tactile hand sensing with under-actuation capabilities. The hand has the potential to be employed in both industrial and prosthetic applications due to its dexterous motion capabilities. However, currently there are no commercially available tactile sensors with compatible dimensions suitable for the prosthetic hand. Hence, in this work, we develop a novel tactile sensor designed based on an opto-electronic technology for the Prisma Hand II. The optimised dimensions of the proposed sensor made it possible to be integrated with the fingertips of the prosthetic hand. The output voltage obtained from the novel tactile sensor is used to determine optimum grasping forces and torques during in-hand manipulation tasks employing Neural Networks (NNs). The grasping force values obtained using a Convolutional Neural Network (CNN) and an Artificial Neural Network (ANN) are compared based on Mean Square Error (MSE) values to find out a better training network for the tasks. The tactile sensing capabilities of the proposed novel sensing method are presented and compared in simulation studies and experimental validations using various hand manipulation tasks. The developed tactile sensor is found to be showcasing a better performance compared to previous version of the sensor used in the hand.

Flexible and Stretchable Light-Emitting Diodes and Photodetectors for Human-Centric Optoelectronics

Optoelectronic devices with unconventional form factors, such as flexible and stretchable light-emitting or photoresponsive devices, are core elements for the next-generation human-centric optoelectronics. For instance, these deformable devices can be utilized as closely fitted wearable sensors to acquire precise biosignals that are subsequently uploaded to the cloud for immediate examination and diagnosis, and also can be used for vision systems for human-interactive robotics. Their inception was propelled by breakthroughs in novel optoelectronic material technologies and device blueprinting methodologies, endowing flexibility and mechanical resilience to conventional rigid optoelectronic devices. This paper reviews the advancements in such soft optoelectronic device technologies, honing in on various materials, manufacturing techniques, and device design strategies. We will first highlight the general approaches for flexible and stretchable device fabrication, including the appropriate material selection for the substrate, electrodes, and insulation layers. We will then focus on the materials for flexible and stretchable light-emitting diodes, their device integration strategies, and representative application examples. Next, we will move on to the materials for flexible and stretchable photodetectors, highlighting the state-of-the-art materials and device fabrication methods, followed by their representative application examples. At the end, a brief summary will be given, and the potential challenges for further development of functional devices will be discussed as a conclusion.

Optical Myography-Based Sensing Methodology of Application of Random Loads to Muscles during Hand-Gripping Training

Hand-gripping training is important for improving the fundamental functions of human physical activity. Bernstein's idea of "repetition without repetition" suggests that motor control function should be trained under changing states. The randomness level of load should be visualized for self-administered screening when repeating various training tasks under changing states. This study aims to develop a sensing methodology of random loads applied to both the agonist and antagonist skeletal muscles when performing physical tasks. We assumed that the time-variability and periodicity of the applied load appear in the time-series feature of muscle deformation data. In the experiment, 14 participants conducted the gripping tasks with a gripper, ball, balloon, Palm clenching, and paper. Crumpling pieces of paper (paper exercise) involves randomness because the resistance force of the paper changes depending on the shape and layers of the paper. Optical myography during gripping tasks was measured, and time-series features were analyzed. As a result, our system could detect the random movement of muscles during training.