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1. Numerical Finger Kinematic Models Derived From Virtual Grasping of Various Cylindrical Objects With the Family of Conic Sections

   https://doi.org/10.1115/1.4048257  

   Won, Jong-Seob; Langari, Reza; Robson, Nina (February 2021, Journal of Mechanisms and Robotics)

   null (Ed.)

   Abstract In this study, a numerical framework for joint rotation configuration models of a finger is proposed. The basic idea is to replicate the finger’s geometric posture observed when the human hand grasps a cylindrical object with various cross sections. In the model development, objects with the cross section adopted from the curves of order two (the family of conic sections) are taken into consideration to realize various finger postures. In addition, four different grasp styles, which simulate the individual-specific contact pattern between the surfaces of object and finger, are modeled and applied for the formulation of numerical models. An idea on how to change flexion/extension patterns in the middle of excursion of movement is proposed and discussed. Series of numerical studies have been conducted and analyzed to evaluate the proposed models. From the results, one can see the models’ feasibility and viability as a solution to describing finger’s flexion/extension movements (FEMs) for grasping patterns. 

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2. Flexion and Extension Capable Motor Tendon Actuated Exosuit Glove with Open Palm

   Patterson, G.; Zell, T.; Parker, D.; Grieve, J.; Watson, S.; Vikas, V. (August 2019, Proceedings of the ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Patients suffering from medical conditions resulting in hand impairment experience difficulty in performing simple daily tasks, like getting dressed or using a pencil, resulting in a poorer quality of life. Rehabilitation attempts to help such individuals regain a sense of control and normalcy. In this context, recent advances in robotics have manifested in multiple designs of hand exoskeletons and exosuit gloves for assistance and rehabilitation. These designs are typically actuated using pneumatic, shape memory alloys and motor-tendon actuators. The proposed Motor Tendon Actuated Exosuit Glove (MTAEG) with an open palm is a soft material glove capable of both flexion and extension of all four fingers of the human hand. Its minimally invasive design maintains an open palm to facilitate haptic and tactile interaction with the environment. The MTAEG achieves flexion-extension motion with joint angles of 45° at the metacarpal joint which is 57% of the desired motion; 90° at the proximal interphalangeal joint which is 100% of the desired motion; and 50° at the distal interphalangeal joint which is 96% of the desired motion. The paper discusses the challenges in achieving the desired motion without the ability to directly model human tendons, and the inability to actuate joints individually. 

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3. STAR–2: A Soft Twisted-string-actuated Anthropomorphic Robotic Gripper: Design, Fabrication, and Preliminary Testing

   https://doi.org/10.1109/AIM46323.2023.10196149  

   Baker, Aaron; Foy, Claire; Swanbeck, Steven; Konda, Revanth; Zhang, Jun (June 2023, IEEE)

   While numerous studies have been conducted, developing a compliant robotic gripper capable of replicating human hand grasping and manipulation capabilities is still challenging. This paper presents the design, fabrication, and preliminary testing of an anthropomorphic soft robotic gripper driven by twisted string actuators (TSAs). Termed as STAR–2, it is a second generation TSA-driven soft gripper from the Smart Robotics Laboratory at the University of Nevada, Reno. The novel design facilitated a monolithic structure comprising of a 3-degrees-of-freedom (DOF) thumb and four fingers each with 2-DOFs. On account of using tendon-based actuation and the large footprint required for the thumb, the design employed meticulously planned tendon routing within the monolithic structure. Preliminary results showed STAR–2’s enhanced ability to demonstrate grasp taxonomies and dexterity over STAR–1. 

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4. Design and Control of Foam Hands for Dexterous Manipulation

   https://doi.org/10.1142/S0219843619500336  

   Bauer, Dominik; Bauer, Cornelia; King, Jonathan P.; Moro, Daniele; Chang, Kai-Hung; Coros, Stelian; Pollard, Nancy (February 2020, International Journal of Humanoid Robotics)

   null (Ed.)

   There has been great progress in soft robot design, manufacture, and control in recent years, and soft robots are a tool of choice for safe and robust handling of objects in conditions of uncertainty. Still, dexterous in-hand manipulation using soft robots remains a challenge. This paper introduces foam robot hands actuated by tendons sewn through a fabric glove. The flexibility of tendon actuation allows for high competence in utilizing deformation for robust in-hand manipulation. We discuss manufacturing, control, and design optimization for foam robots and demonstrate robust grasping and in-hand manipulation on a variety of different physical hand prototypes. 

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5. Concurrent Prediction of Dexterous Finger Flexion and Extension Force via Deep Forest

   Fan, J. Hu (August 2023, 2023 Proceedings of IEEE Engineering in Medicine and Biology Society EMBC Conference)

   Neuromuscular injuries can impair hand function and profoundly impacting the quality of life. This has motivated the development of advanced assistive robotic hands. However, the current neural decoder systems are limited in their ability to provide dexterous control of these robotic hands. In this study, we propose a novel method for predicting the extension and flexion force of three individual fingers concurrently using high-density electromyogram (HD-EMG) signals. Our method employs two deep forest models, the flexor decoder and the extensor decoder, to extract relevant representations from the EMG amplitude features. The outputs of the two decoders are integrated through linear regression to predict the forces of the three fingers. The proposed method was evaluated on data from three subjects and the results showed that it consistently outperforms the conventional EMG amplitude-based approach in terms of prediction error and robustness across both target and non-target fingers. This work presents a promising neural decoding approach for intuitive and dexterous control of the fingertip forces of assistive robotic hands. 

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