Using postural synergies to animate a low-dimensional hand avatar in haptic simulation

Configuration-based Optimization for Virtual Hand Haptic Simulation

Interacting with virtual objects via haptic feedback using the user's hand directly (virtual hand haptic interaction) provides a natural and immersive way to explore the virtual world. It remains a challenging topic to achieve 1 kHz stable virtual hand haptic simulation with no penetration amid hundreds of hand-object contacts. In this paper, we advocate decoupling the high-dimensional optimization problem of computing the graphic-hand configuration, and progressively optimizing the configuration of the graphic palm and fingers, yielding a decoupled-and-progressive optimization framework. We also introduce a method for accurate and efficient hand-object contact simulation, which constructs a virtual hand consisting of a sphere-tree model and five articulated cone frustums, and adopts a configuration-based optimization algorithm to compute the graphic-hand configuration under non-penetration contact constraints. Experimental results show both high update rate and stability for a variety of manipulation behaviors. Non-penetration between the graphic hand and complex-shaped objects can be maintained under diverse contact distributions, and even for frequent contact switches. The update rate of the haptic simulation loop exceeds 1 kHz for the whole-hand interaction with about 250 contacts.

Design and Prototyping of an Underactuated Hand Exoskeleton With Fingers Coupled by a Gear-Based Differential

Exoskeletons and more in general wearable mechatronic devices represent a promising opportunity for rehabilitation and assistance to people presenting with temporary and/or permanent diseases. However, there are still some limits in the diffusion of robotic technologies for neuro-rehabilitation, notwithstanding their technological developments and evidence of clinical effectiveness. One of the main bottlenecks that constrain the complexity, weight, and costs of exoskeletons is represented by the actuators. This problem is particularly evident in devices designed for the upper limb, and in particular for the hand, in which dimension limits and kinematics complexity are particularly challenging. This study presents the design and prototyping of a hand finger exoskeleton. In particular, we focus on the design of a gear-based differential mechanism aimed at coupling the motion of two adjacent fingers and limiting the complexity and costs of the system. The exoskeleton is able to actuate the flexion/extension motion of the fingers and apply bidirectional forces, that is, it is able to both open and close the fingers. The kinematic structure of the finger actuation system has the peculiarity to present three DoFs when the exoskeleton is not worn and one DoF when it is worn, allowing better adaptability and higher wearability. The design of the gear-based differential is inspired by the mechanism widely used in the automotive field; it allows actuating two fingers with one actuator only, keeping their movements independent.

A Synergy-Based Optimally Designed Sensing Glove for Functional Grasp Recognition

Achieving accurate and reliable kinematic hand pose reconstructions represents a challenging task. The main reason for this is the complexity of hand biomechanics, where several degrees of freedom are distributed along a continuous deformable structure. Wearable sensing can represent a viable solution to tackle this issue, since it enables a more natural kinematic monitoring. However, the intrinsic accuracy (as well as the number of sensing elements) of wearable hand pose reconstruction (HPR) systems can be severely limited by ergonomics and cost considerations. In this paper, we combined the theoretical foundations of the optimal design of HPR devices based on hand synergy information, i.e., the inter-joint covariation patterns, with textile goniometers based on knitted piezoresistive fabrics (KPF) technology, to develop, for the first time, an optimally-designed under-sensed glove for measuring hand kinematics. We used only five sensors optimally placed on the hand and completed hand pose reconstruction (described according to a kinematic model with 19 degrees of freedom) leveraging upon synergistic information. The reconstructions we obtained from five different subjects were used to implement an unsupervised method for the recognition of eight functional grasps, showing a high degree of accuracy and robustness.

Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands

The term 'synergy' - from the Greek synergia - means 'working together'. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project "The Hand Embodied" (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies.

Digital Handwriting with a Finger or a Stylus: A Biomechanical Comparison

In this paper, we present a study concerning the human hand during digital handwriting on a tablet. Two different cases are considered: writing with the finger, and writing with the stylus. We chose an approach based on the biomechanics of the human hand to compare the two different input methods. Performance is evaluated using metrics originally introduced and developed in robotics, such as the manipulability indexes. Analytical results assess that writing with the finger is more suitable for performing large, but not very accurate motions, while writing with the stylus leads to a higher precision and more isotropic motion performance. We then carried out two experiments of digital handwriting to support the approach and contextualize the results.