Mechanical performance analysis and experimental study of soft-bodied bird-billed pneumatic gripper

Purpose Flexible mechanical gripper has better safety and adaptability than a rigid mechanical hand. At present, there are few soft grippers for small objects on a millimeter scale. Therefore, the purpose of this paper is to design a soft pneumatic gripper for grasping millimeter-scale small and fragile objects such as jewelry and electronic components. Design/methodology/approach By simulating the clamping action of the bird’s mouth and combining the high flexibility of the soft material, the bird’s beak soft pneumatic gripper is designed. First, the internal cavity of the gripping end of the gripper is determined by bending deformation calculation, and the brief manufacturing process of the gripper is outlined. Then, the single finger of the soft gripper is modeled mechanically, and the relationship between air pressure and bending deformation of the single finger is obtained. Finally, the experimental platform of the soft mechanical gripper is built, and the gripping performance of silicone rubber material is tested by comparison test, bending deformation test, stability test, adaptability test and gripping accuracy test. Findings The designed gripper has the advantages of simple structure, convenient operation, easy grasping of different small objects of millimeter-scale and good adaptability. It can grasp the precise dispensing needle with a minimum diameter of 0.19 mm, and its accuracy meets daily use. Originality/value A new type of soft pneumatic, the mechanical gripper is proposed and manufactured. According to the shape of the bird’s beak and the calculation of bending performance, a hollow finger gripper with better bending performance is designed. Various test results show that the gripper has a significant clamping effect on millimeter small objects, which supplements the research field of millimeter small object gripper.

Effects of Daily Hand Activities on Age-Related Declines of Dynamic Motor Function in Individual Fingers

The present study examined the effects of daily activities of the hands on finger motor function in older adults. Maximum tapping frequency with each finger during single-finger tapping and alternate movements of index–middle, middle–ring, and ring–little finger pairs during double-finger tapping were compared between older adults who used their hands actively in their daily lives and those who did not. The active participants had significantly faster tapping rates for the ring finger in the single-finger tapping and the middle–ring finger pair in the double-finger tapping than did the inactive participants. Thus, daily activity of the hands in older adults could be effective at preventing the loss of dynamic motor function in individual fingers, especially with greater difficulty in movement, resulting from the degeneration with age.

Finger representation in the cortex of the congenitally blind

Hand representation in the primary somatosensory cortex (S1) is thought to be shaped by experience. Individuals with congenital blindness rely on their sense of touch for completing daily tasks that in sighted people would be informed by vision, and possess superior tactile acuity. It has therefore been proposed that their S1 hand representation should differ from that of sighted individuals. Alternatively, it has been proposed that the improved tactile acuity in blind individuals is due to cross-modal plasticity, when regions in the occipital and temporal cortex are typically used for processing vision become activated by touch. We probed finger representation using psychophysics and 7T fMRI (1 mm3 resolution) in three individuals with bilateral anophthalmia, a rare condition in which both eyes fail to develop, as well as sighted controls. Despite anophthalmic individuals’ increased reliance on touch and superior tactile acuity, we found no evidence that they had more pronounced hand representation in S1. This is in line with recent research highlighting the stability of early sensory cortex, despite altered sensorimotor experience in adulthood. Unlike sighted controls, anophthalmic individuals activated the left human middle temporal complex (hMT+) during finger movement. This area did not express any hallmark of typical sensorimotor organisation, suggesting this and previously reported activity does not indicate low-level sensorimotor hand representation. However, left hMT+ contained some single finger information, beyond that found in sighted controls. This latter finding suggests that when the developmentally flexible area hMT+ is unaffected by retinal input, it can acquire novel cross-modal processes, which are potentially unrelated to the area’s function in sighted people. As such, our findings highlight the opportunity for other organising principles, beyond domain specific plasticity, in shaping cross-modal reorganisation.

Force Evaluation of Tendon Routing for Underactuated Grasping

While the modelling analysis of the kinetostatic behaviour of underactuated tendon-driven robotic fingers has been largely addressed in the literature, tendon routing is often not considered by these theoretical models. Tendon routing path plays a fundamental role in defining joint torques, and subsequently the force vectors produced by the phalanxes. However, dynamic tendon behaviour is difficult to predict and is influenced by many external factors including tendon friction, the shape of the grasped object, the initial pose of the fingers, and finger contact points. In this paper, we present an experimental comparison of the force performance of nine fingers, with different tendon routing configurations. We use the concept of force-isotropy, in which forces are equal and distributed on each phalanx as the optimum condition for an adaptive grasp. Our results show only some of the finger designs surveyed exhibited a partial adaptive behaviour, showing distributed force for the proximal and distal phalanxes throughout grasping cycles, while other routings resulted in only a single finger remaining in contact with the object.

Mental individuation of imagined finger movements can be achieved using TMS-based neurofeedback

ABSTRACTNeurofeedback (NF) in combination with motor imagery (MI) can be used for training individuals to volitionally modulate sensorimotor activity without producing overt movements. However, until now, NF methods were of limited utility for mentally training specific hand and finger actions. Here we employed a novel transcranial magnetic stimulation (TMS) based protocol to probe and detect MI-induced motor activity patterns in the primary motor cortex (M1) with the aim to reinforce selective facilitation of single finger representations. We showed that TMS-NF training but not MI training with uninformative feedback enabled participants to selectively upregulate corticomotor excitability of one finger, while simultaneously downregulating excitability of other finger representations within the same hand. Successful finger individuation during MI was accompanied by strong desynchronisation of sensorimotor brain rhythms, particularly in the beta band, as measured by electroencephalography. Additionally, informative TMS-NF promoted more dissociable EEG activation patterns underlying single finger MI, when compared to MI of the control group where no such feedback was provided. Our findings suggest that selective TMS-NF is a new approach for acquiring the ability of finger individuation even if no overt movements are performed. This might offer new treatment modality for rehabilitation after stroke or spinal cord injury.

Concurrent Prediction of Finger Forces Based on Source Separation and Classification of Neuron Discharge Information

A reliable neural-machine interface is essential for humans to intuitively interact with advanced robotic hands in an unconstrained environment. Existing neural decoding approaches utilize either discrete hand gesture-based pattern recognition or continuous force decoding with one finger at a time. We developed a neural decoding technique that allowed continuous and concurrent prediction of forces of different fingers based on spinal motoneuron firing information. High-density skin-surface electromyogram (HD-EMG) signals of finger extensor muscle were recorded, while human participants produced isometric flexion forces in a dexterous manner (i.e. produced varying forces using either a single finger or multiple fingers concurrently). Motoneuron firing information was extracted from the EMG signals using a blind source separation technique, and each identified neuron was further classified to be associated with a given finger. The forces of individual fingers were then predicted concurrently by utilizing the corresponding motoneuron pool firing frequency of individual fingers. Compared with conventional approaches, our technique led to better prediction performances, i.e. a higher correlation ([Formula: see text] versus [Formula: see text]), a lower prediction error ([Formula: see text]% MVC versus [Formula: see text]% MVC), and a higher accuracy in finger state (rest/active) prediction ([Formula: see text]% versus [Formula: see text]%). Our decoding method demonstrated the possibility of classifying motoneurons for different fingers, which significantly alleviated the cross-talk issue of EMG recordings from neighboring hand muscles, and allowed the decoding of finger forces individually and concurrently. The outcomes offered a robust neural-machine interface that could allow users to intuitively control robotic hands in a dexterous manner.

3D Hand Pose Estimation Based on Five-Layer Ensemble CNN

Estimating accurate 3D hand pose from a single RGB image is a highly challenging problem in pose estimation due to self-geometric ambiguities, self-occlusions, and the absence of depth information. To this end, a novel Five-Layer Ensemble CNN (5LENet) is proposed based on hierarchical thinking, which is designed to decompose the hand pose estimation task into five single-finger pose estimation sub-tasks. Then, the sub-task estimation results are fused to estimate full 3D hand pose. The hierarchical method is of great benefit to extract deeper and better finger feature information, which can effectively improve the estimation accuracy of 3D hand pose. In addition, we also build a hand model with the center of the palm (represented as Palm) connected to the middle finger according to the topological structure of hand, which can further boost the performance of 3D hand pose estimation. Additionally, extensive quantitative and qualitative results on two public datasets demonstrate the effectiveness of 5LENet, yielding new state-of-the-art 3D estimation accuracy, which is superior to most advanced estimation methods.