DistaNet: grasp-specific distance biofeedback promotes the retention of myoelectric skills

Objective.An active myoelectric interface responds to the user's muscle signals to enable movements. Machine learning can decode user intentions from myoelectric signals. However, machine learning-based interface control lacks continuous, intuitive feedback about task performance, needed to facilitate the acquisition and retention of myoelectric control skills.Approach.We propose DistaNet as a neural network-based framework that extracts smooth, continuous, and low-dimensional signatures of the hand grasps from multi-channel myoelectric signals and provides grasp-specific biofeedback to the users.Main results.Experimental results show its effectiveness in decoding user gestures and providing biofeedback, helping users retain the acquired motor skills.Significance.We demonstrates myoelectric skill retention in a pattern recognition setting for the first time.

Retrieving a file binder from a bookshelf using pseudo-curved trajectory generation for a foldable robotic hand

Human-assistive robots need to perform trajectory making for and control of a robotic hand along the many rotating mechanisms in our living spaces. If such trajectory control can be performed without high-cost sensors, certainly a significant cost reduction in building the robot will be achieved. This paper describes a method of retrieving a file binder by generating a pseudo-curved trajectory for tilting it using a simple system. A simple claw mechanism with a switch sensor to grasp an object was designed and 3D-printed, and it was attached to a 6-DOF foldable robotic hand developed by the authors. A method for generating a pseudo-curved trajectory using the switch sensor was developed, and the robotic hand was successfully moved along this trajectory to tilt and grasp a file binder to retrieve it from a bookshelf. Experiments to clarify the success rate were also conducted, and it was found that the results depend on the rotational speed of manipulator links and the vibration of the claw mechanism link. A rubber sponge was added to give flexibility to the claw mechanism, which significantly improved the success rate. Furthermore, a control system to recover from tilting failure was constructed, and its effectiveness was validated by experiments.

Recent Advances in Biomimetics for the Development of Bio-Inspired Prosthetic Limbs

Recent advancements in biomimetics have spurred significant innovations in prosthetic limb development by leveraging the intricate designs and mechanisms found in nature. Biomimetics, also known as "nature-inspired engineering", involves studying and emulating biological systems to address complex human challenges. This comprehensive review provides insights into the latest trends in biomimetic prosthetics, focusing on leveraging knowledge from natural biomechanics, sensory feedback mechanisms, and control systems to closely mimic biological appendages. Highlighted breakthroughs include the integration of cutting-edge materials and manufacturing techniques such as 3D printing, facilitating seamless anatomical integration of prosthetic limbs. Additionally, the incorporation of neural interfaces and sensory feedback systems enhances control and movement, while technologies like 3D scanning enable personalized customization, optimizing comfort and functionality for individual users. Ongoing research efforts in biomimetics hold promise for further advancements, offering enhanced mobility and integration for individuals with limb loss or impairment. This review illuminates the dynamic landscape of biomimetic prosthetic technology, emphasizing its transformative potential in rehabilitation and assistive technologies. It envisions a future where prosthetic solutions seamlessly integrate with the human body, augmenting both mobility and quality of life.

Arm muscle synergies enhance hand posture prediction in combination with forearm muscle synergies

Objective.We analyze and interpret arm and forearm muscle activity in relation with the kinematics of hand pre-shaping during reaching and grasping from the perspective of human synergistic motor control.Approach.Ten subjects performed six tasks involving reaching, grasping and object manipulation. We recorded electromyographic (EMG) signals from arm and forearm muscles with a mix of bipolar electrodes and high-density grids of electrodes. Motion capture was concurrently recorded to estimate hand kinematics. Muscle synergies were extracted separately for arm and forearm muscles, and postural synergies were extracted from hand joint angles. We assessed whether activation coefficients of postural synergies positively correlate with and can be regressed from activation coefficients of muscle synergies. Each type of synergies was clustered across subjects.Main results.We found consistency of the identified synergies across subjects, and we functionally evaluated synergy clusters computed across subjects to identify synergies representative of all subjects. We found a positive correlation between pairs of activation coefficients of muscle and postural synergies with important functional implications. We demonstrated a significant positive contribution in the combination between arm and forearm muscle synergies in estimating hand postural synergies with respect to estimation based on muscle synergies of only one body segment, either arm or forearm (p< 0.01). We found that dimensionality reduction of multi-muscle EMG root mean square (RMS) signals did not significantly affect hand posture estimation, as demonstrated by comparable results with regression of hand angles from EMG RMS signals.Significance.We demonstrated that hand posture prediction improves by combining activity of arm and forearm muscles and we evaluate, for the first time, correlation and regression between activation coefficients of arm muscle and hand postural synergies. Our findings can be beneficial for myoelectric control of hand prosthesis and upper-limb exoskeletons, and for biomarker evaluation during neurorehabilitation.

Brownian processes in human motor control support descending neural velocity commands

The motor neuroscience literature suggests that the central nervous system may encode some motor commands in terms of velocity. In this work, we tackle the question: what consequences would velocity commands produce at the behavioral level? Considering the ubiquitous presence of noise in the neuromusculoskeletal system, we predict that velocity commands affected by stationary noise would produce "random walks", also known as Brownian processes, in position. Brownian motions are distinctively characterized by a linearly growing variance and a power spectral density that declines in inverse proportion to frequency. This work first shows that these Brownian processes are indeed observed in unbounded motion tasks e.g., rotating a crank. We further predict that such growing variance would still be present, but bounded, in tasks requiring a constant posture e.g., maintaining a static hand position or quietly standing. This hypothesis was also confirmed by experimental observations. A series of descriptive models are investigated to justify the observed behavior. Interestingly, one of the models capable of accounting for all the experimental results must feature forward-path velocity commands corrupted by stationary noise. The results of this work provide behavioral support for the hypothesis that humans plan the motion components of their actions in terms of velocity.

Journey from human hands to robot hands: biological inspiration of anthropomorphic robotic manipulators

The development of robotic hands that can replicate the complex movements and dexterity of the human hand has been a longstanding challenge for scientists and engineers. A human hand is capable of not only delicate operation but also crushing with power. For performing tasks alongside and in place of humans, an anthropomorphic manipulator design is considered the most advanced implementation, because it is able to follow humans' examples and use tools designed for people. In this article, we explore the journey from human hands to robot hands, tracing the historical advancements and current state-of-the-art in hand manipulator development. We begin by investigating the anatomy and function of the human hand, highlighting the bone-tendon-muscle structure, skin properties, and motion mechanisms. We then delve into the field of robotic hand development, focusing on highly anthropomorphic designs. Finally, we identify the requirements and directions for achieving the next level of robotic hand technology.

Soft Robotic Finger with Energy-Coupled Quadrastability

The performance of the human finger is a significant inspiration for designing soft robotic fingers that can achieve high speed and high force or perform delicate and complex tasks. Existing soft grippers and actuators can be excellent in specific capabilities. However, it is still challenging for them to meet an all-around performance as the human finger, characterized by high actuation speed, wide grasping range, sensing ability, and gentle and high-load grasping capability. The proposed tendon pulley quadrastable (TPQ) finger has combined these qualities in the conducted gripping tasks. A pair of elastic tendons is utilized as the sole energy reservoir to create a novel energy distribution pattern: energy-coupled quadrastability. An energy model is built to analyze and predict the behaviors of the TPQ finger. Mechanical instability is utilized to enhance the actuation speed. The proposed soft lever mechanism endows the TPQ finger with sensing ability. The energy barrier adjusting plates control the energy barrier, adjusting the sensitivity of both active and passive actuation mechanisms. The transition of four stable states forms preplanned trajectories that are applied to create multiple grasping manners. Experiments show that it can respond to stimuli and finish a grasping task in merely 31 ms, and its payload can reach 33.25 kg. At the same time, it can also handle fragile objects such as a piece of rose and grasp a wide range of objects ranging from a thin nut (3.3 mm in height) or a thin card (0.76 mm thick) to a football (220 mm).

A Hand Gesture Recognition Strategy Based on Virtual-Dimension Increase of EMG

The electromyography(EMG) signal is the biocurrent associated with muscle contraction and can be used as the input signal to a myoelectric intelligent bionic hand to control different gestures of the hand. Increasing the number of myoelectric-signal channels can yield richer information of motion intention and improve the accuracy of gesture recognition. However, as the number of acquisition channels increases, its effect on the improvement of the accuracy of gesture recognition gradually diminishes, resulting in the improvement of the control effect reaching a plateau. To address these problems, this paper presents a proposed method to improve gesture recognition accuracy by virtually increasing the number of EMG signal channels. This method is able to improve the recognition accuracy of various gestures by virtually increasing the number of EMG signal channels and enriching the motion intention information extracted from data collected from a certain number of physical channels, ultimately providing a solution to the issue of the recognition accuracy plateau caused by saturation of information from physical recordings. Meanwhile, based on the idea of the filtered feature selection method, a quantitative measure of sample sets (separability of feature vectors [SFV]) derived from the divergence and correlation of the extracted features is introduced. The SFV value can predict the classification effect before performing the classification, and the effectiveness of the virtual-dimension increase strategy is verified from the perspective of feature set differentiability change. Compared to the statistical motion intention recognition success rate, SFV is a more representative and faster measure of classification effectiveness and is also suitable for small sample sets.

A Semi-Autonomous Hierarchical Control Framework for Prosthetic Hands Inspired by Dual Streams of Human

The routine use of prosthetic hands significantly enhances amputees' daily lives, yet it often introduces cognitive load and reduces reaction speed. To address this issue, we introduce a wearable semi-autonomous hierarchical control framework tailored for amputees. Drawing inspiration from the visual processing stream in humans, a fully autonomous bionic controller is integrated into the prosthetic hand control system to offload cognitive burden, complemented by a Human-in-the-Loop (HIL) control method. In the ventral-stream phase, the controller integrates multi-modal information from the user's hand-eye coordination and biological instincts to analyze the user's movement intention and manipulate primitive switches in the variable domain of view. Transitioning to the dorsal-stream phase, precise force control is attained through the HIL control strategy, combining feedback from the prosthetic hand's sensors and the user's electromyographic (EMG) signals. The effectiveness of the proposed interface is demonstrated by the experimental results. Our approach presents a more effective method of interaction between a robotic control system and the human.

Vascularized Composite Allograft Versus Prosthetic for Reconstruction After Facial and Hand Trauma: Comparing Cost, Complications, and Long-term Outcome

ABSTRACT

In the past decade, vascularized composite allotransplantation (VCA) has become clinical reality for reconstruction after face and hand trauma. It offers patients the unique opportunity to regain form and function in a way that had only been achieved with traditional reconstruction or with the use of prostheses. On the other hand, prostheses for facial and hand reconstruction have continued to evolve over the years and, in many cases, represent the primary option for patients after hand and face trauma. We compared the cost, associated complications, and long-term outcomes of VCA with prostheses for reconstruction of the face and hand/upper extremity. Ultimately, VCA and prostheses represent 2 different reconstructive options with distinct benefit profiles and associated limitations and should ideally not be perceived as competing choices. Our work adds a valuable component to the general framework guiding the decision to offer VCA or prostheses for reconstruction after face and upper extremity trauma.

Simulation and experimental study on rope driven artificial hand and driven motor

BACKGROUND

Prosthetic hands have the potential to replace human hands. Using prosthetic hands can help patients with hand loss to complete the necessary daily living actions.

OBJECTIVE

This paper studies the design of a bionic, compact, low-cost, and lightweight 3D printing humanoid hand. The five fingers are underactuated, with a total of 9 degrees of freedom.

METHODS

In the design of an underactuated hand, it is a basic element composed of an actuator, spring, rope, and guide system. A single actuator is providing power for five fingers. And the dynamic simulation is carried out to calculate the motion trajectory effect.

RESULTS

In this paper, the driving structure of the ultrasonic motor was designed, and the structural size of the ultrasonic motor vibrator was determined by modal and transient simulation analysis, which replace the traditional brake, realize the lightweight design of prosthetic hand, improve the motion accuracy and optimize the driving performance of prosthetic hand.

CONCLUSIONS

By replacing traditional actuators with new types of actuators, lightweight design of prosthetic hands can be achieved, improving motion accuracy and optimizing the driving performance of prosthetic hands.

Highly Anthropomorphic Finger Design With a Novel Friction Clutch for Achieving Human-Like Reach-and-Grasp Movements

In the design of prosthetic hand fingers, achieving human-like movement while meeting anthropomorphic demands such as appearance, size, and lightweight is quite challenging. Human finger movement involves two distinct motion characters during natural reach-and-grasp tasks: consistency in the reaching stage and adaptability in the grasping stage. The former one enhances grasp stability and reduces control complexity; the latter one promotes the adaptability of finger to various objects. However, conventional tendon-driven prosthetic finger designs typically incorporate bulky actuation modules or complex tendon routes to reconcile the consistency and adaptability. In contrast, we propose a novel friction clutch consisting of a single tendon and slider, which is simple and compact enough to be configurated within the metacarpal bone. Through tactfully exploiting the friction force to balance the gravity effect on each phalanx during finger motion, this design effectively combines both consistency and adaptability. As a result, the prosthetic finger can maintain consistent motion unaffected by any spatial posture during reaching, execute adaptive motion during grasping, and automatically switch between them, resulting in human-like reach-and-grasp movements. Additionally, the proposed finger achieves a highly anthropomorphic design, weighing only 18.9 g and possessing the same size as an adult's middle finger. Finally, a series of experiments validate the theoretical effectiveness and motion performance of the proposed design. Remarkably, the mechanical principle of the proposed friction clutch is beneficial to achieve highly anthropomorphic design, providing not only a new strategy to prosthetic hand design but also great potential in hand rehabilitation.

Transformation of foldable robotic hand to scissor-like shape for pinching based on human hand movement

Increasing the number of degrees of freedom for multi-finger robotic hands is necessary to achieve high performance. However, this increases structural complexity and the obtained improvement may be small. Humans change the shape of their hands by extending or bending the fingers to apply force to an object through contact with a wide surface or two or more fingers. In some cases, continuous finger movements are not necessary or some fingers do not make contact with the object. A robotic hand with a small number of degrees of freedom could effectively use its fingers to perform many tasks by properly arranging the fingers, increasing the movable range of joints, and utilizing the back and sides of the fingers. This paper proposes a hand system and conducts a theoretical analysis of the transformation of the hand shape into a scissor-like motion to handle a cylindrical object. It is found that the scissor-like motion is unsuitable for cylindrical objects that exceed a certain size. Experiments show the effectiveness of the proposed hand system. The correlation between the contact position of a finger with an object and the success ratio of pinching is demonstrated. Furthermore, a control system that can switch from pinching to grasping when the robot judges that pinching is difficult is developed and experimentally validated.

Decomposition and Reconstruction of Human Palm Movements

OBJECTIVE

The human hand is known to have excellent manipulation ability compared to other primate hands. Without the palm movements, the human hand would lose more than 40% of its functions. However, uncovering the constitution of palm movements is still a challenging problem involving kinesiology, physiology, and engineering science.

METHODS

By recording the palm joint angles during common grasping, gesturing, and manipulation tasks, we built a palm kinematic dataset. Then, a method for extracting the eigen-movements to characterize the common motion correlation relationships of palm joints was proposed to explore the palm movement constitution.

RESULTS

This study revealed a palm kinematic characteristic that we named the joint motion grouping coupling characteristic. During natural palm movements, there are several joint groups with a high degree of motor independence, while the movements of joints within each joint group are interdependent. Based on these characteristics, the palm movements can be decomposed into seven eigen-movements. The linear combinations of these eigen-movements can reconstruct more than 90% of palm movement ability. Moreover, combined with the palm musculoskeletal structures, we found that the revealed eigen-movements are associated with joint groups that are defined by muscular functions, which provided a meaningful context for palm movement decomposition.

CONCLUSION

This paper suggests that some invariable characteristics underlie the variable palm motor behaviors and can be used to simplify palm movement generation.

SIGNIFICANCE

This paper provides important insights into palm kinematics, and helps facilitate motor function assessment and the development of better artificial hands.

A Dual-Modal Hybrid Gripper with Wide Tunable Contact Stiffness Range and High Compliance for Adaptive and Wide-Range Grasping Objects with Diverse Fragilities

The difficulties of traditional rigid/soft grippers in meeting the increasing performance expectations (e.g., high grasping adaptability and wide graspable objects range) of a single robotic gripper have given birth to numerous soft-rigid coupling grippers with promising performance. However, it is still hard for these hybrid grippers to adaptively grasp various objects with diverse fragilities intact, such as incense ash and orange, due to their limited contact stiffness adjustable range and compliance. To solve these challenging issues, herein, we propose a dual-modal hybrid gripper, whose fingers contain a detachable elastomer-coated flexible sheet that is restrained by a moving frame as a teardrop shape. The gripper's two modes switched by controlling the moving frame position can selectively highlight the low contact stiffness and excellent compliance of the teardrop-shaped flexible sheets and the high contact stiffness of the moving frames. Moreover, the contact stiffness of the teardrop-shaped sheets can be wide-range adjusted by online controlling the moving frame position and offline replacing the sheets with different thicknesses. The compliance of the teardrop-shaped sheets also proves to be excellent compared with an Ecoflex 10 fingertip with the same profile. Such a gripper with wide-range tunable contact stiffness and high compliance demonstrates excellent grasping adaptability (e.g., it can safely grasp several fragile strawberries with a maximum size difference of 18 mm, a strawberry with a left/right offset of 3 cm, and a strawberry in two different lying poses) and wide-range graspable objects (from 0.1 g super fragile cigarette ashes to 5.1 kg dumbbell).

Experimental Evaluation of a Hybrid Sensory Feedback System for Haptic and Kinaesthetic Perception in Hand Prostheses

This study proposes a new hybrid multi-modal sensory feedback system for prosthetic hands that can provide not only haptic and proprioceptive feedback but also facilitate object recognition without the aid of vision. Modality-matched haptic perception was provided using a mechanotactile feedback system that can proportionally apply the gripping force through the use of a force controller. A vibrotactile feedback system was also employed to distinguish four discrete grip positions of the prosthetic hand. The system performance was evaluated with a total of 32 participants in three different experiments (i) haptic feedback, (ii) proprioceptive feedback and (iii) object recognition with hybrid haptic-proprioceptive feedback. The results from the haptic feedback experiment showed that the participants' ability to accurately perceive applied force depended on the amount of force applied. As the feedback force was increased, the participants tended to underestimate the force levels, with a decrease in the percentage of force estimation. Of the three arm locations (forearm volar, forearm ventral and bicep), and two muscle states (relaxed and tensed) tested, the highest accuracy was obtained for the bicep location in the relaxed state. The results from the proprioceptive feedback experiment showed that participants could very accurately identify four different grip positions of the hand prosthesis (i.e., open hand, wide grip, narrow grip, and closed hand) without a single case of misidentification. In experiment 3, participants could identify objects with different shapes and stiffness with an overall high success rate of 90.5% across all combinations of location and muscle state. The feedback location and muscle state did not have a significant effect on object recognition accuracy. Overall, our study results indicate that the hybrid feedback system may be a very effective way to enrich a prosthetic hand user's experience of the stiffness and shape of commonly manipulated objects.

Favoring the cognitive-motor process in the closed-loop of BCI mediated post stroke motor function recovery: challenges and approaches

The brain-computer interface (BCI)-mediated rehabilitation is emerging as a solution to restore motor skills in paretic patients after stroke. In the human brain, cortical motor neurons not only fire when actions are carried out but are also activated in a wired manner through many cognitive processes related to movement such as imagining, perceiving, and observing the actions. Moreover, the recruitment of motor cortexes can usually be regulated by environmental conditions, forming a closed-loop through neurofeedback. However, this cognitive-motor control loop is often interrupted by the impairment of stroke. The requirement to bridge the stroke-induced gap in the motor control loop is promoting the evolution of the BCI-based motor rehabilitation system and, notably posing many challenges regarding the disease-specific process of post stroke motor function recovery. This review aimed to map the current literature surrounding the new progress in BCI-mediated post stroke motor function recovery involved with cognitive aspect, particularly in how it refired and rewired the neural circuit of motor control through motor learning along with the BCI-centric closed-loop.

Consistent control information driven musculoskeletal model for multiday myoelectric control

Objective.Musculoskeletal model (MM)-based myoelectric interface has aroused great interest in human-machine interaction. However, the performance of electromyography (EMG)-driven MM in long-term use would be degraded owing to the inherent non-stationary characteristics of EMG signals. Here, to improve the estimation performance without retraining, we proposed a consistent muscle excitation extraction approach based on an improved non-negative matrix factorization (NMF) algorithm for MM when applied to simultaneous hand and wrist movement prediction.Approach.We added constraints andL2-norm regularization terms to the objective function of classic NMF regarding muscle weighting matrix and time-varying profiles, through which stable muscle synergies across days were identified. The resultant profiles of these synergies were then used to drive the MM. Both offline and online experiments were conducted to evaluate the performance of the proposed method in inter-day scenarios.Main results.The results demonstrated significantly better and more robust performance over several competitive methods in inter-day experiments, including machine learning methods, EMG envelope-driven MM, and classic NMF-based MM. Furthermore, the analysis of control information on different days revealed the effectiveness of the proposed method in obtaining consistent muscle excitations.Significance.The outcomes potentially provide a novel and promising pathway for the robust and zero-retraining control of myoelectric interfaces.

The Study of Complex Manipulation via Kinematic Hand Synergies: The Effects of Data Pre-Processing

The study of kinematic hand synergies through matrix decomposition techniques, such as singular value decomposition, supports the theory that humans might control a subspace of predefined motions during manipulation tasks. These subspaces are often referred to as synergies. However, different data pre-processing methods lead to quantitatively different conclusions about these synergies. In this work, we shed light on the role of data pre-processing on the study of hand synergies by analyzing both numerical simulation and real kinematic data from a complex manipulation task, i.e., piano playing. The results obtained suggest that centering the data, by removing the mean, appears to be the most appropriate preprocessing technique for studying kinematic hand synergies.

Preliminary Assessment of Two Simultaneous and Proportional Myocontrol Methods for 3-DoFs Prostheses Using Incremental Learning

Despite progressive developments over the last decades, current upper limb prostheses still lack a suitable control able to fully restore the functionalities of the lost arm. Traditional control approaches for prostheses fail when simultaneously actuating multiple Degrees of Freedom (DoFs), thus limiting their usability in daily-life scenarios. Machine learning, on the one hand, offers a solution to this issue through a promising approach for decoding user intentions but fails when input signals change. Incremental learning, on the other hand, reduces sources of error by quickly updating the model on new data rather than training the control model from scratch. In this study, we present an initial evaluation of a position and a velocity control strategy for simultaneous and proportional control over 3-DoFs based on incremental learning. The proposed controls are tested using a virtual Hannes prosthesis on two healthy participants. The performances are evaluated over eight sessions by performing the Target Achievement Control test and administering SUS and NASA-TLX questionnaires. Overall, this preliminary study demonstrates that both control strategies are promising approaches for prosthetic control, offering the potential to improve the usability of prostheses for individuals with limb loss. Further research extended to a wider population of both healthy subjects and amputees will be essential to thoroughly assess these control paradigms.

IMU Sensors Measurements Towards the Development of Novel Prosthetic Arm Control Strategies

The complexity of the human upper limb makes replicating it in a prosthetic device a significant challenge. With advancements in mechatronic developments involving the addition of a large number of degrees of freedom, novel control strategies are required. To accommodate this need, this study aims at developing an IMU-based control for the HannesARM upper-limb prosthetic device, as a proof-of-concept for new control strategies integrating data-fusion approaches. The natural human control of the upper-limb is based on different inputs that allow adaptive control. To mimic this in prostheses, the implementation of IMUs provides kinematic information of both the stump and the prosthesis to enrich the EMG control. The principle of operation is to decode upper limb movements by using a custom-made system and to replicate them in prosthetic arms improving the control algorithms. To evaluate the system's effectiveness, the custom algorithm's motion extraction was compared to a motion capture system using fifteen able-bodied subjects. The results showed that this system scored 0.16 ± 0.04 and 0.81 ± 0.12 in Root Mean Squared Error and Cross-Correlation compared to the motion capture system. Experimental results demonstrate how this work can extract valuable kinematic information necessary for new and improved control strategies, such as intention detection or pattern recognition, to allow users to perform a broader range of tasks and enhancing in turn their quality of life.

Design of a Multi-Mode Mechanical Finger Based on Linkage and Tendon Fusion Transmission

Today, most humanoid mechanical fingers use an underactuated mechanism driven by linkages or tendons, with only a single and fixed grasping trajectory. This paper proposes a new multi-mode humanoid finger mechanism based on linkage and tendon fusion transmission, which is embedded with an adjustable-length tendon mechanism to achieve three types of grasping mode. The structural parameters of the mechanism are optimized according to the kinematic and static models. Furthermore, a discussion was conducted on how to set the speed ratio of the linkage driving motor and the tendon driving motor to adjust the length and tension of the tendon, in order to achieve the switching of the shape-adaptive, coupled-adaptive, and variable coupling-adaptive grasping modes. Finally, the multi-mode functionality of the proposed finger mechanism was verified through multiple grasping experiments.

Artificial intelligence meets medical robotics

Artificial intelligence (AI) applications in medical robots are bringing a new era to medicine. Advanced medical robots can perform diagnostic and surgical procedures, aid rehabilitation, and provide symbiotic prosthetics to replace limbs. The technology used in these devices, including computer vision, medical image analysis, haptics, navigation, precise manipulation, and machine learning (ML) , could allow autonomous robots to carry out diagnostic imaging, remote surgery, surgical subtasks, or even entire surgical procedures. Moreover, AI in rehabilitation devices and advanced prosthetics can provide individualized support, as well as improved functionality and mobility (see the figure). The combination of extraordinary advances in robotics, medicine, materials science, and computing could bring safer, more efficient, and more widely available patient care in the future. -Gemma K. Alderton.

Finger Kinematics during Human Hand Grip and Release

A bionic robotic hand can perform many movements similar to a human hand. However, there is still a significant gap in manipulation between robot and human hands. It is necessary to understand the finger kinematics and motion patterns of human hands to improve the performance of robotic hands. This study aimed to comprehensively investigate normal hand motion patterns by evaluating the kinematics of hand grip and release in healthy individuals. The data corresponding to rapid grip and release were collected from the dominant hands of 22 healthy people by sensory glove. The kinematics of 14 finger joints were analyzed, including the dynamic range of motion (ROM), peak velocity, joint sequence and finger sequence. The results show that the proximal interphalangeal (PIP) joint had a larger dynamic ROM than metacarpophalangeal (MCP) and distal interphalangeal (DIP) joints. Additionally, the PIP joint had the highest peak velocity, both in flexion and extension. For joint sequence, the PIP joint moved prior to the DIP or MCP joints during flexion, while extension started in DIP or MCP joints, followed by the PIP joint. Regarding the finger sequence, the thumb started to move before the four fingers, and stopped moving after the fingers during both grip and release. This study explored the normal motion patterns in hand grip and release, which provided a kinematic reference for the design of robotic hands and thus contributes to its development.

Empowering Hand Rehabilitation with AI-Powered Gesture Recognition: A Study of an sEMG-Based System

Stroke is one of the most prevalent health issues that people face today, causing long-term complications such as paresis, hemiparesis, and aphasia. These conditions significantly impact a patient's physical abilities and cause financial and social hardships. In order to address these challenges, this paper presents a groundbreaking solution-a wearable rehabilitation glove. This motorized glove is designed to provide comfortable and effective rehabilitation for patients with paresis. Its unique soft materials and compact size make it easy to use in clinical settings and at home. The glove can train each finger individually and all fingers together, using assistive force generated by advanced linear integrated actuators controlled by sEMG signals. The glove is also durable and long-lasting, with 4-5 h of battery life. The wearable motorized glove is worn on the affected hand to provide assistive force during rehabilitation training. The key to this glove's effectiveness is its ability to perform the classified hand gestures acquired from the non-affected hand by integrating four sEMG sensors and a deep learning algorithm (the 1D-CNN algorithm and the InceptionTime algorithm). The InceptionTime algorithm classified ten hand gestures' sEMG signals with an accuracy of 91.60% and 90.09% in the training and verification sets, respectively. The overall accuracy was 90.89%. It showed potential as a tool for developing effective hand gesture recognition systems. The classified hand gestures can be used as a control command for the motorized wearable glove placed on the affected hand, allowing it to mimic the movements of the non-affected hand. This innovative technology performs rehabilitation exercises based on the theory of mirror therapy and task-oriented therapy. Overall, this wearable rehabilitation glove represents a significant step forward in stroke rehabilitation, offering a practical and effective solution to help patients recover from stroke's physical, financial, and social impact.

Bio-robotics research for non-invasive myoelectric neural interfaces for upper-limb prosthetic control: a 10-year perspective review

A decade ago, a group of researchers from academia and industry identified a dichotomy between the industrial and academic state-of-the-art in upper-limb prosthesis control, a widely used bio-robotics application. They proposed that four key technical challenges, if addressed, could bridge this gap and translate academic research into clinically and commercially viable products. These challenges are unintuitive control schemes, lack of sensory feedback, poor robustness and single sensor modality. Here, we provide a perspective review on the research effort that occurred in the last decade, aiming at addressing these challenges. In addition, we discuss three research areas essential to the recent development in upper-limb prosthetic control research but were not envisioned in the review 10 years ago: deep learning methods, surface electromyogram decomposition and open-source databases. To conclude the review, we provide an outlook into the near future of the research and development in upper-limb prosthetic control and beyond.

Bioinspired Multimodal Multipose Hybrid Fingers for Wide-Range Force, Compliant, and Stable Grasping

The increasing demand for grasping diverse objects in unstructured environments poses severe challenges to the existing soft/rigid robotic fingers due to the issues in balancing force, compliance, and stability, and hence has given birth to several hybrid designs. These hybrid designs utilize the advantages of rigid and soft structures and show better performance, but they are still suffering from narrow output force range, limited compliance, and rarely reported stability. Owing to its rigid-soft coupling structure with flexible switched multiple poses, human finger, as an excellent hybrid design, shows wide-range output force, excellent compliance, and stability. Inspired by human finger, we propose a hybrid finger with multiple modes and poses, coupled by a soft actuator (SA) and a rigid actuator (RA) in parallel. The multiple actuation modes formed by a pneumatic-based rigid-soft collaborative strategy can selectively enable the RA's high force and SA's softness, whereas the multiple poses derived from the specially designed underactuated RA skeleton can be flexibly switched with tasks, thus achieving high compliance. Such hybrid fingers also proved to be highly stable under external stimuli or gravity. Furthermore, we modularize and configure these fingers into a series of grippers with excellent grasping performance, for example, wide graspable object range (diverse from 0.1 g potato chips to 27 kg dumbbells for a 420 g two-finger gripper), high compliance (tolerate objects with 94% gripper span size and 4 cm offset), and high stability. Our study highlights the potential of fusing rigid-soft technologies for robot development, and potentially impacts future bionics and high-performance robot development.

Piezo robotic hand for motion manipulation from micro to macro

Multiple degrees of freedom (DOFs) motion manipulation of various objects is a crucial skill for robotic systems, which relies on various robotic hands. However, traditional robotic hands suffer from problems of low manipulation accuracy, poor electromagnetic compatibility and complex system due to limitations in structures, principles and transmissions. Here we present a direct-drive rigid piezo robotic hand (PRH) constructed on functional piezoelectric ceramic. Our PRH holds four piezo fingers and twelve motion DOFs. It achieves high adaptability motion manipulation of ten objects employing pre-planned functionalized hand gestures, manipulating plates to achieve 2L (linear) and 1R (rotary) motions, cylindrical objects to generate 1L and 1R motions and spherical objects to produce 3R motions. It holds promising prospects in constructing multi-DOF ultra-precision manipulation devices, and an integrated system of our PRH is developed to implement several applications. This work provides a new direction to develop robotic hand for multi-DOF motion manipulation from micro scale to macro scale.

Design and control of intelligent bionic artificial hand based on image recognition

BACKGROUND

At present, the popular control method for intelligent bionic prosthetic hands is EMG control. However, the control accuracy of this method is low. It is a trend to integrate computer vision into the prosthetic hand.

OBJECTIVE

The purpose of this paper is to design an intelligent prosthetic hand based on image recognition, improve the control accuracy and the quality of life of the disabled.

METHODS

Convolutional neural network is used to recognize the object to be grasped, and the recognition result is used as a trigger signal to control our intelligent prosthetic hand. We have designed a four-bar linkage mechanism and a side swing mechanism in the structure, which can not only achieve the flexion and extension of fingers but also realize the adduction and abduction of the four fingers and the lateral swing of the thumb.

RESULTS

Through the method of image recognition, the new intelligent bionic hand can achieve five kinds of Human action. Including grasp, side pinch, three-finger pinch, two-finger pinch, and pinch between fingers.

CONCLUSIONS

The experiment result proves that the precision of image recognition control is very excellent, the intelligent prosthetic hand can be completed the corresponding task.

Humanlike spontaneous motion coordination of robotic fingers through spatial multi-input spike signal multiplexing

With advances in robotic technology, the complexity of control of robot has been increasing owing to fundamental signal bottlenecks and limited expressible logic state of the von Neumann architecture. Here, we demonstrate coordinated movement by a fully parallel-processable synaptic array with reduced control complexity. The synaptic array was fabricated by connecting eight ion-gel-based synaptic transistors to an ion gel dielectric. Parallel signal processing and multi-actuation control could be achieved by modulating the ionic movement. Through the integration of the synaptic array and a robotic hand, coordinated movement of the fingers was achieved with reduced control complexity by exploiting the advantages of parallel multiplexing and analog logic. The proposed synaptic control system provides considerable scope for the advancement of robotic control systems.

Object stiffness recognition and vibratory feedback without ad-hoc sensing on the Hannes prosthesis: A machine learning approach

Introduction

In recent years, hand prostheses achieved relevant improvements in term of both motor and functional recovery. However, the rate of devices abandonment, also due to their poor embodiment, is still high. The embodiment defines the integration of an external object - in this case a prosthetic device - into the body scheme of an individual. One of the limiting factors causing lack of embodiment is the absence of a direct interaction between user and environment. Many studies focused on the extraction of tactile information via custom electronic skin technologies coupled with dedicated haptic feedback, though increasing the complexity of the prosthetic system. Contrary wise, this paper stems from the authors' preliminary works on multi-body prosthetic hand modeling and the identification of possible intrinsic information to assess object stiffness during interaction.

Methods

Based on these initial findings, this work presents the design, implementation and clinical validation of a novel real-time stiffness detection strategy, without ad-hoc sensing, based on a Non-linear Logistic Regression (NLR) classifier. This exploits the minimum grasp information available from an under-sensorized and under-actuated myoelectric prosthetic hand, Hannes. The NLR algorithm takes as input motor-side current, encoder position, and reference position of the hand and provides as output a classification of the grasped object (no-object, rigid object, and soft object). This information is then transmitted to the user via vibratory feedback to close the loop between user control and prosthesis interaction. This implementation was validated through a user study conducted both on able bodied subjects and amputees.

Results

The classifier achieved excellent performance in terms of F1Score (94.93%). Further, the able-bodied subjects and amputees were able to successfully detect the objects' stiffness with a F1Score of 94.08% and 86.41%, respectively, by using our proposed feedback strategy. This strategy allowed amputees to quickly recognize the objects' stiffness (response time of 2.82 s), indicating high intuitiveness, and it was overall appreciated as demonstrated by the questionnaire. Furthermore, an embodiment improvement was also obtained as highlighted by the proprioceptive drift toward the prosthesis (0.7 cm).

A lightweight robotic leg prosthesis replicating the biomechanics of the knee, ankle, and toe joint

Robotic leg prostheses promise to improve the mobility and quality of life of millions of individuals with lower-limb amputations by imitating the biomechanics of the missing biological leg. Unfortunately, existing powered prostheses are much heavier and bigger and have shorter battery life than conventional passive prostheses, severely limiting their clinical viability and utility in the daily life of amputees. Here, we present a robotic leg prosthesis that replicates the key biomechanical functions of the biological knee, ankle, and toe in the sagittal plane while matching the weight, size, and battery life of conventional microprocessor-controlled prostheses. The powered knee joint uses a unique torque-sensitive mechanism combining the benefits of elastic actuators with that of variable transmissions. A single actuator powers the ankle and toe joints through a compliant, underactuated mechanism. Because the biological toe dissipates energy while the biological ankle injects energy into the gait cycle, this underactuated system regenerates substantial mechanical energy and replicates the key biomechanical functions of the ankle/foot complex during walking. A compact prosthesis frame encloses all mechanical and electrical components for increased robustness and efficiency. Preclinical tests with three individuals with above-knee amputation show that the proposed robotic leg prosthesis allows for common ambulation activities with close to normative kinematics and kinetics. Using an optional passive mode, users can walk on level ground indefinitely without charging the battery, which has not been shown with any other powered or microprocessor-controlled prostheses. A prosthesis with these characteristics has the potential to improve real-world mobility in individuals with above-knee amputation.

Co-optimization of robotic design and skill inspired by human hand evolution

During evolution of the human hand, evolutionary morphology has been closely related to behavior in complicated environments. Numerous researchers have revealed that learned skills have affected hand evolution. Inspired by this phenomenon, a co-optimization approach for underactuated hands is proposed that takes grasping skills and structural parameters into consideration. In our proposal, hand design, especially the underactuated mechanism, can be parameterized and shared with all the local agents. These mechanical parameters can be updated globally by the independent agents. In addition, we also train human-like 'feeling' of grasping: grasping stability is estimated in advance before the object drops, which can speed up grasping training. In this paper, our method is instantiated to address the optimization problem for the torsion spring mechanical parameters of an underactuated robotic hand with multi-actuators, and then the optimized results are transferred to the actual physical robotic hand to test the improvement of grasping. This collaborative evolution process leverages the dexterity of the multi-actuators and the adaptivity of the underactuated mechanism.

A multi-dimensional framework for prosthetic embodiment: a perspective for translational research

The concept of embodiment has gained widespread popularity within prosthetics research. Embodiment has been claimed to be an indicator of the efficacy of sensory feedback and control strategies. Moreover, it has even been claimed to be necessary for prosthesis acceptance, albeit unfoundedly. Despite the popularity of the term, an actual consensus on how prosthetic embodiment should be used in an experimental framework has yet to be reached. The lack of consensus is in part due to terminological ambiguity and the lack of an exact definition of prosthetic embodiment itself. In a review published parallel to this article, we summarized the definitions of embodiment used in prosthetics literature and concluded that treating prosthetic embodiment as a combination of ownership and agency allows for embodiment to be quantified, and thus useful in translational research. Here, we review the potential mechanisms that give rise to ownership and agency considering temporal, spatial, and anatomical constraints. We then use this to propose a multi-dimensional framework where prosthetic embodiment arises within a spectrum dependent on the integration of volition and multi-sensory information as demanded by the degree of interaction with the environment. This framework allows for the different experimental paradigms on sensory feedback and prosthetic control to be placed in a common perspective. By considering that embodiment lays along a spectrum tied to the interactions with the environment, one can conclude that the embodiment of prosthetic devices should be assessed while operating in environments as close to daily life as possible for it to become relevant.

A user-friendly automatic toolbox for hand kinematic analysis, clinical assessment and postural synergies extraction

The clinical assessment of the human hand is typically conducted through questionnaires or tests that include objective (e.g., time) and subjective (e.g., grasp quality) outcome measures. However, there are other important indicators that should be considered to quantify grasp and movement quality in addition to the time needed by a subject to execute a task, and this is essential for human and artificial hands that attempt to replicate the human hand properties. The correct estimation of hand kinematics is fundamental for computing these indicators with high fidelity, and a technical background is typically required to perform this analysis. In addition, to understand human motor control strategies as well as to replicate them on artificial devices, postural synergies were widely explored in recent years. Synergies should be analyzed not only to investigate possible modifications due to musculoskeletal and/or neuromuscular disorders, but also to test biomimetic hands. The aim of this work is to present an open source toolbox to perform all-in-one kinematic analysis and clinical assessment of the hand, as well as to perform postural synergies extraction. In the example provided in this work, the tool takes as input the position of 28 retroreflective markers with a diameter of 6 mm, positioned on specific anatomical landmarks of the hand and recorded with an optoelectronic motion capture system, and automatically performs 1) hand kinematic analysis (i.e., computation of 23 joint angles); 2) clinical assessment, by computing indicators that allow quantifying movement efficiency (Peak Grip Aperture), smoothness (Normalized Dimensionless Jerk Grasp Aperture) and speed (Peak Velocity of Grasp Aperture), planning capabilities (Time to Peak Grip Aperture), spatial posture (Wrist and Finger Joint Angles) and grasp stability (Posture of Hand Finger Joints), and 3) postural synergies extraction and analysis through the Pareto, Scree and Loadings plots. Two examples are described to demonstrate the applicability of the toolbox: the first one aiming at performing a clinical assessment of a volunteer and the second one aiming at extracting and analyzing the volunteer's postural synergies. The tool allows calculating joint angles with high accuracy (reconstruction errors below 4 mm and 3.2 mm for the fingers and wrist respectively) and automatically performing clinical assessment and postural synergies extraction. Results can be visually inspected, and data can be saved for any desired post processing analysis. Custom-made protocols to extract joint angles, based on different markersets, could be also integrated in the toolbox. The tool can be easily exploitable in clinical contexts, as it does not require any particular technical knowledge to be used, as confirmed by the usability evaluation conducted (perceived usability = 94.2 ± 5.4). In addition, it can be integrated with the SynGrasp toolbox to perform grasp analysis of underactuated virtual hands based on postural synergies.

The sense of agency in emerging technologies for human–computer integration: A review

Human–computer integration is an emerging area in which the boundary between humans and technology is blurred as users and computers work collaboratively and share agency to execute tasks. The sense of agency (SoA) is an experience that arises by a combination of a voluntary motor action and sensory evidence whether the corresponding body movements have somehow influenced the course of external events. The SoA is not only a key part of our experiences in daily life but also in our interaction with technology as it gives us the feeling of “I did that” as opposed to “the system did that,” thus supporting a feeling of being in control. This feeling becomes critical with human–computer integration, wherein emerging technology directly influences people’s body, their actions, and the resulting outcomes. In this review, we analyse and classify current integration technologies based on what we currently know about agency in the literature, and propose a distinction between body augmentation, action augmentation, and outcome augmentation. For each category, we describe agency considerations and markers of differentiation that illustrate a relationship between assistance level (low, high), agency delegation (human, technology), and integration type (fusion, symbiosis). We conclude with a reflection on the opportunities and challenges of integrating humans with computers, and finalise with an expanded definition of human–computer integration including agency aspects which we consider to be particularly relevant. The aim this review is to provide researchers and practitioners with guidelines to situate their work within the integration research agenda and consider the implications of any technologies on SoA, and thus overall user experience when designing future technology.

A Flexible Artificial Sensory Nerve Enabled by Nanoparticle-Assembled Synaptic Devices for Neuromorphic Tactile Recognition

Tactile perception enabled by somatosensory system in human is essential for dexterous tool usage, communication, and interaction. Imparting tactile recognition functions to advanced robots and interactive systems can potentially improve their cognition and intelligence. Here, a flexible artificial sensory nerve that mimics the tactile sensing, neural coding, and synaptic processing functions in human sensory nerve is developed to achieve neuromorphic tactile recognition at device level without relying on algorithms or computing resources. An interfacial self-assembly technique, which produces uniform and defect-less thin film of semiconductor nanoparticles on arbitrary substrates, is employed to prepare the flexible synaptic device. The neural facilitation and sensory memory characteristics of the proton-gating synaptic device enable this system to identify material hardness during robotic grasping and recognize tapping patterns during tactile interaction in a continuous, real-time, high-accuracy manner, demonstrating neuromorphic intelligence and recognition capabilities. This artificial sensory nerve produced in wearable and portable form can be readily integrated with advanced robots and smart human-machine interfaces for implementing human-like tactile cognition in neuromorphic electronics toward robotic and wearable applications.

Benefits of the Cybathlon 2020 experience for a prosthetic hand user: a case study on the Hannes system

BACKGROUND

Cybathlon championship aims at promoting the development of prosthetic and assistive devices capable to meet users' needs. This paper describes and analyses possible exploitation outcomes of our team's (REHAB TECH) experience into the Powered Arm Prosthesis Race of the Cybathlon 2020 Global Edition, with the novel prosthetic system Hannes. In detail, we present our analysis on a concurrent evaluation conducted to verify if the Cybathlon training and competition positively influenced pilot's performance and human-technology integration with Hannes, with respect to a non-runner Hannes user.

METHODS

Two transradial amputees were recruited as pilots (Pilot 1 and Pilot 2) for the Cybathlon competition and were given the polyarticulated myoelectric prosthetic hand Hannes. Due to COVID-19 emergency, only Pilot 1 was trained for the race. However, both pilots kept Hannes for Home Use for seven weeks. Before this period, they both participated to the evaluation of functionality, embodiment, and user experience (UX) related to Hannes, which they repeated at the end of the Home Use and right after the competition. We analysed Pilot 1's training and race outcomes, as well as changes in the concurrent evaluation, and compared these results with Pilot 2's ones.

RESULTS

The Cybathlon training gradually improved Pilot 1's performances, leading to the sixth place with a single error in task 5. In the parallel evaluation, both pilots had an overall improvement over time, whereas Pilot 2 experienced a deterioration of embodiment. In detail, Pilot 1, who followed the training and raced the Cybathlon, improved in greater way.

CONCLUSION

Hannes demonstrated to be a valuable competitor and to perform grasps with human-like behaviors. The higher improvements of Pilot 1, who actively participated in the Cybathlon, in terms of functionality, embodiment and UX, may depend on his training and engagement in the effort of achieving a successful user-prosthesis interaction during the competition. Tasks based on Cybathlon's ones could improve the training phase of a prosthetic user, stimulating dexterity, prosthetic integration, and user perception towards the prosthesis. Likewise, timed races or competitions could facilitate and accelerate the learning phase, improving the efficiency and efficacy of the process.

Comparative analysis of inverse kinematics methodologies to improve the controllability of rehabilitative robotic devices

The solution of the inverse kinematics problem in multi-degrees of freedom robots has been tackled, through the last three decades, by several different approaches including analytical, geometrical, differential and numerical methods. All these techniques present their own advantages and drawbacks. However, a guideline on which approach is better to follow, depending on the kind of task to perform and the type of robotic device used, is still missing. In this work, a quantitative comparative analysis of three different inverse kinematics methodologies for the control of rehabilitative robotic devices is proposed, with aim of devising best practices and guidelines for the selection of the most suitable approach. The analyzed methodologies are implemented and numerically tested on two actual devices, specifically an upper-limb exoskeleton and an upper-limb prosthetic arm.

CyberLimb: a novel robotic prosthesis concept with shared and intuitive control

Background

Existing assistive technologies attempt to mimic biological functions through advanced mechatronic designs. In some occasions, the information processing demands for such systems require substantial information bandwidth and convoluted control strategies, which make it difficult for the end-user to operate. Instead, a practical and intuitive semi-automated system focused on accomplishing daily tasks may be more suitable for end-user adoption.

Methods

We developed an intelligent prosthesis for the Cybathlon Global Edition 2020. The device was designed in collaboration with the prosthesis user (pilot), addressing her needs for the competition and aiming for functionality. Our design consists of a soft robotic-based two finger gripper controlled by a force-sensing resistor (FSR) headband interface, automatic arm angle dependent wrist flexion and extension, and manual forearm supination and pronation for a shared control system. The gripper is incorporated with FSR sensors to relay haptic information to the pilot based on the output of a neural network model that estimates geometries and objects material.

Results

As a student team of the Munich Institute of Robotics and Machine Intelligence, we achieved 12th place overall in the Cybathlon competition in which we competed against state-of-the-art prosthetic devices. Our pilot successfully accomplished two challenging tasks in the competition. During training sessions, the pilot was able to accomplish the remaining competition tasks except for one. Based on observation and feedback from training sessions, we adapted our developments to fit the user’s preferences. Usability ratings indicated that the pilot perceived the prosthesis to not be fully ergonomic due to the size and weight of the system, but argued that the prosthesis was intuitive to control to perform the tasks from the Cybathlon competition.

Conclusions

The system provides an intuitive interface to conduct common daily tasks from the arm discipline of the Cybathlon competition. Based on the feedback from our pilot, future improvements include the prosthesis’ reduction in size and weight in order to enhance its mobility. Close collaboration with our pilot has allowed us to continue with the prosthesis development. Ultimately, we developed a simple-to-use solution, exemplifying a new paradigm for prosthesis design, to help assist arm amputees with daily activities.

A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods

Multifingered robotic hands (usually referred to as dexterous hands) are designed to achieve human-level or human-like manipulations for robots or as prostheses for the disabled. The research dates back 30 years ago, yet, there remain great challenges to effectively design and control them due to their high dimensionality of configuration, frequently switched interaction modes, and various task generalization requirements. This article aims to give a brief overview of multifingered robotic manipulation from three aspects: a) the biological results, b) the structural evolvements, and c) the learning methods, and discuss potential future directions. First, we investigate the structure and principle of hand-centered visual sensing, tactile sensing, and motor control and related behavioral results. Then, we review several typical multifingered dexterous hands from task scenarios, actuation mechanisms, and in-hand sensors points. Third, we report the recent progress of various learning-based multifingered manipulation methods, including but not limited to reinforcement learning, imitation learning, and other sub-class methods. The article concludes with open issues and our thoughts on future directions.

How to compete with robots by assessing job automation risks and resilient alternatives

The effects of robotics and artificial intelligence (AI) on the job market are matters of great social concern. Economists and technology experts are debating at what rate, and to what extent, technology could be used to replace humans in occupations, and what actions could mitigate the unemployment that would result. To this end, it is important to predict which jobs could be automated in the future and what workers could do to move to occupations at lower risk of automation. Here, we calculate the automation risk of almost 1000 existing occupations by quantitatively assessing to what extent robotics and AI abilities can replace human abilities required for those jobs. Furthermore, we introduce a method to find, for any occupation, alternatives that maximize the reduction in automation risk while minimizing the retraining effort. We apply the method to the U.S. workforce composition and show that it could substantially reduce the workers' automation risk, while the associated retraining effort would be moderate. Governments could use the proposed method to evaluate the unemployment risk of their populations and to adjust educational policies. Robotics companies could use it as a tool to better understand market needs, and members of the public could use it to identify the easiest route to reposition themselves on the job market.

Prosthetic embodiment: systematic review on definitions, measures, and experimental paradigms

The term embodiment has become omnipresent within prosthetics research and is often used as a metric of the progress made in prosthetic technologies, as well as a hallmark for user acceptance. However, despite the frequent use of the term, the concept of prosthetic embodiment is often left undefined or described incongruently, sometimes even within the same article. This terminological ambiguity complicates the comparison of studies using embodiment as a metric of success, which in turn hinders the advancement of prosthetics research. To resolve these terminological ambiguities, we systematically reviewed the used definitions of embodiment in the prosthetics literature. We performed a thematic analysis of the definitions and found that embodiment is often conceptualized in either of two frameworks based on body representations or experimental phenomenology. We concluded that treating prosthetic embodiment within an experimental phenomenological framework as the combination of ownership and agency allows for embodiment to be a quantifiable metric for use in translational research. To provide a common reference and guidance on how to best assess ownership and agency, we conducted a second systematic review, analyzing experiments and measures involving ownership and agency. Together, we highlight a pragmatic definition of prosthetic embodiment as the combination of ownership and agency, and in an accompanying article, we provide a perspective on a multi-dimensional framework for prosthetic embodiment. Here, we concluded by providing recommendations on metrics that allow for outcome comparisons between studies, thereby creating a common reference for further discussions within prosthetics research.

Continuous Semi-autonomous Prosthesis Control Using a Depth Sensor on the Hand

Modern myoelectric prostheses can perform multiple functions (e.g., several grasp types and wrist rotation) but their intuitive control by the user is still an open challenge. It has been recently demonstrated that semi-autonomous control can allow the subjects to operate complex prostheses effectively; however, this approach often requires placing sensors on the user. The present study proposes a system for semi-autonomous control of a myoelectric prosthesis that requires a single depth sensor placed on the dorsal side of the hand. The system automatically pre-shapes the hand (grasp type, size, and wrist rotation) and allows the user to grasp objects of different shapes, sizes and orientations, placed individually or within cluttered scenes. The system “reacts” to the side from which the object is approached, and enables the user to target not only the whole object but also an object part. Another unique aspect of the system is that it relies on online interaction between the user and the prosthesis; the system reacts continuously on the targets that are in its focus, while the user interprets the movement of the prosthesis to adjust aiming. Experimental assessment was conducted in ten able-bodied participants to evaluate the feasibility and the impact of training on prosthesis-user interaction. The subjects used the system to grasp a set of objects individually (Phase I) and in cluttered scenarios (Phase II), while the time to accomplish the task (TAT) was used as the performance metric. In both phases, the TAT improved significantly across blocks. Some targets (objects and/or their parts) were more challenging, requiring thus significantly more time to handle, but all objects and scenes were successfully accomplished by all subjects. The assessment therefore demonstrated that the system is indeed robust and effective, and that the subjects could successfully learn how to aim with the system after a brief training. This is an important step toward the development of a self-contained semi-autonomous system convenient for clinical applications.

Design of a Novel Long-Reach Cable-Driven Hyper-Redundant Snake-like Manipulator for Inspection and Maintenance

Robotic inspection and maintenance are gaining importance due to the number of different scenarios in which robots can operate. The use of robotic systems to accomplish such tasks has deep implications in terms of safety for human workers and can significantly extend the life of infrastructures and industrial facilities. In this context, long-reach cable-driven hyper-redundant robots can be employed to inspect areas that are difficult to reach and hazardous environments such as tanks and vessels. This paper presents a novel long-reach cable-driven hyper-redundant robot called SLIM (Snake-Like manipulator for Inspection and Maintenance). SLIM consists of a robotic arm, a pan and tilt mechanism as end-effector, and an actuation box that can rotate and around which the arm can wrap. The robot has a total of 15 degrees of freedom and, therefore, for the task of positioning the tool centre point in a bi-dimensional Cartesian space with a specific attitude, it has 10 degrees of redundancy. The robot is designed to operate in harsh environments and high temperatures and can deploy itself up to about 4.8 m. This paper presents the requirements that drove the design of the robot, the main aspects of the mechanical and electronic systems, the control strategy, and the results of preliminary experimental tests performed with a physical prototype to evaluate the robot performances.

Designing Prosthetic Hands With Embodied Intelligence: The KIT Prosthetic Hands

Hand prostheses should provide functional replacements of lost hands. Yet current prosthetic hands often are not intuitive to control and easy to use by amputees. Commercially available prostheses are usually controlled based on EMG signals triggered by the user to perform grasping tasks. Such EMG-based control requires long training and depends heavily on the robustness of the EMG signals. Our goal is to develop prosthetic hands with semi-autonomous grasping abilities that lead to more intuitive control by the user. In this paper, we present the development of prosthetic hands that enable such abilities as first results toward this goal. The developed prostheses provide intelligent mechatronics including adaptive actuation, multi-modal sensing and on-board computing resources to enable autonomous and intuitive control. The hands are scalable in size and based on an underactuated mechanism which allows the adaptation of grasps to the shape of arbitrary objects. They integrate a multi-modal sensor system including a camera and in the newest version a distance sensor and IMU. A resource-aware embedded system for in-hand processing of sensory data and control is included in the palm of each hand. We describe the design of the new version of the hands, the female hand prosthesis with a weight of 377 g, a grasping force of 40.5 N and closing time of 0.73 s. We evaluate the mechatronics of the hand, its grasping abilities based on the YCB Gripper Assessment Protocol as well as a task-oriented protocol for assessing the hand performance in activities of daily living. Further, we exemplarily show the suitability of the multi-modal sensor system for sensory-based, semi-autonomous grasping in daily life activities. The evaluation demonstrates the merit of the hand concept, its sensor and in-hand computing systems.