Multi-joint Arm Research Articles

Human arm redundancy: a new approach for the inverse kinematics problem.

The inverse kinematics (IK) problem addresses how both humans and robotic systems coordinate movement to resolve redundancy, as in the case of arm reaching where more degrees of freedom are available at the joint versus hand level. This work focuses on which coordinate frames best represent human movements, enabling the motor system to solve the IK problem in the presence of kinematic redundancies. We used a multi-dimensional sparse source separation method to derive sets of basis (or source) functions for both the task and joint spaces, with joint space represented by either absolute or anatomical joint angles. We assessed the similarities between joint and task sources in each of these joint representations, finding that the time-dependent profiles of the absolute reference frame's sources show greater similarity to corresponding sources in the task space. This result was found to be statistically significant. Our analysis suggests that the nervous system represents multi-joint arm movements using a limited number of basis functions, allowing for simple transformations between task and joint spaces. Additionally, joint space seems to be represented in an absolute reference frame to simplify the IK transformations, given redundancies. Further studies will assess this finding's generalizability and implications for neural control of movement.

Few sex-specific effects of fatigue on muscle synergies in a repetitive pointing task

Previous studies have identified some sex differences in how individual muscles change their activation during repetitive multi-joint arm motion-induced fatigue. However, little is known about how indicators of multi-muscle coordination change with fatigue in males and females. Fifty-six (29 females) asymptomatic young adults performed a repetitive, forward-backward pointing task until scoring 8/10 on a Borg CR10 scale while surface electromyographic activity of upper trapezius, anterior deltoid, biceps brachii, and triceps brachii was recorded. Activation coefficient, synergy structure, and relative weight of each muscle within synergies were calculated using the non-negative matrix factorization method. Two muscle synergies were extracted from the fatiguing task. The synergy structures were mostly preserved after fatigue, while the activation coefficients were altered. A significant Sex × Fatigue interaction effect showed more use of the anterior deltoid in males especially before fatigue in synergy 1 during shoulder stabilization (p = 0.04). As for synergy 2, it was characterized by variations in the relative weight of biceps, which was higher by 16 % in females compared to males (p = 0.04), and increased with fatigue (p = 0.03) during the elbow flexion acceleration phase and the deceleration phase of the backward pointing movement. Findings suggest that both sexes adapted to fatigue similarly, using fixed synergy structures, with alterations in synergy activation patterns and relative weights of individual muscles. Results support previous findings of an important role for the biceps and anterior deltoid in explaining sex differences in patterns of repetitive motion-induced upper limb fatigue.

Adaptive Hybrid Optimization Learning-Based Accurate Motion Planning of Multi-Joint Arm.

Motion planning is important to the automatic operation of the manipulator. It is difficult for traditional motion planning algorithms to achieve efficient online motion planning in a rapidly changing environment and high-dimensional planning space. The neural motion planning (NMP) algorithm based on reinforcement learning provides a new way to solve the above-mentioned task. Aiming to overcome the difficulty of training the neural network in high-accuracy planning tasks, this article proposes to combine the artificial potential field (APF) method and reinforcement learning. The neural motion planner can avoid obstacles in a wide range; meanwhile, the APF method is exploited to adjust the partial position. Considering that the action space of the manipulator is high-dimensional and continuous, the soft-actor-critic (SAC) algorithm is adopted to train the neural motion planner. By training and testing with different accuracy values in a simulation engine, it is verified that, in the high-accuracy planning tasks, the success rate of the proposed hybrid method is better than using the two algorithms alone. Finally, the feasibility of directly transferring the learned neural network to the real manipulator is verified by a dynamic obstacle-avoidance task.

Learning to manipulate a whip with simple primitive actions – A simulation study

This simulation study investigated whether a 4-degrees-of-freedom (DOF) arm could strike a target with a 50-DOF whip using a motion profile similar to discrete human movements. The interactive dynamics of the multi-joint arm was modeled as a constant joint-space mechanical impedance, with values derived from experimental measurement. Targets at various locations could be hit with a single maximally smooth motion in joint-space coordinates. The arm movements that hit the targets were identified with fewer than 250 iterations. The optimal actions were essentially planar arm motions in extrinsic task-space coordinates, predominantly oriented along the most compliant direction of both task-space and joint-space mechanical impedances. Of the optimal movement parameters, striking a target was most sensitive to movement duration. This result suggests that the elementary actions observed in human motor behavior may support efficient motor control in interaction with a dynamically complex object.

A Comparison of Spinal Robotic Systems and Pedicle Screw Accuracy Rates: Review of Literature and Meta-Analysis.

Introduction The motivation to improve accuracy and reduce complication rates in spinal surgery has driven great advancements in robotic surgical systems, with the primary difference between the newer generation and older generation models being the presence of an optical camera and multijointed arm. This study compares accuracy and complication rates of pedicle screw placement in older versus newer generation robotic systems reported in the literature. Methods We performed a systemic review and meta-analysis describing outcomes of pedicle screw placement with robotic spine surgery. We assessed the robustness of these findings by quantifying levels of cross-study heterogeneity and publication bias. Finally, we performed meta-regression to test for associations between pedicle screw accuracy and older versus newer generation robotic spine system usage. Results Average pedicle screw placement accuracy rates for old and new generation robotic platforms were 97 and 99%, respectively. Use of new generation robots was significantly associated with improved pedicle screw placement accuracy ( p = 0.03). Conclusion Accuracy of pedicle screw placement was high across all generations of robotic surgical systems. However, newer generation robots were shown to be significantly associated with accurate pedicle screw placement, showing the benefits of upgrading robotic systems with a real-time optical camera and multijointed arm.

Restoration of complex movement in the paralyzed upper limb

Objective. Functional electrical stimulation (FES) involves artificial activation of skeletal muscles to reinstate motor function in paralyzed individuals. While FES applied to the upper limb has improved the ability of tetraplegics to perform activities of daily living, there are key shortcomings impeding its widespread use. One major limitation is that the range of motor behaviors that can be generated is restricted to a small set of simple, preprogrammed movements. This limitation stems from the substantial difficulty in determining the patterns of stimulation across many muscles required to produce more complex movements. Therefore, the objective of this study was to use machine learning to flexibly identify patterns of muscle stimulation needed to evoke a wide array of multi-joint arm movements. Approach. Arm kinematics and electromyographic (EMG) activity from 29 muscles were recorded while a ‘trainer’ monkey made an extensive range of arm movements. Those data were used to train an artificial neural network that predicted patterns of muscle activity associated with a new set of movements. Those patterns were converted into trains of stimulus pulses that were delivered to upper limb muscles in two other temporarily paralyzed monkeys. Main results. Machine-learning based prediction of EMG was good for within-subject predictions but appreciably poorer for across-subject predictions. Evoked responses matched the desired movements with good fidelity only in some cases. Means to mitigate errors associated with FES-evoked movements are discussed. Significance. Because the range of movements that can be produced with our approach is virtually unlimited, this system could greatly expand the repertoire of movements available to individuals with high level paralysis.

Robot-assisted Total Mesorectal Excision and Lateral Pelvic Lymph Node Dissection for Locally Advanced Middle-low Rectal Cancer.

Since their approval for clinical use, da Vinci surgical robots have shown great advantages in gastrointestinal surgical operations, especially in complex procedures. The high-quality 3-D visual, multijoint arm and natural tremor filtration allow the surgeon to expose and dissect more accurately with minimal invasion. Total mesorectal excision is the standard surgical technique for the treatment of resectable rectal cancer. To reduce the lateral recurrence rate, lateral pelvic lymph node dissection can be performed, as it is a safe and feasible procedure for locally advanced middle-low rectal cancer with a high possibility of metastasis to the lateral lymph nodes. However, the complexity of the anatomic structures and the high postoperative complication rate limit its application. Recently, several surgeons have increasingly used robotic techniques for total mesorectal excision and lateral pelvic lymph node dissection. Compared with open and laparoscopic surgery, the robotic technique has several advantages, such as less blood loss, fewer blood transfusions, minimal trauma, shorter postoperative hospitalization, and quicker recovery. A robotic approach is generally regarded as a reasonable alternative for complicated procedures such as lateral pelvic lymph node dissection, although there are a limited number of high-quality prospective randomized controlled studies reporting direct evidence. Here, we provide the detailed steps of robot-assisted total mesorectal excision and lateral pelvic lymph node dissection performed at the First Affiliated Hospital of Xi'an Jiaotong University.

Nonlinear Intelligent Control of Two Link Robot Arm by Considering Human Voluntary Components.

This paper proposes a nonlinear intelligent control of a two link robot arm by considering human voluntary components. In general, human arm viscoelastic properties are regulated in different manners according to various task requirements. The viscoelasticity consists of joint stiffness and viscosity. The research of the viscoelasticity can improve the development of industrial robots, rehabilitation and sports etc. So far, some results have been shown using filtered human arm viscoelasticity measurements. That is, human motor command is removed. As a result, the dynamics of human voluntary component during movements is omitted. In this paper, based on the feedforward characteristics of human multi joint arm, a model is obtained by considering human voluntary components using a support vector regression technique. By employing the learned model, a nonlinear intelligent control of two link robot arm is proposed. Experimental results confirm the effectiveness of this proposal.

Clinical validation of kinematic assessments of post-stroke upper limb movements with a multi-joint arm exoskeleton

BackgroundThe clinical evaluation of the upper limb of severely impaired stroke patient is challenging. Sensor-based assessments may allow for an objective evaluation of this patient population. This study investigated the validity of a device-assisted approach in comparison to the clinical outcome that it is supposed to reflect.MethodsIn nineteen severely impaired chronic stroke patients, we applied a gravity-compensating, multi-joint arm exoskeleton (Armeo Spring) and compared this sensor-based assessment with the clinical outcome measure Upper Extremity Fugl-Meyer Assessment (UE-FMA) scale. Specifically, we assessed separately and subsequently the range of motion in joint space for four single joints (i.e., wrist, elbow and shoulder flexion/extension (FE), and shoulder internal/external rotation (IER)), and the closing and opening of the hand with a pressure sensor placed in the handle.ResultsWithin the kinematic parameters, a strong correlation was observed between wrist and elbow FE (r > 0.7, p < 0.003; Bonferroni corrected). The UE-FMA was significantly predicted by a multiple regression model (F (5, 13) = 12.22, p < 0.0005, adj. R2 = 0.83). Both shoulder IER and grip pressure added significantly (p < 0.05) to the prediction with the standardized coefficients β of 0.55 and 0.38, respectively.ConclusionsBy applying an exoskeleton-based self-contained evaluation of single-joint movements, a clinically valid assessment of the upper limb range of motion in severely impaired stroke patients is feasible. Shoulder IER contributed most relevantly to the prediction of the clinical status. These findings need to be confirmed in a large, independent patient cohort.

Three-Dimensional Assessment of Upper Limb Proprioception via a Wearable Exoskeleton

Proprioception—the sense of body segment’s position and movement—plays a crucial role in human motor control, integrating the sensory information necessary for the correct execution of daily life activities. Despite scientific evidence recognizes that several neurological diseases hamper proprioceptive encoding with consequent inability to correctly perform movements, proprioceptive assessment in clinical settings is still limited to standard scales. Literature on physiology of upper limb’s proprioception is mainly focused on experimental approaches involving planar setups, while the present work provides a novel paradigm for assessing proprioception during single—and multi-joint matching tasks in a three-dimensional workspace. To such extent, a six-degrees of freedom exoskeleton, ALEx-RS (Arm Light Exoskeleton Rehab Station), was used to evaluate 18 healthy subjects’ abilities in matching proprioceptive targets during combined single and multi-joint arm’s movements: shoulder abduction/adduction, shoulder flexion/extension, and elbow flexion/extension. Results provided evidence that proprioceptive abilities depend on the number of joints simultaneously involved in the task and on their anatomical location, since muscle spindles work along their preferred direction, modulating the streaming of sensory information accordingly. These findings suggest solutions for clinical sensorimotor evaluation after neurological disease, where assessing proprioceptive deficits can improve the recovery path and complement the rehabilitation outcomes.

An optimal property of the hyperplane system in a finite cubing

Motivated by navigation and control problems in robotics, Ghrist and Peterson introduced a class of non-positively curved (NPC) cubical complexes arising as configuration spaces of reconfigurable systems, best regarded as discretized state space representations of embodied agents such as a multi-jointed robotic arm. In current real world applications, agents are increasingly required to respond autonomously to sensory input in order for them to contend with a priori unknown obstacles to navigation. In particular, the configuration spaces in question may not be known in advance. This motivates the following problem formulation: Given a NPC cubical complex $$\mathcal {C}$$ and a point-separating collection $$\varSigma $$ of Boolean queries on its 0-skeleton, $$\mathcal {C}^{(0)}$$ , find an efficient algorithm for learning $$\mathcal {C}$$ from the outputs provided by $$\varSigma $$ along an appropriately chosen path in $$\mathcal {C}$$ . In this note, we tackle the problem of identifying $$\mathcal {C}$$ when it is known that $$\mathcal {C}$$ is CAT(0). We show that the collection of canonical hyperplanes of $$\mathcal {C}$$ is the unique solution of a sub-modular minmax problem over the space of point-separating systems of Boolean queries on $$\mathcal {C}^{(0)}$$ , which may also be formulated in terms of the quadratic form associated with the graph Laplacian of $$\mathcal {C}^{(1)}$$ .

A self-aligning end-effector robot for individual joint training of the human arm.

AimIntense training of arm movements using robotic devices can help reduce impairments in stroke. Recent evidence indicates that independent training of individual joints of the arm with robots can be as effective as coordinated multi-joint arm training. This makes a case for designing and developing robots made for training individual joints, which can be simpler and more compact than the ones for coordinate multi-joint arm training. The design of such a robot is the aim of the work presented in this paper.MethodsAn end-effector robot kinematic design was developed and the optimal robot link lengths were estimated using an optimization procedure. A simple algorithm for automatically detecting human limb parameters is proposed and its performance was evaluated through a simulation study.ResultsA six-degrees-of-freedom end-effector robot with three actuated degrees-of-freedom and three non-actuated self-aligning degrees-of-freedom for safe assisted training of the individual joints (shoulder or elbow) of the human arm was conceived. The proposed robot has relaxed constraints on the relative positioning of the human limb with respect to the robot. The optimized link lengths chosen for the robot allow it to cover about 80% of the human limb’s workspace, and possess good overall manipulability. The simple estimation procedure was demonstrated to estimate human limb parameters with low bias and variance.DiscussionThe proposed robot with three actuated and three non-actuated degrees-of-freedom has a compact structure suitable for both the left and right arms without any change to its structure. The proposed automatic estimation procedure allows the robot to safely apply forces and impose movements to the human limb, without the need for any manual measurements. Such compact robots have the highest potential for clinical translation

Repetitive Control for Multi-Joint Arm Movements Based on Virtual Trajectories.

According to the neuromuscular model of virtual trajectory control, the postures and movements of limbs are performed by shifting the equilibrium positions determined by agonist and antagonist muscle activities. In this study, we develop virtual trajectory control for the reaching movements of a multi-joint arm, introducing a proportional-derivative feedback control scheme. In virtual trajectory control, it is crucial to design a suitable virtual trajectory such that the desired trajectory can be realized. To this end, we propose an algorithm for updating virtual trajectories in repetitive control, which can be regarded as a Newton-like method in a function space. In our repetitive control, the virtual trajectory is corrected without explicit calculation of the arm dynamics, and the actual trajectory converges to the desired trajectory. Using computer simulations, we assessed the proposed repetitive control for the trajectory tracking of a two-link arm. Our results confirmed that when the feedback gains were reasonably high and the sampling time was sufficiently small, the virtual trajectory was adequately updated, and the desired trajectory was almost achieved within approximately 10 iterative trials. We also propose a method for modifying the virtual trajectory to ensure that the formation of the actual trajectory is identical even when the feedback gains are changed. This modification method makes it possible to execute flexible control, in which the feedback gains are effectively altered according to motion tasks.

Wireless recording from unrestrained monkeys reveals motor goal encoding beyond immediate reach in frontoparietal cortex.

System neuroscience of motor cognition regarding the space beyond immediate reach mandates free, yet experimentally controlled movements. We present an experimental environment (Reach Cage) and a versatile visuo-haptic interaction system (MaCaQuE) for investigating goal-directed whole-body movements of unrestrained monkeys. Two rhesus monkeys conducted instructed walk-and-reach movements towards targets flexibly positioned in the cage. We tracked 3D multi-joint arm and head movements using markerless motion capture. Movements show small trial-to-trial variability despite being unrestrained. We wirelessly recorded 192 broad-band neural signals from three cortical sensorimotor areas simultaneously. Single unit activity is selective for different reach and walk-and-reach movements. Walk-and-reach targets could be decoded from premotor and parietal but not motor cortical activity during movement planning. The Reach Cage allows systems-level sensorimotor neuroscience studies with full-body movements in a configurable 3D spatial setting with unrestrained monkeys. We conclude that the primate frontoparietal network encodes reach goals beyond immediate reach during movement planning.

Operator-Based Robust Nonlinear Control Analysis and Design for a Bio-Inspired Robot Arm with Measurement Uncertainties

In this paper, a robust nonlinear tracking control design for a bio-inspired robot arm with human-like motion mechanism is investigated, and the bio-inspired operator controller based on human multi-joint viscoelastic properties is designed by using operator-based robust right coprime factorization approach. The motion mechanism of human multi-joint arm is used, and the measurement uncertainties of human multi-joint arm viscoelasticity are considered in designing bio-inspired operator controller. Based on the proposed design scheme, the sufficient conditions for the robust stability are derived in considering the coupling effects and measurement uncertainties, and the output tracking performance is realized. The effectiveness of the proposed design scheme was confirmed by the simulation results based on experimental data, and the time-varying estimated experimental data of human multi-joint arm viscoelasticity is used in simulation.

Estimation of Involuntary Impedance in Multi-joint Arm Movements

Stability analysis in physical human-robot interaction requires consideration of human feedback behavior. In unpredictable scenarios, where voluntary cognitive feedback is too slow to guarantee desired task execution, the central nervous system relies on intrinsic and involuntary reflexive feedback. In this work, we present a method for the estimation of the combined effects of intrinsic and involuntary reflexive feedback in multi-joint arm movements, termed involuntary impedance. We apply external force perturbations that are specifically designed to evoke feedback jerk, which can be isolated by application of a high pass filter, and limit the duration of the estimation interval to guarantee the exclusion of voluntary feedback. The isolation of the feedback behavior is validated in simulation and the estimation of the involuntary impedance components is evaluated in an experiment with human participants.

Improving Hand Function of Severely Impaired Chronic Hemiparetic Stroke Individuals Using Task-Specific Training With the ReIn-Hand System: A Case Series.

Purpose: In this study, we explored whether improved hand function is possible in poststroke chronic hemiparetic individuals with severe upper limb motor impairments when they participate in device-aided task-specific practice.Subjects: Eight participants suffering from chronic stroke (>1-year poststroke, mean: 11.2 years) with severely impaired upper extremity movement (Upper Extremity Subscale of the Fugl-Meyer Motor Assessment (UEFMA) score between 10 and 24) participated in this study.Methods: Subjects were recruited to participate in a 20-session intervention (3 sessions/7 weeks). During each session, participants performed 20–30 trials of reaching, grasping, retrieving, and releasing a jar with the assistance of a novel electromyography-driven functional electrical stimulation (EMG-FES) system.This EMG-FES system allows for Reliable and Intuitive use of the Hand (called ReIn-Hand device) during multi-joint arm movements. Pre-, post-, and 3-month follow-up outcome assessments included the UEFMA, Cherokee McMaster Stroke Assessment, grip dynamometry, Box and Blocks Test (BBT), goniometric assessment of active and passive ranges of motion (ROMs) of the wrist and the metacarpophalangeal flexion and extension (II, V fingers), Nottingham Sensory Assessment–Stereognosis portion (NSA), and Cutaneous Sensory Touch Threshold Assessment.Results: A nonparametric Friedman test of differences found significant changes in the BBT scores (χ2 = 10.38, p < 0.05), the passive and active ROMs (χ2 = 11.31, p < 0.05 and χ2 = 12.45, p < 0.01, respectively), and the NSA scores (χ2 = 6.42, p < 0.05) following a multi-session intervention using the ReIn-Hand device.Conclusions: These results suggest that using the ReIn-Hand device during reaching and grasping activities may contribute to improvements in gross motor function and sensation (stereognosis) in individuals with chronic severe UE motor impairment following stroke.

Robot Imitation Learning of Social Gestures with Self-Collision Avoidance Using a 3D Sensor.

To effectively interact with people, social robots need to perceive human behaviors and in turn display their own behaviors using social communication modes such as gestures. The modeling of gestures can be difficult due to the high dimensionality of the robot configuration space. Imitation learning can be used to teach a robot to implement multi-jointed arm gestures by directly observing a human teacher’s arm movements (for example, using a non-contact 3D sensor) and then mapping these movements onto the robot arms. In this paper, we present a novel imitation learning system with robot self-collision awareness and avoidance. The proposed method uses a kinematical approach with bounding volumes to detect and avoid collisions with the robot itself while performing gesticulations. We conducted experiments with a dual arm social robot and a 3D sensor to determine the effectiveness of our imitation system in being able to mimic gestures while avoiding self-collisions.

Design, Kinematics, and Control of a Multijoint Soft Inflatable Arm for Human-Safe Interaction

In this paper, a novel soft inflatable arm is proposed for telepresence robots. The new proposed structure of the arm is achieved by a very common and low-cost inflatable material and it is very light, weighing only about 50 g. However, it can realize agile movement by driving six tiny cables installed in the shoulder and elbow joints. The soft inflatable arm can work by pumping air at a very low pressure ( $7.32 \pm 3.45$ kPa) and allows direct and soft human contact without any external force sensors. This paper proposes joint compressed models, joint kinematic models, and kinematic models for the whole inflatable robot arm. These models can be easily applied to multijoint arms. In addition, a new redundancy resolution method is also developed for the inflatable arm, which makes it easier to control resolution and is less complex than other traditional approaches. Numerous experiments have been conducted, including performances of accuracy, repeatability, motion trajectories, human-safe interaction, and remote interaction. Results are satisfactory and validate the expected performance of the proposed robotic arm.

Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration

SUMMARYBackgroundPeople with chronic tetraplegia due to high cervical spinal cord injury (SCI) can regain limb movements through coordinated electrical stimulation of peripheral muscles and nerves, known as Functional Electrical Stimulation (FES). Users typically command FES systems through other preserved, but limited and unrelated, volitional movements (e.g. facial muscle activity, head movements). We demonstrate an individual with traumatic high cervical SCI performing coordinated reaching and grasping movements using his own paralyzed arm and hand, reanimated through FES, and commanded using his own cortical signals through an intracortical brain-computer-interface (iBCI).MethodsThe study participant (53 years old, C4, ASIA A) received two intracortical microelectrode arrays in the hand area of motor cortex, and 36 percutaneous electrodes for electrically stimulating hand, elbow, and shoulder muscles. The participant used a motorized mobile arm support for gravitational assistance and to provide humeral ab/adduction under cortical control. We assessed the participant’s ability to cortically command his paralyzed arm to perform simple single-joint arm/hand movements and functionally meaningful multi-joint movements. We compared iBCI control of his paralyzed arm to that of a virtual 3D arm. This study is registered with ClinicalTrials.gov, NCT00912041.FindingsThe participant successfully cortically commanded single-joint and coordinated multi-joint arm movements for point-to-point target acquisitions (80% – 100% accuracy) using first a virtual arm, and second his own arm animated by FES. Using his paralyzed arm, the participant volitionally performed self-paced reaches to drink a mug of coffee (successfully completing 11 of 12 attempts within a single session) and feed himself.InterpretationThis is the first demonstration of a combined FES+iBCI neuroprosthesis for both reaching and grasping for people with SCI resulting in chronic tetraplegia, and represents a major advance, with a clear translational path, for clinically viable neuroprostheses for restoring reaching and grasping post-paralysis.