Human Arm Movements Research Articles

A Novel Evaluation Framework for Teleoperation and a Case Study on Natural Human-Arm-Imitation Through Motion Capture

Although tele-operation has a long history, when it comes to tuning, comparison, and evaluation of tele-operation systems, no standard framework exists which can fulfill desiderata such as: concisely modeling multiple aspects of the system as a whole, i.e. timing, accuracy, and event transitions, while also providing for separation of user-, feedback-, as well as learning-dependent components. On the other hand, real-time remote tele-operation of robotic arms, either industrial or humanoid, is highly suitable for a number of applications, especially in difficult or inaccessible environment, and thus such an evaluation framework would be desirable. Usually, teleoperation is driven by buttons, joysticks, haptic controllers, or slave-arms, providing an interface which can be quite cumbersome and unnatural, especially when operating robots with multiple degrees of freedom. Thus, in thus paper, we present a two-fold contribution: (a) a task-based teleoperation evaluation framework which can achieve the desiderata described above, as well as (b) a system for teleoperation of an industrial arm commanded through human-arm motion capture, which is used as a case study, and also serves to illustrate the effectiveness of the evaluation framework that we are introducing. In our system the desired trajectory of a remote robotic arm is easily and naturally controlled through imitation of simple movements of the operator’s physical arm, obtained through motion capture. Furthermore, an extensive real-world evaluation is provided, based on our proposed probabilistic framework, which contains an inter-subject quantitative study with 23 subjects, a longitudinal study with 6 subjects, as well as opinions and attitudes towards tele-operation study. The results provided illustrate the strengths of the proposed evaluation framework—by enabling the quick production of multiple task-, user-, system-, as well as learning-centric results, as well as the benefits of our natural imitation-based approach towards teleoperation. Furthermore, an interesting ordering of preferences towards different potential application areas of teleoperation is indicated by our data. Finally, after illustrating their effectiveness, we discuss how both our evaluation framework as well as teleoperation system presented are not only applicable in a wide variety of teleoperation domains, but are also directly extensible in many beneficial ways.

A computational neuromusculoskeletal model of human arm movements

This work proposes a computational neuromusculoskeletal model of human arm movements. The model consists of three components: the supraspinal neural control system, the spinal motor system, and the muscle-tendon actuation system. In the supraspinal neural system model, the cerebellum is regarded as having feedforward control and the cerebrum as feedback control principally based on the feedback-error learning scheme. This computational model proposes that the feedforward control of the cerebellum may not need to be an explicit locus of an inverse dynamic model. This model also includes the modularly organized spinal motor system such that it simplifies controlling redundant muscular actuators. Cerebellar feedforward control and the spinal motor system are assumed to be adaptive. The two motor adaptations seem to synergistically promote motion flexibility and simplify the neural system structure. The neural control system is combined with the Hill-type muscle-tendon model to generate arm movement. The overall model proposes that an approximate inverse dynamic model may implicitly be constructed over the integrated neuromusculoskeletal system, and it is not necessary to be explicitly computed in a specific motor system. To cope with the human neural system, neuromuscular activation dynamics and neural transmission delays are included in the model. A computational simulation study using the model was implemented to verify the feasibility of the model. Center out reaching movements and learning of those movements as well s generations of figure eightlike movements were computationally tested. A plausible motor control scheme of movement is discussed using the model.

Finger Impedance Evaluation by Means of Hand Exoskeleton

Modulation of arm mechanical impedance is a fundamental aspect for interaction with the external environment and its regulation is essential for stability preservation during manipulation. Even though past research on human arm movements has suggested that models of human finger impedance would benefit the study of neural control mechanisms and the design of novel hand prostheses, relatively few studies have focused on finger and hand impedance. This article touches on the two main aspects of this research topic: first it introduces a mechanical refinement of a device that can be used to effectively measure finger impedance during manipulation tasks; then, it describes a pilot study aimed at identifying the inertia of the finger and the viscous and elastic properties of finger muscles. The proposed wearable exoskeleton, which has been designed to measure finger posture and impedance modulation while leaving the palm free, is capable of applying fast displacements while monitoring the interaction forces between the human finger and the robotic links. Moreover, due to the relatively small inertia of the fingers, it allows us to meet some stringent specifications, performing relatively large displacements (~45°) before the stretch reflex intervenes (~25 ms). The results of measurements on five subjects show that inertia, damping, and stiffness can be effectively identified and that the parameters obtained are comparable with values from previous studies.

Descending Corticospinal Control of Intersegmental Dynamics

To make an accurate movement, the CNS has to overcome the inherent complexities of the multijoint limb. For example, interaction torques arise when motion of individual arm segments propagates to adjacent segments causing their movement without any muscle contractions. Since these passive joint torques significantly add to the overall torques generated by active muscular contractions, they must be taken into account during planning or execution of goal-directed movements. We investigated the role of the corticospinal tract in compensating for the interaction torques during arm movements in humans. Twelve subjects reached to visual targets with their arm supported by a robotic exoskeleton. Reaching to one target was accompanied by interaction torques that assisted the movement, while reaching to the other target was accompanied by interaction torques that resisted the movement. Corticospinal excitability was assessed at different times during movement using single-pulse transcranial magnetic stimulation (TMS) over the upper-arm region of M1 (primary motor cortex). We found that TMS responses in shoulder monoarticular and elbow-shoulder biarticular muscles changed together with the interaction torques during movements in which the interaction torques were resistive. In contrast, TMS responses did not correlate with assistive interaction torques or with co-contraction. This suggests that the descending motor command includes compensation for passive limb dynamics. Furthermore, our results suggest that compensation for interaction torques involves the biarticular muscles, which span both shoulder and elbow joints and are in a biomechanically advantageous position to provide such compensation.

Study of Smooth and Accurate Position Controls of Pneumatic Artificial Muscle Actuators for Robotic Arms

This paper utilizes position errors, position error rates and payload variations as inner feedback loops for controlling the actuators of joint rotations in a pneumatic artificial muscle (PAM) driven robot. The result is an algorithm that is able to adaptively regulate input signals for the proportional pressure valve that controls the air flow to the PAM, in an attempt to better mimic the point-to-point movement of human arm. Further, the algorithm is able to utilize the angular position information from an outer feedback loop to activate/deactivate a solenoid valve for a rapid damping of the non-oscillatory motion and thereby, able to quickly and accurately bring the joints to a rest at a desired position. The proposed method appears to be effective in overcoming some of the inherent drawbacks that arise from the viscoelastic shell and air compressibility of the PAM element.

The Binding of Learning to Action in Motor Adaptation

In motor tasks, errors between planned and actual movements generally result in adaptive changes which reduce the occurrence of similar errors in the future. It has commonly been assumed that the motor adaptation arising from an error occurring on a particular movement is specifically associated with the motion that was planned. Here we show that this is not the case. Instead, we demonstrate the binding of the adaptation arising from an error on a particular trial to the motion experienced on that same trial. The formation of this association means that future movements planned to resemble the motion experienced on a given trial benefit maximally from the adaptation arising from it. This reflects the idea that actual rather than planned motions are assigned ‘credit’ for motor errors because, in a computational sense, the maximal adaptive response would be associated with the condition credited with the error. We studied this process by examining the patterns of generalization associated with motor adaptation to novel dynamic environments during reaching arm movements in humans. We found that these patterns consistently matched those predicted by adaptation associated with the actual rather than the planned motion, with maximal generalization observed where actual motions were clustered. We followed up these findings by showing that a novel training procedure designed to leverage this newfound understanding of the binding of learning to action, can improve adaptation rates by greater than 50%. Our results provide a mechanistic framework for understanding the effects of partial assistance and error augmentation during neurologic rehabilitation, and they suggest ways to optimize their use.

Estimating joint kinematics from skin motion observation: modelling and validation

Modelling of soft tissue motion is required in many areas, such as computer animation, surgical simulation, 3D motion analysis and gait analysis. In this paper, we will focus on the use of modelling of skin deformation during 3D motion analysis. The most frequently used method in 3D human motion analysis involves placing markers on the skin of the analysed segment which is composed of the rigid bone and the surrounding soft tissues. Skin and soft tissue deformations introduce a significant artefact which strongly influences the resulting bone position, orientation and joint kinematics. For this study, we used a statistical solid dynamics approach which is a combination of several previously reported tools: the point cluster technique (PCT) and a Kalman filter which was added to the PCT. The methods were tested and evaluated on controlled human-arm motions, using an optical motion capture system (ViconTM). The addition of a Kalman filter to the PCT for rigid body motion estimation results in a smoother signal that better represents the joint motion. Calculations indicate less signal distortion than when using a digital low-pass filter. Furthermore, adding a Kalman filter to the PCT substantially reduces the dispersion of the maximal and minimal instantaneous frequencies. For controlled human movements, the result indicated that adding a Kalman filter to the PCT produced a more accurate signal. However, it could not be concluded that the proposed Kalman filter is better than a low-pass filter for estimation of the motion. We suggest that implementation of a Kalman filter with a better biomechanical motion model will be more likely to improve the results.

A Wearable Sensing System for Tracking and Monitoring of Functional Arm Movement

This paper presents a new sensing system for home-based rehabilitation based on optical linear encoder (OLE), in which the motion of an optical encoder on a code strip is converted to the limb joints' goniometric data. A body sensing module was designed, integrating the OLE and an accelerometer. A sensor network of three sensing modules was established via controller area network bus to capture human arm motion. Experiments were carried out to compare the performance of the OLE module with that of commercial motion capture systems such as electrogoniometers and fiber-optic sensors. The results show that the inexpensive and simple-design OLE's performance is comparable to that of expensive systems. Moreover, a statistical study was conducted to confirm the repeatability and reliability of the sensing system. The OLE-based system has strong potential as an inexpensive tool for motion capture and arm-function evaluation for short-term as well as long-term home-based monitoring.

Riemannian geometric approach to human arm dynamics, movement optimization, and invariance

We present a generally covariant formulation of human arm dynamics and optimization principles in Riemannian configuration space. We extend the one-parameter family of mean-squared-derivative (MSD) cost functionals from Euclidean to Riemannian space, and we show that they are mathematically identical to the corresponding dynamic costs when formulated in a Riemannian space equipped with the kinetic energy metric. In particular, we derive the equivalence of the minimum-jerk and minimum-torque change models in this metric space. Solutions of the one-parameter family of MSD variational problems in Riemannian space are given by (reparameterized) geodesic paths, which correspond to movements with least muscular effort. Finally, movement invariants are derived from symmetries of the Riemannian manifold. We argue that the geometrical structure imposed on the arm's configuration space may provide insights into the emerging properties of the movements generated by the motor system.

Visual-haptic cue integration with spatial and temporal disparity during pointing movements

Many perceptual cue combination studies have shown that humans can integrate sensory information across modalities as well as within a modality in a manner that is close to optimal. While the limits of sensory cue integration have been extensively studied in the context of perceptual decision tasks, the evidence obtained in the context of motor decisions provides a less consistent picture. Here, we studied the combination of visual and haptic information in the context of human arm movement control. We implemented a pointing task in which human subjects pointed at an invisible unknown target position whose vertical position varied randomly across trials. In each trial, we presented a haptic and a visual cue that provided noisy information about the target position half-way through the reach. We measured pointing accuracy as function of haptic and visual cue onset and compared pointing performance to the predictions of a multisensory decision model. Our model accounts for pointing performance by computing the maximum a posteriori estimate, assuming minimum variance combination of uncertain sensory cues. Synchronicity of cue onset has previously been demonstrated to facilitate the integration of sensory information. We tested this in trials in which visual and haptic information was presented with temporal disparity. We found that for our sensorimotor task temporal disparity between visual and haptic cue had no effect. Sensorimotor learning appears to use all available information and to apply the same near-optimal rules for cue combination that are used by perception.

Odeling of Human Arm Energy Expenditure for Predicting Energy Optimal Trajectories

Human arm motion can inspire the trajectory planning of anthropomorphic robotic arms to achieve energyecient movements. An approach for predicting metabolic cost in the planar human arm motion by means of the biomechanical simulation is proposed in this work. Two biomechanical models, including an analytical model and a musculoskeletal model, are developed to implement the proposed approach. The analytical model is developed by modifying a human muscle expenditure model, in which the muscles are grouped as torque providers for computation eciency. In the musculoskeletal model, the predication of metabolic cost is conducted on the basis of individual muscles. With the proposed approach, metabolic costs for parameterized target-reaching arm motions are calculated and utilized to identify optimal arm trajectories.

2A1-F03 ミニチュアヒューマノイド用7自由度アームの開発(ウェアラブルロボティクス)

In our previous work, a wearable robot named telecommunicator has been developed. The telecommunicator can achieve rich communications with a remote person through a small robot. Until now, we developed several prototypes of the wearable telecommunicator T1, T2, T3 and MH-1. MH-1 has human like shape and ability to reproduce the movements of human's arm and head. Currently, we are developing MH-2 that expresses a presence of the operator by 20-DOF motions. In this paper, a 7-DOF arm of MH-2 with the parallel wire mechanism is developed for MH-2. The effectiveness of the mechanism is verified by the prototype model.

A Mimicking Human Arm with 5 DOF Controlled by LabVIEW

In this paper, a proposed design of 5 DOF arm robot is presented; each of the arm's joints will be generated by the corresponding human arm's joints . The paper will present a full design of a robot arm that mimics the human arm motion. Also, a graphical simulator will be designed to mimic the human arm simultaneously with the mechanical robot arm. Both of the arms are controlled using LabVIEW and data acquisition card NI-PCI 6024E. For control applications, LabVIEW Real-Time provide a single software development tool that can implement the functions to PC-based control systems, thus acting as a programmable automation controller. Index Terms—Control, Human-Machine Interface, LabVIEW, Data Acquisition, 5DOF, and Human Motion

A k-Nearest Neighbor Based Algorithm for Human Arm Movements Recognition Using EMG Signals

In a human-robot interface, the prediction of motion, which is based on context information of a task, has the potential to improve the robustness and reliability of motion classification to control human-assisting manipulators. The electromyography (EMG) signals can be used as a control source of artificial arm after it has been processed. The objective of this work is to achieve better classification with multiple parameters using K-Nearest Neighbor for different movements of a prosthetic arm. A K- Nearest Neighbor (K-NN) rule is one of the simplest and the most important methods in pattern recognition. The proposed structure is simulated using MATLAB Ver. R2009a, and satisfied results are obtained by comparing with conventional method of recognition using Artificial Neural Network(ANN), that explains the ability of the proposed structure to recognize the movements of human arm based EMG signals. Results show the proposed technique achieved a uniformly good performance with respect to ANN in term of time which is important in recognition systems, better accuracy in recognition when applied to lower SNR signal.

A k-Nearest Neighbor Based Algorithm for Human Arm Movements Recognition Using EMG Signals

In a human–robot interface, the prediction of motion, which is based on context information of a task, has the potential to improve the robustness and reliability of motion classification to control human-assisting manipulators. The electromyography (EMG) signals can be used as a control source of artificial arm after it has been processed. The objective of this work is to achieve better classification with multiple parameters using KNearest Neighbor for different movements of a prosthetic arm. A K- Nearest Neighbor (K-NN) rule is one of the simplest and the most important methods in pattern recognition. The proposed structure is simulated using MATLAB Ver. R2009a, and satisfied results are obtained by comparing with conventional method of recognition using Artificial Neural Network(ANN), that explains the ability of the proposed structure to recognize the movements of human arm based EMG signals. Results show the proposed technique achieved a uniformly good performance with respect to ANN in term of time which is important in recognition systems, better accuracy in recognition when applied to lower SNR signal .

A method for estimating three-dimensional human arm movement with two electromagnetic sensors

Measurement of upper-limb movements is important in various domains. In this article, an upper-limb three-dimensional movement recording technique is proposed based on only two electromagnetic sensors. Two joints are considered with a total of seven degrees of freedom (DoF; three translations and four rotations). The chosen sequence of joint rotations is compliant with ISB recommendations: the shoulder is modelled with a ball and socket joint with three DoF and the elbow with a one DoF revolute joint. This article is focused on the procedure used to calibrate and sense the upper-limb movements from the raw data coming from the flock of birds sensors. The principle of the method is to define the centre of the wrist, elbow and shoulder joints in the frame of the adequate sensor. This operation is done by performing calibration gestures. Results are proposed and commented.

Passerine reactions to human behaviour and vegetation structure in Peterborough, Canada

Different bird species react differently to human proximity in urban parks. The ‘alert distance’, i.e. the distance between a bird and an approaching person when the bird reacts visibly to the intruder, and the flight distance, the distance between the two when the bird takes flight, vary for different bird species. Such alert distances are less studied for passerines. This paper examines the alert and flight distances of ten variably sized passerine species in Peterborough, Ontario, Canada. Subject birds were approached by an observer walking at a constant speed, using frontal and sideways approaches and either immobile or swinging arms, to test the hypothesis that alert distances vary according to human movements, bird size, and surrounding vegetation. All species recorded longer alert distances when the approaching person used a frontal, rather than a sideways approach, and also recorded longer alert and flight distances when the person used arm movements, as against walking with no arm movements. Apart from human arm movements, the strongest positive predictor of alert distances was bird size, while significant negative predictors were tree and shrub height and vegetation cover. Contrary to some other studies, increased vegetation height and cover more often reduced than increased alert distances, and open grass did not increase alert distances. Path and road proximity was not a significant predictor of alert distance. If sensitive to such issues, park design may include more trees and shrubbery, balancing conservation with human recreation and security concerns.

Wearable wireless sensing system for capturing human arm motion

Having the capability to track human body kinematics allows clinicians to classify and analyze a stroke patient's progress which aid in the rehabilitation program. Monitoring and capturing real time body motion further permits corrective measures to be implemented for more effective rehabilitation results. In this current work, an innovative and unobtrusive wearable sensing system is being proposed. This compact and cost effective mocap approach effectively captures human joint angles and does that hinder limb motion as commonly encountered in other existing systems. It is easily worn by simply attaching the plaster module onto the limb joints to be monitored. The paper details the design and implementation of the proposed sensor and sensing methodology. The tested wireless optical sensor is able to detect the posture and movement of the human arm with particular attention to its application in post stroke rehabilitation for the healthcare service. Real time experimental data are collected from a subject using a hand exerciser and compared with a commercial motion capture system. The results demonstrate the feasibility and viability of the proposed sensing system in tracking human arm postures and movement.

Target switching in curved human arm movements is predicted by changing a single control parameter

Straight-line movements have been studied extensively in the human motor-control literature, but little is known about how to generate curved movements and how to adjust them in a dynamic environment. The present work studied, for the first time to my knowledge, how humans adjust curved hand movements to a target that switches location. Subjects (n = 8) sat in front of a drawing tablet and looked at a screen. They moved a cursor on a curved trajectory (spiral or oval shaped) toward a goal point. In half of the trials, this goal switched 200 ms after movement onset to either one of two alternative positions, and subjects smoothly adjusted their movements to the new goal. To explain this adjustment, we compared three computational models: a superposition of curved and minimum-jerk movements (Flash and Henis in J Cogn Neurosci 3(3):220-230, 1991), Vector Planning (Gordon et al. in Exp Brain Res 99(1):97-111, 1994) adapted to curved movements (Rescale), and a nonlinear dynamical system, which could generate arbitrarily curved smooth movements and had a point attractor at the goal. For each model, we predicted the trajectory adjustment to the target switch by changing only the goal position in the model. As result, the dynamical model could explain the observed switch behavior significantly better than the two alternative models (spiral: P = 0.0002 vs. Flash, P = 0.002 vs. Rescale; oval: P = 0.04 vs. Flash; P values obtained from Wilcoxon test on R (2) values). We conclude that generalizing arbitrary hand trajectories to new targets may be explained by switching a single control command, without the need to re-plan or re-optimize the whole movement or superimpose movements.

16. Function analysis of spinal interneuron reveals use-dependent differences relating to normal human arm movements

16. Function analysis of spinal interneuron reveals use-dependent differences relating to normal human arm movements