Arm Joint Angles Research Articles

Evaluating Joint Angle Data for Clinical Assessment Using Multidimensional Inverse Kinematics with Average Segment Morphometry.

Movement analysis is a critical tool in understanding and addressing various disabilities associated with movement deficits. By analyzing movement patterns, healthcare professionals can identify the root causes of these alterations, which is essential for preventing, diagnosing, and rehabilitating a broad spectrum of medical conditions, disabilities, and injuries. With the advent of affordable motion capture technologies, quantitative data on patient movement is more accessible to clinicians, enhancing the quality of care. Nonetheless, it is crucial that these technologies undergo rigorous validation to ensure their accuracy in collecting and monitoring patient movements, particularly for remote healthcare services where direct patient observation is not possible. In this study, motion capture technology was used to track upper extremity movements during a reaching task presented in virtual reality. Kinematic data was then calculated for each participant using a scaled dynamic inertial model. The goal was to evaluate the accuracy of joint angle calculations using inverse kinematics from motion capture relative to the typical movement redundancy. Shoulder, elbow, radioulnar, and wrist joint angles were calculated with models scaled using either direct measurements of each individual's arm segment lengths or those lengths were calculated from individual height using published average proportions. The errors in joint angle trajectories calculated using the two methods of model scaling were compared to the inter-trial variability of those trajectories. The variance of this error was primarily within the normal range of variability between repetitions of the same movements. This suggests that arm joint angles can be inferred with good enough accuracy from motion capture data and individual height to be useful for the clinical assessment of motor deficits.

Distributed sensing along fibers for smart clothing.

Textile sensors transform our everyday clothing into a means to track movement and biosignals in a completely unobtrusive way. One major hindrance to the adoption of "smart" clothing is the difficulty encountered with connections and space when scaling up the number of sensors. There is a lack of research addressing a key limitation in wearable electronics: Connections between rigid and textile elements are often unreliable, and they require interfacing sensors in a way incompatible with textile mass production methods. We introduce a prototype garment, compact readout circuit, and algorithm to measure localized strain along multiple regions of a fiber. We use a helical auxetic yarn sensor with tunable sensitivity along its length to selectively respond to strain signals. We demonstrate distributed sensing in clothing, monitoring arm joint angles from a single continuous fiber. Compared to optical motion capture, we achieve around five degrees error in reconstructing shoulder, elbow, and wrist joint angles.

Abnormal coordination of upper extremity during target reaching in persons post stroke

Understanding abnormal synergy of the upper extremity (UE) in stroke survivors is critical for better identification of motor impairment. Here, we investigated to what extent stroke survivors retain the ability to coordinate multiple joints of the arm during a reaching task. Using an exoskeleton robot, 37 stroke survivors’ arm joint angles (θ) and torques (τ) during hand reaching in the horizontal plane was compared to that of 13 healthy controls. Kinematic and kinetic coordination patterns were quantified as variances of the multiple-joint angles and multiple-joint torques across trials, respectively, that were partitioned into task-irrelevant variance (TIVθ and TIVτ) and task-relevant variance (TRVθ and TRVτ). TIVθ and TRVθ (or TIVτ and TRVτ) led to consistent and inconsistent hand position (or force), respectively. The index of synergy (ISθ and ISτ) was determined as {IS}_{theta }=({TIV}_{theta }-{TRV}_{theta })/({TIV}_{theta }+{TRV}_{theta }) and {IS}_{tau }=({TIV}_{tau }-{TRV}_{tau })/({TIV}_{tau }+{TRV}_{tau }) for kinematic and kinetic coordination patterns, respectively. Both kinematic ISθ and kinetic ISτ in the stroke group were significantly lower than that of the control group, indicating stroke survivors had impaired reaching abilities in utilizing the multiple joints of the UE for successful completion of a reaching task. The reduction of kinematic ISθ in the stroke group was mainly attributed to the lower TIVθ as compared to the control group, while the reduction of kinetic ISτ was mainly due to the higher {TRV}_{tau } as well as lower TIVτ. Our results also indicated that stroke may lead to motor deficits in formation of abnormal kinetic synergistic movement of UE, especially during outward movement. The findings in abnormal synergy patterns provides a better understanding of motor impairment, suggesting that impairment-specific treatment could be identified to help improve UE synergies, focusing on outward movements.

Utility and Usability of Two Forms of Supplemental Vibrotactile Kinesthetic Feedback for Enhancing Movement Accuracy and Efficiency in Goal-Directed Reaching.

Recent advances in wearable sensors and computing have made possible the development of novel sensory augmentation technologies that promise to enhance human motor performance and quality of life in a wide range of applications. We compared the objective utility and subjective user experience for two biologically inspired ways to encode movement-related information into supplemental feedback for the real-time control of goal-directed reaching in healthy, neurologically intact adults. One encoding scheme mimicked visual feedback encoding by converting real-time hand position in a Cartesian frame of reference into supplemental kinesthetic feedback provided by a vibrotactile display attached to the non-moving arm and hand. The other approach mimicked proprioceptive encoding by providing real-time arm joint angle information via the vibrotactile display. We found that both encoding schemes had objective utility in that after a brief training period, both forms of supplemental feedback promoted improved reach accuracy in the absence of concurrent visual feedback over performance levels achieved using proprioception alone. Cartesian encoding promoted greater reductions in target capture errors in the absence of visual feedback (Cartesian: 59% improvement; Joint Angle: 21% improvement). Accuracy gains promoted by both encoding schemes came at a cost in terms of temporal efficiency; target capture times were considerably longer (1.5 s longer) when reaching with supplemental kinesthetic feedback than without. Furthermore, neither encoding scheme yielded movements that were particularly smooth, although movements made with joint angle encoding were smoother than movements with Cartesian encoding. Participant responses on user experience surveys indicate that both encoding schemes were motivating and that both yielded passable user satisfaction scores. However, only Cartesian endpoint encoding was found to have passable usability; participants felt more competent using Cartesian encoding than joint angle encoding. These results are expected to inform future efforts to develop wearable technology to enhance the accuracy and efficiency of goal-directed actions using continuous supplemental kinesthetic feedback.

Measuring Arm and Hand Joint Kinematics to Estimate Impairment During a Functional Reach and Grasp Task after Stroke

Background Current approaches to characterizing deficits in upper limb movements after stroke typically focus either on changes in a functional measure, for example, how well a patient can complete a task, or changes in impairment, for example, isolated measurements of joint range of motion. However, there can be notable dissociations between static measures of impairment versus those of function. Objective We develop a method to measure upper limb joint angles during performance of a functional task and use measurements to characterize joint impairment in the context of a functional task. Methods We developed a sensorized glove that can precisely measure select finger, hand, and arm joints while participants complete a functional reach-to-grasp task involving manipulation of a sensorized object. Results We first characterized the accuracy and precision of the glove’s joint angle measurements. We then measured joint angles in neurologically intact participants (n = 4 participants, 8 limbs) to define the expected distribution of joint angle variation during task execution. These distributions were used to normalize finger, hand, and arm joint angles in stroke participants (n = 6) as they performed the task. We present a participant-specific visualization of functional joint angle variance which illustrated that stroke participants with nearly identical clinical scores exhibited unique patterns of joint angle variation. Conclusions Overall, measuring individual joint angles in the context of a functional task may inform whether changes in functional scores over recovery or rehabilitation are driven by changes in impairment or the development of compensatory strategies, and provide a quantified path toward personalized rehabilitative therapy.

Transformation from arm joint coordinates to hand external coordinates explains non-uniform precision of hand position sense in horizontal workspace

People perceive hand position in horizontal workspace more precisely in radial than in azimuth directions and closer to the body than farther away. Current explanations for this position sense non-uniformity include spatial asymmetry in arm proprioceptive activities and/or cortex maps, experience-dependent learning, arm posture, and others. Here we investigated contributions to this non-uniformity of a posture-dependent transformation from arm joint angles, sensed by arm proprioceptors, to hand position. We measured precision of hand position sense in a bimanual hand mirror-position matching task at four horizontal targets forming a square in front of the body in 11 blindfolded individuals. We found lower hand precision in azimuth than in radial direction, higher azimuth precision at close targets, and higher radial precision at distant targets. We then theoretically analyzed the transformation of random angle errors at shoulder and elbow into hand position random errors in a horizontal plane and obtained similar distributions of hand position errors. The predicted and experimental hand-precision ellipse orientations, but not ellipse shapes or sizes, were highly correlated and were nearly orthogonal to arm stiffness ellipse orientations reported in the literature. We concluded that the joint-to-hand coordinate transformation is responsible for the non-uniform precision of hand position sense.

Trunk muscle activity and kinematics during boxing and battle rope exercise in people with motor-complete spinal cord injury

Context Recovery of seated balance is a rehabilitation priority for people with motor-complete spinal cord injury (mcSCI). Previous research has demonstrated that people with mcSCI can voluntarily engage their trunk muscles during different exercise programs that have the potential to improve seated balance control. Boxing and battle rope exercises could offer another opportunity to improve seated balance for people with mcSCI, but it is unknown if this type of exercise engages trunk musculature and challenges seated balance. Objective To describe the movement patterns of people with mcSCI compared to controls by characterizing the muscle activation patterns and kinematics of the trunk and upper-body during boxing and battle rope exercise. Design Cross-sectional study. Participants 4 males with mcSCI between C7-T9, and 4 able-bodied controls. Methods Participants performed different boxing and battle rope exercises while kinematics and electromyography (EMG) from the trunk and arms were recorded. Outcome Measures Trunk EMG amplitude, trunk and arm joint angles, and trunk curvature. Results Boxing and battle ropes elicited higher relative EMG activity in people with mcSCI compared to controls (P < 0.001). Participants with mcSCI had similar upper-limb kinematics during the exercises to controls, but demonstrated reduced trunk rotation and increased trunk curvature. Conclusions These findings suggest that boxing and battle rope can elicit trunk activity in people with mcSCI, though they may adopt increased trunk curvatures. Future research should explore if such exercise programs may improve seated balance in people with mcSCI.

Robot in the Mirror: Toward an Embodied Computational Model of Mirror Self-Recognition

Self-recognition or self-awareness is a capacity attributed typically only to humans and few other species. The definitions of these concepts vary and little is known about the mechanisms behind them. However, there is a Turing test-like benchmark: the mirror self-recognition, which consists in covertly putting a mark on the face of the tested subject, placing her in front of a mirror, and observing the reactions. In this work, first, we provide a mechanistic decomposition, or process model, of what components are required to pass this test. Based on these, we provide suggestions for empirical research. In particular, in our view, the way the infants or animals reach for the mark should be studied in detail. Second, we develop a model to enable the humanoid robot Nao to pass the test. The core of our technical contribution is learning the appearance representation and visual novelty detection by means of learning the generative model of the face with deep auto-encoders and exploiting the prediction error. The mark is identified as a salient region on the face and reaching action is triggered, relying on a previously learned mapping to arm joint angles. The architecture is tested on two robots with a completely different face.

Algorithm to Generate Trajectories in a Robotic Arm Using an LCD Touch Screen to Help Physically Disabled People

In the last two-decade, robotics has attracted a lot of attention from the biomedical sectors, to help physically disabled people in their quotidian lives. Therefore, the research of robotics applied in the control of an anthropomorphic robotic arm to people assistance and rehabilitation has increased considerably. In this context, robotic control is one of the most important problems and is considered the main part of trajectory planning and motion control. The main solution for robotic control is inverse-kinematics, because it provides the angles of robotic arm joints. However, there are disadvantages in the algorithms presented by several authors because the trajectory calculation needs an optimization process which implies more calculations to generate an optimized trajectory. Moreover, the solutions presented by the authors implied devices where the people are dependent or require help from other people to control these devices. This article proposes an algorithm to calculate an accuracy trajectory in any time of interest using an LCD touch screen to calculate the inverse-kinematics and get the end-point of the gripper; the trajectory is calculated using a novel distribution function proposed which makes an easy way to get fast results to the trajectory planning. The obtained results show improvements to generate a safe and fast trajectory of an anthropomorphic robotic arm using an LCD touch screen allowed calculating short trajectories with minimal fingers moves.

Dimensionality Reduction and Motion Clustering During Activities of Daily Living: Three-, Four-, and Seven-Degree-of-Freedom Arm Movements.

This paper is the first in a two-part series analyzing human arm and hand motion during a wide range of unstructured tasks. The wide variety of motions performed by the human arm during daily tasks makes it desirable to find representative subsets to reduce the dimensionality of these movements for a variety of applications, including the design and control of robotic and prosthetic devices. This paper presents a novel method and the results of an extensive human subjects study to obtain representative arm joint angle trajectories that span naturalistic motions during Activities of Daily Living (ADLs). In particular, we seek to identify sets of useful motion trajectories of the upper limb that are functions of a single variable, allowing, for instance, an entire prosthetic or robotic arm to be controlled with a single input from a user, along with a means to select between motions for different tasks. Data driven approaches are used to discover clusters and representative motion averages for the wrist 3 degree of freedom (DOF), elbow-wrist 4 DOF, and full-arm 7 DOF motions. The proposed method makes use of well-known techniques such as dynamic time warping (DTW) to obtain a divergence measure between motion segments, Ward's distance criterion to build hierarchical trees, and functional principal component analysis (fPCA) to evaluate cluster variability. The emerging clusters associate various recorded motions into primarily hand start and end location for the full-arm system, motion direction for the wrist-only system, and an intermediate between the two qualities for the elbow-wrist system.

Developing a Method to Control an Arm-Assist-Suit by Predicting Arm-Trajectory Using Electromyography.

Many researchers have developed assist-suits to support repetitive and strenuous physical labor, but existing suits show unsatisfactory responsiveness and restrict arm motions. Therefore, we propose a method for an arm-assist-suit that synchronizes arm motions by using electromyography (EMG) to predict arm trajectory. EMG is used to measure and record electrical signals while muscles are active. Further, predicted arm-joint motions and estimated arm-joint angles are used for arm trajectory predictions. In this study, we attempted the prediction of elbow-joint motions and the timing of motion changes. Two subjects executed twelve types of elbow-joint movements that had four start and endpoints. We measured seven muscle types with EMG points on the right arm(hand, elbow, and shoulder) a motion capture system, respectively. After processing these data, we applied a multiclass logistic regression, which is a machine-learning technique, to predict elbow-joint motions, namely, rest, flexion, and extension. The precision in elbow joint motion prediction shows a difference between the two subjects for the three motions analyzed. Additionally, the rest prediction accuracy is lower than both flexion and extension for each subject. The prediction of elbow-joint motion change timing does not correlate with the elbow-joint motion predictions, with the timing prediction precision being very low and thus, causing some difficulties. To overcome these difficulties, and improve precision in future work, we plan to apply an independent component analysis to eliminate noise and add or change features.Clinical Relevance- This study aims to establish a benchmark for future research on the improvement of responsiveness and range-of-motion of arm-assist-suits.

Design and Analysis of a Wearable Upper Limb Rehabilitation Robot with Characteristics of Tension Mechanism

Nowadays, patients with mild and moderate upper limb paralysis caused by cerebral apoplexy are uncomfortable with autonomous rehabilitation. In this paper, according to the “rope + toothed belt” generalized rope drive design scheme, we design a utility model for a wearable upper limb rehabilitation robot with a tension mechanism. Owing to study of the human upper extremity anatomy, movement mechanisms, and the ranges of motion, it can determine the range of motion angles of the human arm joints, and design the shoulder joint, elbow joint, and wrist joint separately under the principle of ensuring the minimum driving torque. Then, the kinematics, workspace and dynamics analysis of each structure are performed. Finally, the control system of the rehabilitation robot is designed. The experimental results show that the structure is convenient to wear on the human body, and the robot’s freedom of movement matches well with the freedom of movement of the human body. It can effectively support and traction the front and rear arms of the affected limb, and accurately transmit the applied traction force to the upper limb of the joints. The rationality of the wearable upper limb rehabilitation robot design is verified, which can help patients achieve rehabilitation training and provide an effective rehabilitation equipment for patients with hemiplegia caused by stroke.

A Robotic Recording and Playback Platform for Training Surgeons and Learning Autonomous Behaviors Using the da Vinci Surgical System

This paper describes a recording and playback system developed using a da Vinci Standard Surgical System and research kit. The system records stereo laparoscopic videos, robot arm joint angles, and surgeon–console interactions in a synchronized manner. A user can then, on-demand and at adjustable speeds, watch stereo videos and feel recorded movements on the hand controllers of entire procedures or sub procedures. Currently, there is no reported comprehensive ability to capture expert surgeon movements and insights and reproduce them on hardware directly. This system has important applications in several areas: (1) training of surgeons, (2) collection of learning data for the development of advanced control algorithms and intelligent autonomous behaviors, and (3) use as a “black box” for retrospective error analysis. We show a prototype of such an immersive system on a clinically-relevant platform along with its recording and playback fidelity. Lastly, we convey possible research avenues to create better systems for training and assisting robotic surgeons.

Sex-Specific Links in Motor and Sensory Adaptations to Repetitive Motion-Induced Fatigue.

The objectives of this study were to assess the sex-specific relationships between motor and sensory adaptations to repetitive arm motion-induced neck/shoulder fatigue, and to measure how additional sensory stimulation affects these adaptations. Twenty-three participants performed two sessions of a repetitive pointing task until scoring 8 on the Borg CR10 scale for neck/shoulder exertion or for a maximum of 45min, with and without sensory stimulation (i.e., light touch) applied on the fatiguing shoulder. Just before reaching the task termination criteria, all participants showed changes in mean and variability of arm joint angles and experienced a fivefold increase in anterior deltoid sensory threshold in the stimulus-present condition. Women with the greatest increases in anterior deltoid sensory thresholds demonstrated the greatest increases in shoulder variability (r = .66), whereas men with the greatest increases in upper-trapezius sensory thresholds demonstrated the greatest changes in shoulder angle (r = -.60) and coordination (r = .65) variability. Thus, sensory stimulation had no influence on time to termination but affected how men and women differently adapted, suggesting sex differences in sensorimotor fatigue response mechanisms.

Uncontrolled manifold analysis of joint angle variability during table tennis forehand

This study was conducted with the objective of evaluating the variance structure of the trunk and racket arm joint angles in table tennis topspin forehand using the uncontrolled manifold (UCM) approach, regarding racket orientation as the task variable. Nine advanced and eight intermediate male collegiate table tennis players performed the topspin strokes against backspin balls. The trunk, upper limb, and racket were modeled as six rigid-link segments with a total of 16 rotation degrees of freedom. The UCM analysis was conducted using 30 trial datasets per participant to quantify the degree of redundancy exploitation needed to stabilize the vertical and horizontal angles of the racket. Irrespective of the performance level, the variance of the joint angle vector increased towards ball impact. The degree of redundancy exploitation increased towards ball impact. As a result, the variability of the racket angles was minimal at impact. Both groups of players used the relative movement between the racket and the hand to stabilize the racket angles at ball impact. The variance of the joint angle vector that affected the vertical racket face angle at ball impact was significantly smaller for advanced players than for intermediate players, and the degree of redundancy exploitation to stabilize that angle at impact tended to be larger for the advanced players. The ability to use the redundancy of the joint configuration to stabilize the vertical racket face angle at impact may be a critical factor that affects performance level.

Estimation of distal arm joint angles from EMG and shoulder orientation for transhumeral prostheses

In this paper, we quantify the extent to which shoulder orientation, upper-arm electromyography (EMG), and forearm EMG are predictors of distal arm joint angles during reaching in eight subjects without disability as well as three subjects with a unilateral transhumeral amputation and targeted reinnervation. Prior studies have shown that shoulder orientation and upper-arm EMG, taken separately, are predictors of both elbow flexion/extension and forearm pronation/supination. We show that, for eight subjects without disability, shoulder orientation and upper-arm EMG together are a significantly better predictor of both elbow flexion/extension during unilateral (R2=0.72) and mirrored bilateral (R2=0.72) reaches and of forearm pronation/supination during unilateral (R2=0.77) and mirrored bilateral (R2=0.70) reaches. We also show that adding forearm EMG further improves the prediction of forearm pronation/supination during unilateral (R2=0.82) and mirrored bilateral (R2=0.75) reaches. In principle, these results provide the basis for choosing inputs for control of transhumeral prostheses, both by subjects with targeted motor reinnervation (when forearm EMG is available) and by subjects without target motor reinnervation (when forearm EMG is not available). In particular, we confirm that shoulder orientation and upper-arm EMG together best predict elbow flexion/extension (R2=0.72) for three subjects with unilateral transhumeral amputations and targeted motor reinnervation. However, shoulder orientation alone best predicts forearm pronation/supination (R2=0.88) for these subjects, a contradictory result that merits further study.

An MR-compatible gyroscope-based arm movement tracking system

BackgroundFunctional magnetic resonance imaging is well suited to link neural population activation with movement parameters of complex natural arm movements. However, currently existing MR-compatible arm tracking devices are not constructed to measure arm joint movement parameters of unrestricted movements. Therefore, to date most research focuses on simple arm movements or includes very little knowledge about the actual movement kinematics. New methodWe developed a low cost gyroscope-based arm movement tracking system (GAMTS) that features MR-compatibility. The system consists of dual-axis analogue gyroscopes that measure rotations of upper and lower arm joints. After MR artifact reduction, the rotation angles of the individual arm joints are calculated and used to animate a realistic arm model that is implemented in the OpenSim platform. The OpenSim platform can then provide the kinematics of any point on the arm model. ResultsIn order to demonstrate the capabilities of the system, we first assessed the quality of reconstructed wrist movements in a low-noise environment where typical MR-related problems are absent and finally, we validated the reconstruction in the MR environment. Comparison with existing methodsThe system provides the kinematics of the whole arm when natural unrestricted arm movements are performed inside the MR-scanner. ConclusionThe GAMTS is reliably capable of reconstructing the kinematics of trajectories and the reconstruction error is small in comparison with the movement induced variation of speed, displacement, and rotation. Moreover, the system can be used to probe brain areas for their correlation with movement kinematics.

Tibialis anterior moment arm: effects of measurement errors and assumptions.

Accurate estimates of tibialis anterior (TA) muscle force are important in many contexts. Two approaches commonly used to estimate moment arms are the tendon excursion (TE) and geometric (GEO) methods. Previous studies report poor agreement between the two approaches. The purposes of this study were to 1) assess the effect of methodological variations in the two methods of moment arm estimation and 2) determine how these variations affect agreement between the methods. TA moment arms were determined using TE and GEO. Errors associated with tendon stretch/hysteresis, talus rotation relative to the foot, and the location of the line of action were investigated. For TE, large errors in moment arm estimates across the range of motion were found when tendon length changes (P = 0.001) were not corrected for. For GEO, the estimated moment arm was reduced at an ankle angle of -15° when discrepancies between talus and foot rotations were accounted for or when an alternative tendon line of action was used either separately (effect size (ES), 0.46 and 0.58, respectively; P > 0.05) or together (ES, 0.89; P > 0.05). TE-derived moment arms were smaller than GEO-derived moment arms (ES, 0.68-4.86, varying by angle) before accounting for sources of error. However, these differences decreased after error correction (ES, 0.09-1.20, P > 0.05). Nonetheless, the shape of the moment arm-joint angle relation was curvilinear for TE but linear for GEO. Of all methodological modifications, accounting for tendon length changes had the largest effect on TA moment arm estimates. We conclude that the TE method is viable to determine TA moment arms as long as changes in tendon length are accounted for.

Rapid calibration of a multi-sensorial humanoid’s upper body: An automatic and self-contained approach

This paper addresses the problem of calibrating a pair of cameras, a Microsoft Kinect sensor and an inertial measurement unit (IMU) mounted at the head of a humanoid robot with respect to its kinematic chain. As complex manipulation tasks require an accurate interplay of all involved sensors, the quality of calibration is crucial for the outcome of the intended tasks. Typical procedures for calibrating are often time-consuming, involve multiple people overseeing a series of subsequent calibration steps and require external tools. We therefore propose to auto-calibrate all sensors in a single, completely automatic and self-contained procedure, i.e. without a calibration plate. By automatically detecting a single point feature on each wrist while moving the robot’s head, the stereo cameras’, the Kinect’s infrared camera’s intrinsic and extrinsic and an IMU’s extrinsic parameters are calibrated while considering the arm joint elasticities and joint angle offsets. All parameters are obtained by formulating the calibration problem as a single least-squares batch-optimization problem. The procedure is integrated on DLR’s humanoid robot Agile Justin allowing to obtain an accurate calibration in around 5 minutes by simply “pushing a button”. The proposed approach is experimentally validated by means of standard metrics of the calibration errors.

Analysis and prediction of upper extremity movements by cyclograms

Abstract Quantification of upper extremity movement is a common objective in both research and clinical practice. Currently, methods based on angle-angle diagrams, also called cyclograms, seem to be promising. Nevertheless, compared to the study of lower limbs, the concept of angle-angle diagrams has not been systematically used to study upper limb movements during walking. The paper describes two examples of new methods based on angle-angle diagrams for application in rehabilitation and assistive robotics. The cyclograms represent information about the relationship between the angles and their changes over time. We used cyclograms as patterns for learning artificial neural networks and predicting the movement of upper-limb. Together with artificial intelligence, cyclograms offer wide scope of application in prosthesis control systems. Using bilateral cyclogram, the information about the relationship between the right and left arm joint angles is used to evaluate the symmetry of movements. The method based on the orientation of the bilateral cyclogram can be used as an additional method for determining the symmetry of movements of the upper limbs or exo-prosthesis.