Elbow Joint Angle Estimation Research Articles

Improved Estimation of Elbow Flexion Angle from IMU Measurements Using Anatomical Constraints

ObjectivesInertial Measurement Units (IMUs) are a valid alternative to optical tracking systems for human motion capture, but they are subject to several disturbances that limit their accuracy. We aim to improve the accuracy of elbow joint angle estimation from IMU measurements by introducing a novel postprocessing algorithm that uses anatomical constraints and does not require any prior calibration or knowledge of anthropometric parameters. Materials and MethodsWe propose a new error model that addresses sensor misalignment and fusion errors. We use an error state extended Kalman filter (ESEKF) with state constraints to integrate the anatomical constraints. We validate the proposed algorithm by testing it in different scenarios and comparing it with a state-of-the-art optical tracking system. ResultsThe research results highlight the superior performance of the proposed method compared with existing techniques. The study demonstrates a significant reduction in errors, particularly in complex arm movements and under strong external disturbances. The results obtained in the three different tested scenarios underscore the robustness and effectiveness of the developed algorithm, reaching half the error committed by the existing calibration-free correction algorithms proposed in the literature. ConclusionsThe developed technique provides highly accurate estimates of joint angles in several challenging real-world scenarios.

Estimation of shoulder and elbow joint angle from linear quadratic tracking based on six muscle force vectors

Two dimensional human’s arm is one of the optimal control application. This arm model consists of joint angles consisting of shoulder joint and elbow joint. Beside that, there are also muscle force vectors consisting of pectoralis major, posterior deltoid, brachialis, lateral head of triceps brachii, biceps brachii, and longhead of triceps. Linear Quadratic Tracking (LQT) is constructed to obtain solution of state and optimal control. From state solution and optimal control obtained, they will be used estimation by Backpropagation and ANFIS. From data resulted from LQT simulation, the input used are muscle force vectors. They will be used for estimation the output i.e. angle of shoulder joint and angle of elbow joint. From the LQT simulation, the angle position can follow the given reference function. Then Backpropagation and ANFIS can make estimation in angle of shoulder joint and angle of elbow joint based on six muscle force vectors with small RMSE. In Backpropagation, the estimation result of angle of shoulder joint between target and output with RMSE is 0.0954 and the estimation result of angle elbow joint between target and output with RMSE is 0.2528. In ANFIS, the estimation result of angle of shoulder joint between target and output with RMSE is 0.0062 and the estimation result of angle elbow joint between target and output with RMSE is 0.0526.

Effect of velocity and acceleration in joint angle estimation for an EMG-Based upper-limb exoskeleton control

Most studies on estimating user's joint angles to control upper-limb exoskeleton have focused on using surface electromyogram (sEMG) signals. However, the variations in limb velocity and acceleration can affect the sEMG data and decrease the angle estimation performance in the practical use of the exoskeleton. This paper demonstrated that the variations in elbow angular velocity (EAV) and elbow angular acceleration (EAA) associated with normal use led to a large effect on the elbow joint angle estimation. To minimize this effect, we proposed two methods: (1) collecting sEMG data of multiple EAVs and EAAs as training data and (2) measuring the values of EAV and EAA with a gyroscope. A self-developed upper-limb exoskeleton with pneumatic muscles was used in the online control phase to verify our methods' effectiveness. The predicted elbow angle from the sEMG-angle models which were trained in the offline estimation phase was transferred to control signal of the pneumatic muscles to actuate the exoskeleton to move to the same angle. In the offline estimation phase, the average root mean square error (RMSE) between predicted elbow angle and actual elbow angle was reduced from 22.54° to 10.01° (using method one) and to 6.45° (using method two), respectively; in the online control phase, method two achieved a best control performance (average RMSE = 6.87°). The results showed that using multi-sensor fusion (sEMG sensors and gyroscope) achieved a better estimation performance than using only sEMG sensor, which was helpful to eliminate the velocity and acceleration effect in real-time joint angle estimation for upper-limb exoskeleton control.

Dynamic feature for an effective elbow-joint angle estimation based on electromyography signals

<a name="_Hlk561743"></a><span>Some physical parameters influence the electromyography signal (EMG). when the EMG signal is used to estimate the position of the elbow. An adaptable feature was important to reduce a variation on the parameters. The aim of this paper is to estimate the joint position of the elbow using EMG signal based on a dynamic function. The major contribution of this work is that the method proposed is capable of determining the elbow position using the non-pattern (NPR) recognition (PR) method. A Wilson amplitude (WAMP) which used a dynamic threshold was used to reduce the EMG signal. The dynamic threshold was generated from the root mean square (RMS) processor. With the dynamic threshold, the model could adapt to any variations on the independent variables. In order to confirm this opportunity, this work involved ten healthy male subjects to perform an experimental protocol. After a tuning and calibration process, the mean of RMS error and correlation coefficient are 9.83º±1.69º and 0.98±0.01 for a single cycle of motion, 10.39º±1.82º and 0.97±0.01 for a continuous cycle of motion and 15.19º±1.92º and 0.94±0.02 for the arbitrary gesture. For conclusion, the performance of the prediction did not significantly depend on the varying cycle of gesture (p-value>0.05). This study has confirmed that the success of the non-pattern recognition-based prediction of elbow position is adaptable to any different subjects, loads, and speed of motion</span><span>.</span>

Integrated System for Monitoring Muscular States during Elbow Flexor Resistance Training in Bedridden Patients.

To improve or maintain the physical function of bedridden patients, appropriate and effective exercises are required during the patient's bed rest. Resistance training (RT) is an effective exercise for improving the physical function of bedridden patients, and the improvement of the physical function is caused by mechanical stimuli associated with RT. Currently, the measured mechanical stimuli are external variables which represent the synthetic effect of multiple muscles and body movements. Important features of stimuli experienced by muscles are of crucial importance in explaining muscular strength and power adaptation. This study describes an integrated system for assessing muscular states during elbow flexor resistance training in bedridden patients, and some experiments were carried out to test and evaluate this system. The integrated system incorporates an elbow joint angle estimation model (EJAEM), a musculoskeletal model (MSM), and a muscle-tendon model. The EJAEM enables real-time interaction between patient and MSM. The MSM is a three-dimensional model of the upper extremity, including major muscles that make up the elbow flexor and extensor, and was built based on public data. One set of concentric and eccentric contraction was performed by a healthy subject, and the results of the calculations were analyzed to show important features of mechanical stimuli experienced by muscles during the training. The integrated system provides a considerable method to monitor the body-level and muscle-level mechanical stimuli during elbow flexor resistance training in bedridden patients.

Performance Analysis of the Windowing Technique on Elbow Joint Angle Estimation Using Electromyography Signal

In the digital signal processing, the windowing technique is essential in the feature extraction process because it influences the performance of the proposed models. The objective of this study is to evaluate the performance of the windowing technique in the elbow joint angle estimation based on electromyography (EMG) signal. In the elbow joint angle estimation, the results showed that the window length and percentage of overlap in windowing technique affected the performance of the estimation for all time-domain features. In the adjacent windowing technique, a window length of 100 milliseconds has the highest performance in estimation. In the overlap windowing technique, the percentage of overlap is 10% .It is the highest performance of the estimation. Features ZC, SSC, WAMP, and MYOP have better RMSE than the others. In the window length of 100 milliseconds, the mean and standard deviation of RMSE for the features ZC, SSC, WAMP, and MYOP are 14.41°±3.86°, 15.20°±4.59°, 14.50°±3.33° and 13.56°±3.53°, respectively.

Adaptive threshold to compensate the effect of muscle fatigue on elbow-joint angle estimation based on electromyography

Muscle fatigue is a major problem in the study based on electromyography (EMG) signal. When the muscle in the fatigue condition, the power of the EMG signal increases significantly. This phenomenon can be a problem in the elbow-joint angle estimation. The purpose of this study is to develop a method in order to compensate the effect of the muscle fatigue on the elbow-joint angle estimation based on time domain features using EMG signal. The EMG signal was collected from biceps while the subjects performed a fatiguing motion of flexion and extension. The EMG was extracted using four time-domain features, namely zero crossing (ZC), sign slope change (SSC), Wilson amplitude (WAMP) and myopulse percentage rate (MYOP). The yielded features were filtered using second order Butterworth low pass filter. In the proposed method, to compensate the effect of the muscle fatigue, the RMS of the EMG signal was calculated for every cycle and used it as a threshold value of the features. The results show that the proposed method is able to compensate the effect of muscle fatigue with a consistent root mean square error (RMSE). The improvement of the performance ranges between 17.41% and 37.9% (for all adaptive features).

Effect of shoulder angle variation on sEMG-based elbow joint angle estimation

For the decade now, surface electromyogram (sEMG) signal has been extensively applied in joint angle estimation to control the prostheses and exoskeleton systems. However, the sEMG signal patterns can be severely affected by shoulder angle variations, which restricts its applications to a practical use. In our study, we evaluate the effect of shoulder angle variations on elbow angle estimation performance. This adverse effect increases mean root mean square (RMS) error by 14.85∘ in our experiment. Then, four estimation methods are proposed to solve this problem: (1) using a training set including all shoulder angles' training data to train model; (2) adding two shoulder muscles' sEMG as additional inputs; (3) a two-step method using arm muscles' sEMG and two shoulder muscles' sEMG; and (4) a two-step method using arm muscles' sEMG and measured shoulder angle value by a motion sensor. 13 subjects are employed in this study. The experimental results demonstrate that the mean RMS error is reduced from 21.36∘ to 12.85∘ in method one, 9.84∘ in method two, 7.67∘ in method three, and 6.93∘ in method four, respectively. These results show that our methods are effective to eliminate the adverse effect of shoulder angle variations and achieve a better elbow angle estimation performance. Furthermore, this study is helpful to develop a natural and stable control system for prostheses and exoskeleton systems.

Muscle fatigue compensation of the electromyography signal for elbow joint angle estimation using adaptive feature

The purpose of this study is to develop and evaluate an adaptive feature to compensate the effect of muscle fatigue in the elbow joint angle estimation. In the experiment protocol, subjects are asked to move the elbow in flexion and extension for 15 min. The electromyography (EMG) signal collected from biceps was extracted using the Wilson amplitude (WAMP) and modified WAMP features. A modified WAMP feature consists of the standard WAMP feature and an adaptive threshold; to smooth the features, the Kalman filter was applied. The results prove that muscle fatigue affects the EMG signal. The RMSEs resulting from the standard WAMP feature in the non-fatigue and fatigue conditions are 12.41° ± 5.16° and 18.17° ± 5.53°, respectively. The RMSEs resulting from modified WAMP feature in the non-fatigue and fatigue conditions are 12.63° ± 5.22° and 13.86° ± 4.7°, respectively. This proposed method is able to compensate the effect of muscle fatigue on the elbow joint angle estimation.

Estimation of Elbow Joint Angle Based on Electromyography Using the Sign-Slope Change Feature and Kalman Filtering

Electromyography (EMG) has widely used in the field of biomedical engineering as a control signal for prosthetic devices and exoskeleton robotic. High accuracy in the prediction of the limb joint is very important to determine the effectiveness of the system. In this study, we propose a new algorithm to improve the elbow joint angle prediction based on electromyography using Sign Slope Change (SSC) feature extraction and Kalman filter (SSC-KF). The EMG signals acquired from the biceps were extracted using SSC feature to get the estimation of the elbow joint angle. The accuracy of the prediction of the elbow joint angle was improved by using Kalman filter. In this study, the SSC-KF algorithm can predict the elbow joint angle with high accuracy. The Pearson’s correlation coefficients (mean±S.D.) were 0.95±0.02, 0.96±0.01, and 0.96±0.015 for the motion period of 12 seconds, 8 seconds, and 6 seconds, respectively. Root Mean Square Errors (mean±S.D.) were 10.37°±1.72°, 9.89°±1.11°, and 9.99°±2.2°, for the motion periods of 12 seconds, 8 seconds, and 6 seconds respectively. This SSC-KF algorithm can predict the elbow joint prediction by using a single lead electrode from biceps muscle.

Real time data acquire from multiple Accelerometers and IMU to calculate 3-direction angles and relative orientation

Measuring human body kinematics using inertial measurement unit (IMU) has increased in popularity as its flexibility, cheap and ease of use, compared to alternative strategies. In this work estimation of Elbow joint angle was done in three directions based on 9 DOF Adafruit IMU and three ADXL-345 accelerometers. A special circuit board was designed and programmed using Arduino UNO to handle two IMU with six accelerometers in two links The Kalman filter is applied to the IMU tand adapted to work with the circuit adequetly . Also, low pass filters applied to the Accelerometers data. As the outcome data is huge a program has been developed using C# to receive the data from two links. Matlab program was used to calculate three direction angles based on the sensors data collected from the circuit board. Three directions joint angle has been estimated and rotational matrix for the model is formulated.

Continuous Estimation of Elbow Joint Angle by Multiple Features of Surface Electromyographic Using Grey Features Weighted Support Vector Machine

Continuous Estimation of Elbow Joint Angle by Multiple Features of Surface Electromyographic Using Grey Features Weighted Support Vector Machine

Elbow joint angle and elbow movement velocity estimation using NARX-multiple layer perceptron neural network model with surface EMG time domain parameters.

Estimation of elbow dynamics has been the object of numerous investigations. In this work a solution is proposed for estimating elbow movement velocity and elbow joint angle from Surface Electromyography (SEMG) signals. Here the Surface Electromyography signals are acquired from the biceps brachii muscle of human hand. Two time-domain parameters, Integrated EMG (IEMG) and Zero Crossing (ZC), are extracted from the Surface Electromyography signal. The relationship between the time domain parameters, IEMG and ZC with elbow angular displacement and elbow angular velocity during extension and flexion of the elbow are studied. A multiple input-multiple output model is derived for identifying the kinematics of elbow. A Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural network (MLPNN) model is proposed for the estimation of elbow joint angle and elbow angular velocity. The proposed NARX MLPNN model is trained using Levenberg-marquardt based algorithm. The proposed model is estimating the elbow joint angle and elbow movement angular velocity with appreciable accuracy. The model is validated using regression coefficient value (R). The average regression coefficient value (R) obtained for elbow angular displacement prediction is 0.9641 and for the elbow anglular velocity prediction is 0.9347. The Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural networks (MLPNN) model can be used for the estimation of angular displacement and movement angular velocity of the elbow with good accuracy.

ESTIMATION OF ELBOW JOINT POSITION BEFORE, DURING, AND FOLLOWING A 17-DAY SPACEFLIGHT.

1920 The ability to estimate elbow joint position was tested in four male astronauts before (-30 and -12 days), during (1, 4, 9, and 15 days), and after(+1, +5, +9, and +16 days) a 17-day spaceflight (STS-78). Subjects were asked to estimate 90 ° or 135 ° joint angles during passive (relaxed) and active (10% maximal agonist effort) isokinetic extension (EXT) and flexion(FLX). Subjects also estimated these joint positions during low resistance isotonic EXT. For each angle, 6 (isokinetic) and 3 (isotonic) trials were requested in random order. Across all test conditions, the mean angle estimate ranges were 93 °-118 ° and 119 °-152 ° for requested angles of 90 ° and 135 °, respectively. Subjects overestimated the 90 ° position across all conditions, whereas the accuracy of the 135 ° estimate varied. Angle estimates were most accurate during isokinetic FLX for 90 °(mean error +9.8 °) and isokinetic EXT for 135 ° (mean error +0.7°). No differences occurred between passive vs. active isokinetic EXT or FLX. Estimates of each joint angle during isokinetic EXT were less than isotonic EXT, but greater than isokinetic FLX (P<0.05). During spaceflight, joint angle estimates were lower during isokinetic FLX and EXT for 90 °(P<0.05) and 135 ° (P≤0.10) compared to preflight. Spaceflight also decreased angle estimates during isotonic FLX and EXT (P<0.05). Elbow angle estimates had returned to preflight values one day after return to 1G. These data demonstrate that elbow joint angle estimation was: 1) decreased during spaceflight, particularly for isotonic conditions 2) rapidly restored to preflight values upon return to 1G; and 3) not changed by low level agonist muscle exertion during isokinetic movements. These data indicate that the proprioception of elbow joint position is altered by microgravity, but rapidly readapts to preflight control values after return to 1G.