Hand Control System Research Articles

A Machine Learning–Based Ergonomic Assessment of Wireless Hand Control System for Lower‐Limb Disabled Tractor Operators and Abled Female Agricultural Workers

ABSTRACTTractor being the most used power source for agricultural operations needs hand control (HC) and foot control (FC) to maneuver it. FCs restrict lower‐limb disabled agricultural workers from participating in tractor operation, and high requirement of actuation forces to operate FCs may create overexertion and early fatigue to female agricultural workers. Therefore, a sensor‐based HC system has been developed to assist them in tractor operation with minimal actuating force. This study focuses on ergonomic assessment of the HC system to assess the suitability for the abled and disabled agricultural workers, including physiological, psychophysical, and muscle fatigue parameters. Heart rate (HR) of abled male and female, and disabled male and female was observed in the range of 83–118, 85–117, 93–118, and 92–114 beats/min, respectively, during tractor operation. Energy expenditure rate (EER) during tractor operation with FCs (9.7–17.4 kJ/min) was observed higher than with the HC system (7.3–16.5 kJ/min). Body parts discomfort was observed highest for the right hand of all the subjects (4.9–5.3) and maximum overall discomfort was experienced by abled females during the operation with FCs (5.4) as they have to exert higher force. The root mean square (RMS) value of the electromyography signal obtained for extensor digitorum muscle was found to be higher for all the subjects and with both HC and FC (abled male, 17.37–40.43 µV; abled female, 14.76–45.29 µV; disabled male, 15.49–40.23 µV; disabled female, 30.32–54.29 µV) than other upper arm muscles middle deltoid, flexor carpi radialis, and brachioradialis. Muscle workload for all the selected muscles of all the subjects was observed within the recommended limit during the tractor operation with a developed HC system (< 30%). Categorization of overall discomfort rating (ODR) of the subjects using HR, EER, and RMS through machine learning algorithms such as k‐nearest neighbor (KNN), random forest classifier, and support vector machine predicted the ODR with accuracies in the range of 77%–83%. KNN algorithm was found to be most accurate with prediction accuracy of 83%. The developed HC system provides assistantship to the lower‐limb disabled agricultural workers (1%–100% disability of lower limbs) and allows female workers to operate the tractor with minimal physical exertion.

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

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

Development of Wrist Separated Exoskeleton Socket of Myoelectric Prosthesis Hand for Symbrachydactyly.

In recent years, the functionality of myoelectric prosthetic hands has improved as motors have become smaller and controls have become more advanced. Attempts have been made to reproduce the rotation and flexion of the wrist by adding degrees of freedom to the wrist joint. However, it is still difficult to fully reproduce the functionality of the wrist joint owing to the weight of the prosthesis and size limitations. In this study, we developed a new socket and prosthetic hand control system that does not interfere with the wrist joint motion. This allows individuals with hand defects who previously used prosthetic hands with fixed wrist joints to freely use their remaining wrist functionality. In the pick-and-place experiment, where blocks were moved from higher to lower locations, we confirmed that the proposed system resulted in a lower elbow position compared with the traditional prosthesis, and the number of blocks transported increased. This significantly reduced the compensatory motion of the elbow and improved the user's performance compared with the use of a conventional prosthetic hand. This study demonstrates the usefulness of a new myoelectric prosthetic hand that utilizes the residual functions of people with hand deficiencies, which have not been utilized in the past, and the direction of its development.

I-MYO: A multi-grasp prosthetic hand control system based on gaze movements, augmented reality, and myoelectric signals.

Controlling a multi-grasp prosthetic hand still remains a challenge. This study explores the influence of merging gaze movements and augmented reality in bionics on improving prosthetic hand control. A control system based on gaze movements, augmented reality, and myoelectric signals (i-MYO) was proposed. In the i-MYO, the GazeButton was introduced into the controller to detect the grasp-type intention from the eye-tracking signals, and the proportional velocity scheme based on the i-MYO was used to control hand movement. The able-bodied subjects with no prior training successfully transferred objects in 91.6% of the cases and switched the optimal grasp types in 97.5%. The patient could successfully trigger the EMG to control the hand holding the objects in 98.7% of trials in around 3.2s and spend around 1.3s switching the optimal grasp types in 99.2% of trials. Merging gaze movements and augmented reality in bionics can widen the control bandwidth of prosthetic hand. With the help of i-MYO, the subjects can control a prosthetic hand using six grasp types if they can manipulate two muscle signals and gaze movement.

A sensor‐based assistive technology for lower‐limb‐disabled agricultural workers and abled female workers for tractor clutch and brake operation

AbstractAgricultural mechanization although reduces the food demand and supply gap through improved and smart technologies, it also risks the lower‐limb disability of workers due to accidents that restrict them to participate in agricultural operations due to the unavailability of appropriate assistive technologies. The lack of use of ergonomics further minimizes the inclusion of female workers in farming operations. Therefore, this study is focused on the development of a sensor‐based wireless hand control system for tractor clutch and brake operation to assist lower‐limb‐disabled agricultural workers and abled females in tractor operation. Actuation force and speed of conventional clutch and brake pedals were evaluated using an integrated foot transducer and used as a reference for the design of the system (clutch: 92–310 N, 36–43 mm/s; brake: 106–329 N, 57–63 mm/s). The hand control system consists of a transmitter and a receiver node incorporating a single‐board computer, matrix keypad, voltage regulators, relay switches, wireless communication through radio frequency modules, and electric linear actuators connected with the pedals through L‐shaped clips, nuts, bolts, and chains. The response time of the developed system was determined as 100 ms and the stopping distance of the tractor using the hand control system ranged from 44.0 to 190.7 cm at a forward speed of 2.1–7.9 km/h. The ergonomic evaluation measured using the K4b2 oxygen analyzer identified the foot control usage in tractors as high energy consuming (9.7–15.1 kJ/min) compared with the hand control usage (7.3–14.1 kJ/min). Electromyography‐root mean square (EMG‐RMS) of upper limb muscle, extension digitorum was found to be highest (14.76–45.29 µV) and was significantly affected by forward speed, gender, subjects, and type of control (p < 0.01) whereas, EMG‐RMS of other selected upper limb muscles, middle deltoid, flexor carpi radialis, and brachioradialis was not significantly affected by the type of control (foot or hand). Muscle workload of all the muscles was observed within the recommended limit (<30%) during the tractor operation with the developed hand control system. The developed hand control system provides options to bypass foot‐based actuation requirement for lower limb disabled as well as female workers. The novel design requires minimal workplace and operating forces and is inclusive of operator ergonomics.

Suppression of Packet Losses and Disturbances in Networked Control Systems Based on Equivalent-Input-Disturbance Approach.

This article presents a method of suppressing packet losses and exogenous disturbances for a networked control system (NCS) subject to network-introduced delays. The NCS has two feedback loops: 1) a local one and 2) a main one. The local feedback loop contains a state observer, an equivalent-input-disturbance (EID) estimator, and state feedback. It is used to ensure prompt disturbance suppression. The controller in the main feedback loop contains an internal model to track a reference input. The system is divided into two subsystems for the design of controllers. The state-observer gain is designed for one subsystem using the concept of perfect regulation to ensure disturbance estimation performance. The state-feedback gains of the other subsystem are designed based on a stability condition in the form of a linear matrix inequality (LMI). A tracking specification is embedded in the LMI-based stability condition to ensure satisfactory tracking performance. A case study on a two-finger robot hand control system and a comparison with a Smith-EID and H∞ controller approach validate the effectiveness and superiority of the presented method.

Classification of Different Shoulder Girdle Motions for Prosthesis Control Using a Time-Domain Feature Extraction Technique

Abstract—The upper limb amputation exerts a significant burden on the amputee, limiting their ability to perform everyday activities, and degrading their quality of life. Amputee patients’ quality of life can be improved if they have natural control over their prosthetic hands. Among the biological signals, most commonly used to predict upper limb motor intentions, surface electromyography (sEMG), and axial acceleration sensor signals are essential components of shoulder-level upper limb prosthetic hand control systems. In this work, a pattern recognition system is proposed to create a plan for categorizing high-level upper limb prostheses in seven various types of shoulder girdle motions. Thus, combining seven feature groups, which are root mean square, four-order autoregressive, wavelength, slope sign change, zero crossing (ZC), mean absolute value, and cardinality. In this article, the time-domain features were first extracted from the EMG and acceleration signals. Then, the spectral regression (SR) and principal component analysis dimensionality reduction methods are employed to identify the most salient features, which are then passed to the linear discriminant analysis (LDA) classifier. EMG and axial acceleration signal datasets from six intact-limbed and four amputee participants exhibited an average classification error of 15.68 % based on SR dimensionality reduction using the LDA classifier.

Multisensory Fusion, Haptic, and Visual Feedback Teleoperation System Under IoT Framework

The combination of teleoperating robots and the Internet of Things (IoT) could be employed in many areas, including remote nursing and semi-mechanical control. However, it is known that subjects can quickly encounter physical and mental fatigue, which can potentially lower accuracy in teleoperation. To address this issue, this article presents a closed-loop teleoperation system based on multisensory fusion, visual, and haptic feedback within the IoT framework. Various sensors for electromyography, inertial measurement unit, and mechanical hand control system are deployed to obtain body signals from participants, subsequently processed by artificial intelligence methods. Resistive sensors are installed at the robotic hand side to learn the contact force between the robot hand and the grasped object. To allow the user to understand the contact force levels, a haptic interface is equipped to provide three-level mechanical vibrations. To help users identify and trace different objects, a region convolution neural network for detecting 100 different categories of things is constructed. In addition, the real-time control (delay <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {&lt; } 110$ </tex-math></inline-formula> ms) is offered by a tri-layered IoT architecture. The feasibility of the proposed technique is validated by a grasping task carried out by 20 volunteers. During experiments, it is observed that merely 10.8 s is averagely needed for participants in finishing the task, with a high average success rate of 97%. Besides, NASA-TLX questionnaire and maximum voluntary contraction test report that users suffer light mental and physical fatigue when the proposed technique is used.

A Non-Invasive, Soft Robotic Wearable Glove to Attenuate Hand Tremors

Hand tremors are a widespread medical condition that decreases a patient’s quality of life. Due to uncontrollable hand shaking, patients have difficulty completing essential daily tasks. Many also experience embarrassment and anxiety in social settings, which negatively affects many areas of their lives. Current treatments are often invasive, ineffective, and have unwanted side effects. Some non-invasive solutions excessively limit hand motion, making them ineffective in helping the wearer complete tasks, while others are highly visible and bulky. This paper presents a minimalistic, non-invasive, wearable glove that uses active feedback control and miniature, soft robotic pneumatic actuators to attenuate hand tremors while allowing freedom of movement in other directions. We 3D-modelled the hand and wrist and designed our glove and actuators using Fusion 360. These models were imported into Matlab and Simulink, where we simulated the hand tremor, actuation, and control system using spatial contact forces. To actively reduce the tremor amplitude, we used an accelerometer to detect the onset of tremor motion and create a real-time estimate of the hand’s position. This estimate drove a closed-loop PI controller that produced force commands for the pneumatic actuators, which applied a counterforce opposing the direction of hand motion. Based on our simulation results, our active glove control system design successfully reduced the hand tremor amplitude by 75%, from a baseline of ±8.0mm to a significantly improved amplitude of ±2.0mm. These results indicate that our proposed solution could effectively attenuate hand tremors to a level that would increase the wearer’s quality of life.

Hardware and software architecture for anthropomorphic robot hand control system

Hardware and software architecture for anthropomorphic robot hand control system

Robot Hand Control System for Picking Various Shaped Objects

Robot Hand Control System for Picking Various Shaped Objects

Reliability of forearm medial-anterior surface dimensional changes at different isometric hand grip forces

Indirect techniques of predicting hand grip force are fundamental to develop hand control systems for assistive devices. The purpose of this study was to determine the reliability of 3D position of forearm surface at different isometric hand grip forces. Three-dimensional motion analysis was used to measure displacement of 24 discrete and standardized surface markers placed on forearm in 20 healthy participants. The relative displacements were measured for isometric grip forces at 0%, 5%, 20% 50% and 80% of maximum voluntary contraction (MVC). Intraclass correlation coefficient (ICC) for relative radial displacement (RRD) of each marker was calculated. Averaged single measure ICC of 24 markers at five grip forces was 0.61; while the highest averaged single measure ICC (0.80) for all markers was achieved at 80% of MVC and the lowest (0.47) at 0% of MVC. The average measure ICC for each grip force across the 24 markers also increased with grip force from 0.80 at 0% of MVC to the maximum of 0.95 at 80% of MVC. In conclusion, RRD showed moderate and high ICCs for single and average measures respectively. Overall, this study suggests that the reliable dimensional changes of 3D positions of forearm surface might be considered as an indirect and non-invasive method to predict hand grip force in future.

General Concept of the EMG Controlled Bionic Hand

The article presents a general concept of a bionic hand control system using a multichannel EMG signal, being under development at present. The method of acquisition and processing of multi-channel EMG signal and feature extraction for machine learning were described. Moreover, the design of the control system implementation in the real-time embedded system was discussed.

Finger Angle Estimation From Array EMG System Using Linear Regression Model With Independent Component Analysis.

Surface ElectroMyoGraphy (EMG) signals from the forearm used in prosthetic hand and finger control systems require precise anatomy data of finger muscles that are small and located deep within the forearm. The main problem of this method is that the signal quality depends on the placement of EMG sensor, which can significantly affects the accuracy and precision to estimate joint angles or forces. Moreover, in case of amputees, the location of finger muscles is unknown and needed to be identified manually for EMG recording. As a result, most modern prosthetic hands utilize limited number of muscles with pattern recognition to control finger according to pre-defined grip which is unable to mimic natural finger motion. To address such issue, we used array EMG sensors to obtain EMG signals from all possible positions on the forearm and applied regression method to produce natural finger motion. The signals were analyzed using independent component analysis (ICA) to find the best-fitted independent component (IC) that matches the anatomical data taken after the experiment. Next, from the IC and EMG signals, finger angles were estimated using linear regression model (LRM). Each finger was assigned EMG and IC component for flexion and extension muscles, to assess the possibility of controlling each finger angle separately. We compared the joint angles of each finger between calculated from IC and EMG by correlation coefficients (CC) for all fingers. The average CC values were higher than 0.7, demonstrating the strength of the linear relationship. The different between IC and EMG methods suggests that the IC method can reduce noise and increase the signal to noise ratio. The performance of ICA method showed higher CC value at around 0.2 ± 0.10. In order to confirm the performance of ICA method, we also tested mathematical musculoskeletal model (MSM). The result from this study showed that not only array EMG sensors with ICA significantly improve the quality of signal detected from forearm but also reduce problems of conventional EMG sensors and consequently improve the performance of regression method to imitate natural finger motion.

Differentiating Variations in Thumb Position From Recordings of the Surface Electromyogram in Adults Performing Static Grips, a Proof of Concept Study.

Hand gesture and grip formations are produced by the muscle synergies arising between extrinsic and intrinsic hand muscles and many functional hand movements involve repositioning of the thumb relative to other digits. In this study we explored whether changes in thumb posture in able-body volunteers can be identified and classified from the modulation of forearm muscle surface-electromyography (sEMG) alone without reference to activity from the intrinsic musculature. In this proof-of-concept study, our goal was to determine if there is scope to develop prosthetic hand control systems that may incorporate myoelectric thumb-position control. Healthy volunteers performed a controlled-isometric grip task with their thumb held in four different opposing-postures. Grip force during task performance was maintained at 30% maximal-voluntary-force and sEMG signals from the forearm were recorded using 2D high-density sEMG (HD-sEMG arrays). Correlations between sEMG amplitude and root-mean squared estimates with variation in thumb-position were investigated using principal-component analysis and self-organizing feature maps. Results demonstrate that forearm muscle sEMG patterns possess classifiable parameters that correlate with variations in static thumb position (accuracy of 88.25 ± 0.5% anterior; 91.25 ± 2.5% posterior musculature of the forearm sites). Of importance, this suggests that in transradial amputees, despite the loss of access to the intrinsic muscles that control thumb action, an acceptable level of control over a thumb component within myoelectric devices may be achievable. Accordingly, further work exploring the potential to provide myoelectric control over the thumb within a prosthetic hand is warranted.

Prosthetic hand control system based on object matching and tracking

Prosthetic hand control system based on object matching and tracking

Finger angle estimation using musculoskeletal model on thumb and index finger

Prostatic hand control system usually controls finger using pattern recognition from surface ElectroMyoGraphy (EMG) signal from the forearm, as most of the finger muscles are located in the forearm with small tendon connected to the skeletal system. However, pattern recognition is not the same method as muscles control the hand and finger, this lead to a limitation in postal and long-time training is required for each subject. All finger provide benefit to daily life but 50 percent of all hand functions are made possible by the thumb and index finger. Therefore, due to the complexity of forearm muscles, this research will limit on thumb and index finger. The method proposed using sEMG from muscles and simple movement of flexion and extension to estimate equilibrium position and stiffness with one degree of freedom (DOF) for each finger call musculoskeletal model. Our research interesting in using the same principle of the musculoskeletal model to estimate equilibrium position and stiffness of thumb and index finger separately by measure position in 3-dimensional space. The result will be confirmed by measure position, force and EMG of the subject simultaneously.

Multiple Sensors Based Hand Motion Recognition Using Adaptive Directed Acyclic Graph

The use of human hand motions as an effective way to interact with computers/robots, robot manipulation learning and prosthetic hand control is being researched in-depth. This paper proposes a novel and effective multiple sensor based hand motion capture and recognition system. Ten common predefined object grasp and manipulation tasks demonstrated by different subjects are recorded from both the human hand and object points of view. Three types of sensors, including electromyography, data glove and FingerTPS are applied to simultaneously capture the EMG signals, the finger angle trajectories, and the contact force. Recognising different grasp and manipulation tasks based on the combined signals is investigated by using an adaptive directed acyclic graph algorithm, and results of comparative experiments show the proposed system with a higher recognition rate compared with individual sensing technology, as well as other algorithms. The proposed framework contains abundant information from multimodal human hand motions with the multiple sensor techniques, and it is potentially applicable to applications in prosthetic hand control and artificial systems performing autonomous dexterous manipulation.

Hidrolik Preslerde Çift El Kumanda Sistemleriyle Güvenliğin İyileştirilmesi

Hidrolik Preslerde Çift El Kumanda Sistemleriyle Güvenliğin İyileştirilmesi

Bionic tracking method by hand &amp; eye-vergence visual servoing

Animals rotate their eyes to gaze at the target prey, enhancing the ability of measuring the distance to the target precisely for catching it. These animals, visual tracking includes the triangular eye-vergence control and their body’s motion control by visual servoing. The research aims to realize a bionic robot tracking performance, in which the body links moves together with eyes’ view orientation. This paper proposed a hand & eye-vergence dual control system which included two feedback loops: an outer loop for conventional visual servoing to direct a manipulator toward a target object and an inner loop for active motion control of binocular cameras to change the viewpoint along with the moving object to give an accurate and broad observation. This research also foused on how to compensate a fictional motion of the target seen by camera images in an eye-in-hand system, where the camera was fixed on the end-effector and moved together with the hand motion. A robust motion-feedforward (MFF) recognition method is proposed to compensate the fictional motion of the target based on the manipulator’s joint velocity, then the real motion of the target seen by camera images is extracted, which can improve the feedback image sensing unit to make the whole servoing system dynamically stable. The effectiveness of the proposed hand & eye-vergence visual servoing method is shown by tracking experiments using a 6-DoF robot manipulator and a 3-DoF binocular vision system.