Closed-loop Force Research Articles

Closed-Loop Force Control by Biorealistic Hand Prosthesis with Visual and Tactile Sensory Feedback.

The ability of a novel biorealistic hand prosthesis for grasp force control reveals improved neural compatibility between the human-prosthetic interaction. The primary purpose here was to validate a virtual training platform for amputee subjects and evaluate the respective roles of visual and tactile information in fundamental force control tasks. We developed a digital twin of tendon-driven prosthetic hand in the MuJoCo environment. Biorealistic controllers emulated a pair of antagonistic muscles controlling the index finger of the virtual hand by surface electromyographic (sEMG) signals from amputees' residual forearm muscles. Grasp force information was transmitted to amputees through evoked tactile sensation (ETS) feedback. Six forearm amputees participated in force tracking and holding tasks under different feedback conditions or using their intact hands. Test results showed that visual feedback played a predominant role than ETS feedback in force tracking and holding tasks. However, in the absence of visual feedback during the force holding task, ETS feedback significantly enhanced motor performance compared to feedforward control alone. Thus, ETS feedback still supplied reliable sensory information to facilitate amputee's ability of stable grasp force control. The effects of tactile and visual feedback on force control were subject-specific when both types of feedback were provided simultaneously. Amputees were able to integrate visual and tactile information to the biorealistic controllers and achieve a good sensorimotor performance in grasp force regulation. The virtual platform may provide a training paradigm for amputees to adapt the biorealistic hand controller and ETS feedback optimally.

A Sensory Soft Robotic Gripper Capable of Learning-Based Object Recognition and Force-Controlled Grasping

Soft robotic grippers possess high structural compliance and adaptability, allowing them to grasp objects with unknown and irregular shapes and sizes. To enable more dexterous manipulation, soft sensors that are similar in mechanical properties to common elastomer materials are desired to be integrated into soft grippers. In this paper, we develop ionic hydrogel-based strain and tactile sensors and integrate these sensors into a three-finger soft gripper for learning-based object recognition and force-controlled grasping. Such hydrogel-based sensors have excellent conductivity, high stretchability and toughness, good ambient stability, and unique antifreezing property; they can be readily attached to a soft gripper at desired locations for strain and tactile sensing. By using a deep-learning model, the sensory soft gripper is demonstrated to be capable of grasping and recognizing objects at both room and freezing temperatures, and achieving close to 100% recognition accuracy for ten typical objects. Moreover, the capacitive tactile feedback of the gripper is utilized to develop a closed-loop force controller and realize force-controlled grasping of fragile or highly deformable objects. A new slip detection and compensation strategy is also proposed and validated for the sensory gripper for adjusting the grasping force in real time upon detecting slippage. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The multimodal sensation of a soft robotic gripper could enrich its grasping functionalities and improve its manipulation performance. This research integrates novel antifreezing ionic hydrogel-based strain and tactile sensors into a three-finger soft robotic gripper for learning-based object recognition and force-controlled grasping. Constructed from a highly stretchable, ambient-stable, and antifreezing ionic hydrogel, the strain and tactile sensors can be readily integrated at the desired locations on the soft gripper, and can reliably operate at both ambient and freezing temperatures with excellent mechanical and electrical properties. Based on the feedback of the strain and tactile sensors, a deep learning model is employed to enable high-accuracy object recognition while grasping, which can be useful for manipulation in vision-free environments. Closed-loop force control and slip compensation strategies are also demonstrated for reliably grasping fragile/deformable objects and handling slip events during the manipulation of heavy objects. The sensory soft gripper and the associated object recognition and force control methods could find practical applications in a variety of robotic manipulation tasks.

Human–Robot Attachment System for Exoskeletons: Design and Performance Analysis

Exoskeleton robots found application in neurorehabilitation, telemanipulation, and power augmentation. The human–robot attachment system of an exoskeleton should transmit all the interaction forces while keeping the anatomical and robotic joint axes aligned. Existing attachment concepts were bounding the performance of modern exoskeletons due to insufficient stiffness for high-performance force control, time-consuming adaption processes, and/or bulkiness. Therefore, we developed an augmented attachment system for a recent fully actuated nine-degree-of-freedom upper limb exoskeleton. The proposed system was compared to a conventional solution in a case study with four participants. The proposed attachment system lowered the relative motion between the human and the robot under static loads for all defined landmarks by 45% on average. The occurrence of undesired contacts in the trials was mitigated by 74%, thus improving conditions for closed-loop force control. Furthermore, the proposed system adapted better to the user's anatomy facilitating more accurate alignment and less obstruction. On average, self-attachment took <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {43(8.3)}$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{s}$</tex-math></inline-formula> to don(doff). Thereby, the alignment of anatomic landmarks had typically less than 15 mm offset to a thorough expert alignment, making self-attachment eligible. The augmented attachment system and the insights gained by the case study are expected to enable improvement of the physical human–robot interaction of exoskeletons.

A Flexible and Fully Autonomous Breast Ultrasound Scanning System

The quality of breast ultrasound imaging is greatly affected by the contact force of the probe, which largely requires experienced sonographers to complete the clinical examination. We propose a flexible and fully autonomous ultrasound scanning system for breast ultrasound imaging. It consists of an ultrasound machine, a dual robotic arms system, a multi-structured light system, a human&#x2013;computer interaction system, and a flexible ultrasound probe clamping device (FUPCD). First, the dynamics model of the FUPCD was analyzed, and a closed-loop force control strategy was established. We then implemented an automatic scanning system. The hysteresis characteristics of the FUPCD and transient response of the force controller were experimentally verified. The system could keep the steady-state error less than <inline-formula> <tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 5<inline-formula> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> within 0.5 s. Second, the performance of the control system to maintain constant contact force at different scanning speeds (<inline-formula> <tex-math notation="LaTeX">$&lt;$</tex-math> </inline-formula>20 mm/s) was evaluated. In a series of comparative studies, the fluctuation range of the target contact force applied by the flexible automatic scanning was 10<inline-formula> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula>. The force control capability of the system was 30.84 times higher than that of traditional manual scanning, 18.48 times higher than that of flexible automatic scanning (FC off), and 24.3 times higher than that of rigid automatic scanning (FC on). Furthermore, the effectiveness of the FUPCD arc design and flexibility was verified. In addition, the change trend of the average grayscale level (GSL) of the ultrasound image was basically the same as the contact force, demonstrating that the stability and repeatability of the ultrasound image can be improved by controlling the stability of the contact force. In conclusion, the proposed system can simplify the workflow of breast ultrasound examination and improve the diagnostic capabilities of the ultrasound imaging system. <i>Note to Practitioners</i>&#x2014;The motivation of this study is to solve the problem of poor image reproducibility in breast ultrasound scanning, but the proposed system can also be applied to ultrasound scanning of other parts of the body. The position, direction, and contact force of the ultrasound probe affects the image quality and repeatability of the ultrasound, thereby affecting the diagnostic ability. Therefore, we propose and develop a flexible and fully autonomous breast ultrasound scanning system. We aimed to improve the autonomy and stability of the scanning process by designing end-to-end automated scanning strategies, flexible clamping devices, and closed-loop force control strategies. Among them, the fully automatic three-dimensional perception and trajectory planning can provide global fitting capabilities to different forms of breast surfaces and control the probe to maintain the best contact posture. At the same time, the fully automatic workflow reduces additional interference and reduces the complexity of the workflow. The flexible ultrasound probe clamping device improves the passive applicability to flexible tissues and reduces the resistance in the scanning process. The closed-loop force control strategy can adjust the contact force in real time. Experiments verified that the repeatability could be evaluated by contact force. A series of human&#x2013;machine comparison experiments verified the advancement and effectiveness of the system. In future research, we will further solve the problem of abnormalities and repeatability of ultrasound images acquired during the scanning process by combining the multi-modal feedback of images and forces.

An Improved Approach for Grasp Force Sensing and Control of Upper Limb Soft Robotic Prosthetics.

The following research proposes a closed loop force control system, which is implemented on a soft robotic prosthetic hand. The proposed system uses a force sensing approach that does not require any sensing elements to be embedded in the prosthetic's fingers, therefore maintaining their monolithic structural integrity, and subsequently decreasing the cost and manufacturing complexity. This is achieved by embedding an aluminum test specimen with a full bridge strain gauge circuit directly inside the actuator's housing rather than in the finger. The location of the test specimen is precisely at the location of the critical section of the bending moment on the actuator housing due to the tension in the driving tendon. Therefore, the resulting loadcell can acquire a signal proportional to the prosthetic's grasping force. A PI controller is implemented and tested using this force sensing approach. The experiment design includes a flexible test object, which serves to visually demonstrate the force controller's performance through the deformation that the test object experiences. Setpoints corresponding to "light", "medium", and "hard" grasps were tested with pinch, tripod, and full grasps and the results of these tests are documented in this manuscript. The developed controller was found to have an accuracy of ±2%. Additionally, the deformation of the test object increased proportionally with the given grasp force setpoint, with almost no deformation during the light grasp test, slight deformation during the medium grasp test, and relatively large deformation of the test object during the hard grasp test.

On a Vision-Based Manipulator Simulator

This paper presents a typical grasp–hold–release micromanipulation simulator based on MATLAB and Simulink. Closed-loop force and position control were implemented only based on a camera. The work was mainly focused on control performance improvements and GUI design. Different types of control strategies were investigated in both the position and force control processes. Finally, incremental PID and positional PID were adopted in the gripper position control and force control processes, respectively. The best performance of the gripper position control was the fast response of 0.3 s without overshoot and steady-state errors in the range of 10–40 Hz. The new camera control algorithm kept a big motion range (4.48 × 4.48 mm) and a high position resolution of 0.56 µm, with a high force resolution of 1.56 µN in the force control stage. The maximum error between the measured force and the real force was maintained below 4 µN. The steady-state error and the setting time were less than 1.2% and less than 1.5 s, respectively. A separate app, the Image Generation Simulator app, was developed to assist users in getting suitable initial coordinates, camera parameters, desired position resolutions, and force resolutions, which are packed as a standalone executable file. The main app can run the simulation model, debug, playback, and report simulation results, and calculate the calibration equation. Different initial coordinates, camera parameters, sample frequencies, controller parameters, and even controller types can be adjusted from this app.

3D printed smart elastomeric foam with force sensing and its integration with robotic gripper

Robotic grippers are extensively investigated as terminal actuators for gripping workpieces in robotic applications. Lately, implementing closed-loop force control for grippers to grip fragile workpieces securely is attractive. This paper aims to provide a dual-security solution combining gripping force control and stress-buffering capability. An integrated force-sensitive smart polydimethylsiloxane (PDMS) foam containing an embedded force-sensitive resistor (FSR) was designed and directly fabricated by 3D printing technology. The real-time force measurement was fed back into the control loop with proportional-integral (PI) rectification to precisely regulate the driving air pressure. In addition, the inherent stress plateau effect of the PDMS foam structure provides a constant stress buffer to absorb potential collision energy. The results show that the dual-security gripper with smart PDMS foam is able to precisely control the applied force and reliably grip fragile objects even in the presence of disturbances, enhancing the reliability of gripping applications. • Smart elastic foams with embedded force sensing capability are designed and 3D printed. • The printed smart elastic foams provide tunable mechanical and electrical properties. • The smart foam integrated with robotic gripper exhibits excellent stress buffering and closed-loop force feedback performance.

Force-Sensorless Identification and Classification of Tissue Biomechanical Parameters for Robot-Assisted Palpation.

The implementation of robotic systems for minimally invasive surgery and medical procedures is an active topic of research in recent years. One of the most common procedures is the palpation of soft tissues to identify their mechanical characteristics. In particular, it is very useful to identify the tissue's stiffness or equivalently its elasticity coefficient. However, this identification relies on the existence of a force sensor or a tactile sensor mounted at the tip of the robot, as well as on measuring the robot velocity. For some applications it would be desirable to identify the biomechanical characteristics of soft tissues without the need for a force/tactile nor velocity sensors. An estimation of such quantities can be obtained by a model-based state observer for which the inputs are only the robot joint positions and its commanded joint torques. The estimated velocities and forces can then be employed for closed-loop force control, force reflection, and mechanical parameters estimation. In this work, a closed-loop force control is proposed based on the estimated contact forces to avoid any tissue damage. Then, the information from the estimated forces and velocities is used in a least squares estimator of the mechanical parameters. Moreover, the estimated biomechanical parameters are employed in a Bayesian classifier to provide further help for the physician to make a diagnosis. We have found that a combination of the parameters of both linear and nonlinear viscoelastic models provide better classification results: 0% misclassifications against 50% when using a linear model, and 3.12% when using only a nonlinear model, for the case in which the samples have very similar mechanical properties.

Sensorized Reconfigurable Soft Robotic Gripper System for Automated Food Handling

This article presents a versatile soft robotic gripper system whereby its fingers can be reconfigured into different poses such as scoop, pinch, and claw. This allows the gripper to efficiently and safely handle food samples of different shapes, sizes and stiffness such as uncooked tofu and broccoli floret. The 3D-printed fingers were tested to last up to 25 000 cycles without significant changes in the curvature profile and force output profile. A benchmark experiment was conducted to evaluate the performance of the gripper and state-of-the-art gripping solutions. Capability of versatile soft gripper was optimized by integrating vision and tactile sensing facilities. An object recognition system was developed to identify food samples such as potato, broccoli, and sausage. Position and orientation of food samples were identified and pick-and-place pathway was optimized to achieve the best gripping performance. Flexible tactile sensors were integrated into soft fingers and closed-loop force feedback control system was developed. This allowed the gripper to automatically explore and select the most stable grip pose for different food samples. Integration of vision and force feedback system ensure that objects detected by the system would be firmly gripped. The reconfigurable soft robotic gripper system has been demonstrated to perform high-speed pick-and-place tasks (∼3 s per item) with object recognition system, making it a potential solution to food and grocery supply chain needs.

Artistic robotic pencil sketching using closed-loop force control

This study introduces an artistic robotic painting system. The main purpose was to use a force sensor combined with a 6-axis robotic arm to create pencil sketches. Compared with other painting robots, our system has additional tactile sensing that can overcome the problem of pencil wear and can simulate the feeling of human sketches. To provide the robot with the capability to create paintings, our experiment used neural style transfer technology to extract the content and style image features and recomposed them into a new artwork. The newly generated image was processed for edge detection and further layered using methods, such as k-means clustering and image grayscale quantisation, resulting in a contour layer and multiple background layers. We carried out path planning of different layers according to the layering result, and we sorted the pixels by finding the shortest distance between them to plan a feasible path for the robotic arm at a higher speed. Finally, in the robotic arm painting experiment, we conducted two types of art creation: Chinese brush painting and pencil sketching. In the first type, we used position control to create a Chinese brush painting, and in the second type, we used a proportional-integral-derivative (PID) controller for force control to create pencil sketching. The experimental results show the validity of the proposed techniques in artistic robotic painting, both in Chinese brush painting and pencil sketching, with painting times of less than 30 min.

Closing the Control Loop with Time-Variant Embedded Soft Sensors and Recurrent Neural Networks.

Embedded soft sensors can significantly impact the design and control of soft-bodied robots. Although there have been considerable advances in technology behind these novel sensing materials, their application in real-world tasks, especially in closed-loop control tasks, has been severely limited. This is mainly because of the challenge involved with modeling a nonlinear time-variant sensor embedded in a complex soft-bodied system. This article presents a learning-based approach for closed-loop force control with embedded soft sensors and recurrent neural networks (RNNs). We present learning protocols for training a class of RNNs called long short-term memory (LSTM) that allows us to develop accurate and robust state estimation models of these complex dynamical systems within a short period of time. Using this model, we develop a simple feedback force controller for a soft anthropomorphic finger even with significant drift and hysteresis in our feedback signal. Simulation and experimental studies are conducted to analyze the capabilities and generalizability of the control architecture. Experimentally, we are able to develop a closed-loop controller with a control frequency of 25 Hz and an average accuracy of 0.17 N. Our results indicate that current soft sensing technologies can already be used in real-world applications with the aid of machine learning techniques and an appropriate training methodology.

Passivity of Series Elastic Actuation Under Model Reference Force Control During Null Impedance Rendering.

Series elastic actuation (SEA) is an interaction control paradigm that relies on a compliant force sensing element and utilizes the model of this compliant dynamics in closed-loop force control. We present sufficient conditions for passivity of SEA under model reference force control (MRFC) during null impedance rendering. We prove that overestimation of robot inertia and underestimation of the stiffness of the series elastic element can ensure coupled stability of interaction for SEA under MRFC during null impedance rendering, as long as a lower limit on damping compensation is not violated. We experimentally verify the passivity bounds and demonstrate the null impedance rendering performance of MRFC.

Electrically assisted pulse forming using closed-loop force control

The direct application of an electric current to a metallic workpiece during a manufacturing process, more commonly known as electrically-assisted manufacturing (EAM), produces many beneficial effects. During forming, significant flow stress reductions can be achieved. While there is much literature characterizing the effects of an electric current on the mechanical properties of materials during deformation, few feasible control systems for use in industrial processing have been developed. One such method is developed and tested herein using tensile testing in which electric current is triggered once a user-defined stress level is reached. The current then remains on until the stress reaches a desired minimum level. This cycle repeats until fracture occurs. The influence of several process parameters is discussed and an empirical formulation for determining the pulse frequency and the required energy per pulse is developed. This control method was found to be successful for forming metals while not exceeding a desired stress level. Furthermore, its application towards industrial use is also discussed.

Design and Evaluation of a Haptic Robot-Assisted Catheter Operating System With Collision Protection Function

In vascular interventional surgery, experienced physicians rely on proximal force sensing to detect the collision and reduce vascular injury caused by surgical tools. However, in robot-assisted tele-interventional surgery (RATIS), providing high-precision force feedback to physician is still a main challenge. In this paper, a haptic robot-assisted catheter operating system with a novel spring-based haptic force interface was developed. With a closed loop force adjustment system, the haptic force interface can provide accuracy force feedback. Moreover, a collision protection function with a proximal-force-based collision detection algorithm was proposed to improve surgical safety. In case of no collision, transparency of the teleoperated system is realized; in case of collision, the provided haptic force will be amplified. The results demonstrated the usability of the developed haptic robot-assisted catheter operating system with collision protection function.

Physical Human-Robot Interaction Using HandsOn-SEA: An Educational Robotic Platform With Series Elastic Actuation.

For gaining proficiency in physical human-robot interactions, it is crucial for engineering students to be provided with the opportunity to gain hands-on experience with robotic devices that feature kinesthetic feedback. In this article, we propose HandsOn-SEA, a low-cost, single degree-of-freedom, force-controlled educational robot with series elastic actuation and introduce educational modules for the use of the device to allow students to experience the fundamental performance trade-offs inherent in robotic systems. The novelty of the proposed robot is due to the deliberate introduction of a compliant element between the actuator and the handle, whose deflections are measured to perform closed-loop force control. As an admittance-type robot, HandsOn-SEA relies on force feedback to achieve the desired level of safety and transparency and complements the existing impedance-type educational robots. We present the integration of HandsOn-SEA into the robotics curriculum, by providing guidelines for its use in a senior level robotics course, to help students experience the challenges involved in the synergistic design and control of robotic devices. We systematically evaluate the efficacy of the device in a robotics course delivered for five semesters and provide evidence that HandsOn-SEA is effective in instilling fundamental concepts and trade-offs in the design and control of robotic devices.

EEG-FES-Force-MMG closed-loop control systems of a volunteer with paraplegia considering motor imagery with fatigue recognition and automatic shut-off

People with spinal cord injury (SCI) may have their paralyzed muscles activated through functional electrical stimulation (FES). This neuromodulation technique has been used frequently to assist in controlling the movement of neuroprostheses. Electroencephalography (EEG) is able to trigger FES from the motor imagery captured through movements intentions. This research presents an isometric neuromuscular control system of the quadriceps muscle activated by EEG. Additionally, the detection of neuromuscular fatigue through the mechanomyography (MMG) technique is proposed, which is used to shut-off the system. A pilot study was performed on a chronic 42-year-old paraplegic (no voluntary contraction below the spinal cord injury level T8) volunteer. To do so, the training procedure for EEG signals was divided into the calibration and feedback phases. In the first one, four EEG channels and the Linear Discriminant Analysis (LDA) classifier were used to classify between motor imagery of the right leg and remain at rest. The maximum accuracy obtained during this stage was 77%. In the feedback phase, the volunteer was able to activate FES through brain–computer interface (BCI) in two tests (defined as Test 1 and Test 2) with the same procedure in different days. The closed-loop force control was tested with the setpoint of 2 kgf and 2.5 kgf and proved to be stable on both tests, successfully turning off the FES using the fatigue threshold from the MMG signal, being the main contribution of this work.

The Neural Representation of Force across Grasp Types in Motor Cortex of Humans with Tetraplegia.

Intracortical brain-computer interfaces (iBCIs) have the potential to restore hand grasping and object interaction to individuals with tetraplegia. Optimal grasping and object interaction require simultaneous production of both force and grasp outputs. However, since overlapping neural populations are modulated by both parameters, grasp type could affect how well forces are decoded from motor cortex in a closed-loop force iBCI. Therefore, this work quantified the neural representation and offline decoding performance of discrete hand grasps and force levels in two human participants with tetraplegia. Participants attempted to produce three discrete forces (light, medium, hard) using up to five hand grasp configurations. A two-way Welch ANOVA was implemented on multiunit neural features to assess their modulation to force and grasp. Demixed principal component analysis (dPCA) was used to assess for population-level tuning to force and grasp and to predict these parameters from neural activity. Three major findings emerged from this work: (1) force information was neurally represented and could be decoded across multiple hand grasps (and, in one participant, across attempted elbow extension as well); (2) grasp type affected force representation within multiunit neural features and offline force classification accuracy; and (3) grasp was classified more accurately and had greater population-level representation than force. These findings suggest that force and grasp have both independent and interacting representations within cortex, and that incorporating force control into real-time iBCI systems is feasible across multiple hand grasps if the decoder also accounts for grasp type.

Design and Validation of a Two-Degree-of-Freedom Powered Ankle-Foot Orthosis with Two Pneumatic Artificial Muscles

Powered ankle-foot orthosis (PAFO) is a field of wearable robotics that improves the lives of people of old age or with physical impairments by aiding in the wearer's ankle joint movements. Most of the PAFOs developed thus far offer only one degree-of-freedom (dof), which uses the talocrural joint alone as the axis of rotation, where the emphasis is on moving forward. However, because this type of wearable robotics has evolved, developing PAFOs for functional rehabilitation has become necessary. This enhances the quality of walking rather than providing simple rehabilitation. The subtalar joint is responsible for the rotation of the inversion and eversion of the ankle, enabling balanced walking in humans, and is an important part of balance training for elderly people or stroke patient rehabilitation. Therefore, we developed a 2-dof PAFO that uses a calculated spatial formula of the subtalar joint based on anatomical data. In recognition of the fact that the pneumatic artificial muscle (PAM) can be used within the contracting range alone because of the instinct of the PAM, we analyzed the workspace of the fabricated PAFO through kinematic analysis and verified the possibility of using the PAFO during the gait cycle. Experiments were also conducted on the closed-loop force frequency response using a sliding mode control of the solenoid valve to validate the control characteristics. Lastly, clinical experiments with healthy subjects were conducted for validation in wearing conditions.

Vision-based micro-manipulations in simulation

Manipulating micro-objects is needed in various applications such as micro-assembly, minimally invasive surgery and micro-organism manipulation, which refers to micro-manipulation in this paper. During such operations, controlling the gripping force is mandatory, in order to prevent damage or the breaking of the micro-parts being grasped and handled, given that they are often very fragile. The force sensing is frequently carried out by sensors embedded in the micro-gripper, but this produces strong design and manufacturing constraints consequently. In this paper, vision-based manipulation of micro-parts is investigated. Moreover, instead of working with real setups, this paper investigates micro-manipulations carried out in simulation. Working on simulators instead of real setups allows to easily and quickly implement different vision-based force measurement methods, different force control strategies and even different micro-gripper systems. Using the simulators, we are also able to compare the performance and limitations achieved in each manipulation, and to study the impact of certain parameters on manipulation performance. MATLAB-based simulators were designed in this paper. A vision-based force measurement method was then implemented and integrated into a closed-loop force control. In the end, successful grasp-hold-release manipulations of micro-parts (400–600 µm size) were achieved with a force-sensing resolution of less than 6 µN. Moreover, the manipulation was observed to be successful when softness variation is introduced on the micro-object (± 20% around the average value), and when pictures are degraded because of noise and blur effects.

Upper limb movement characteristics of children and youth with dyskinetic cerebral palsy – A sensor approach

People with spinal cord injury (SCI) may have their paralyzed muscles activated through functional electrical stimulation (FES). This neuromodulation technique has been used frequently to assist in controlling the movement of neuroprostheses. Electroencephalography (EEG) is able to trigger FES from the motor imagery captured through movements intentions. This research presents an isometric neuromuscular control system of the quadriceps muscle activated by EEG. Additionally, the detection of neuromuscular fatigue through the mechanomyography (MMG) technique is proposed, which is used to shut-off the system. A pilot study was performed on a chronic 42-year-old paraplegic (no voluntary contraction below the spinal cord injury level T8) volunteer. To do so, the training procedure for EEG signals was divided into the calibration and feedback phases. In the first one, four EEG channels and the Linear Discriminant Analysis (LDA) classifier were used to classify between motor imagery of the right leg and remain at rest. The maximum accuracy obtained during this stage was 77%. In the feedback phase, the volunteer was able to activate FES through brain–computer interface (BCI) in two tests (defined as Test 1 and Test 2) with the same procedure in different days. The closed-loop force control was tested with the setpoint of 2 kgf and 2.5 kgf and proved to be stable on both tests, successfully turning off the FES using the fatigue threshold from the MMG signal, being the main contribution of this work.