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This study aims to develop and evaluate a unique 3D haptic system utilizing a gimbal mechanism and three Magneto-Rheological Brakes (MRBs). The research begins with a comprehensive literature review to propose a configuration for a 3D haptic joystick with force feedback, employing MRF actuators. The gimbal mechanism integrates the three MRBs to provide force feedback for rotational movement along the X, Y, and Z axes. The design and simulation of the MRBs are carried out using the finite element method and the Bingham plastic rheological model, with a focus on minimizing mass and production costs. To address the objective optimization problem associated with the MRBs, Particle Swarm Optimization is applied. Subsequently, a physical prototype of the 3D haptic joystick is constructed, and an evaluation is conducted to assess the feedback force. This research also paves the way for future investigations into force feedback in remote control, particularly within the context of the Master-Slave system.

Writing skills play a major part in communication, whether in a typing or in a handwriting scenario. Thus, a decrease or even loss of these skills can be a major setback. The system described herein aims to promote handwriting rehabilitation through haptic guidance and analysis of characters written using a haptic joystick. The first goal of this work is the creation of a system that encourages a user to write better through repetition, and thus have an impact on the proprioceptive system. In the second part of this work, there is a character analysis model evolution. Character analysis is performed by digit or letter classification and quantification of written characters quality. As such, different methods are studied and evaluated to achieve the best classification accuracy. Regarding handwritten characters classification, the pre-eminent procedure for digits consists in the use of Histogram of Oriented Gradients coupled with a multiclass Support Vector Machine (HOG-SVM) whereas for letters the strategy involves using a Convolutional Neural Network (CNN). For handwriting quality quantification, Dynamic Time Warping (DTW) was performed between the written character and a reference image of the same character. Finally, haptic guidance impact in a handwriting retraining in rehabilitation scenario is evaluated.

This paper presents a haptic-based assistance system(AS) for powerwheelchair users designed using the model-based shared control approach. The idea is to combine robust control with a high-level driving supervisor in order to successfully share control authority between thewheelchair user and the assistance system. This shared control strategy is composed of two parts, namely an operational part and a tactical part. Through the haptic joystick interface, this assistance system aims to reduce user's effort when manipulating the joystick, guide the user to avoid any potential collisions, and maintain the active participation of the user in driving the wheelchair. In the operational part, an optimal Takagi-Sugeno fuzzy control approach is proposed to deal with the time-varying user's intention represented by his desired longitudinal and angular position errors and velocities and hand torques. The control design is formulated as an LMI optimization problemwhich can be easily solved with numerical solvers. Two unknown input observers for Takagi-Sugeno fuzzy model have been designed to estimate the user's intention in order to generate an assistance torque via a haptic force feedback joystick. The control supervisor in the tactical part, aims to provide a decision-making signal which allows for the conflict management based on the user hand torque estimation. A specific algorithm has been developed to solve the conflict between the user's desired actions and the suggestions fromthe assistance system to ensure the user remains the final decision-maker. Experimental results show the effectiveness and the validity of the proposed assistance system. [ABSTRACT FROM AUTHOR]

An impedance matching control framework between a human and a haptic joystick for long-term teleoperation is proposed in this research. An impedance model of the human arm is established analyzing the characteristics of human perception, decision, and action. The coefficients of the human arm's impedance have been identified using a least squares method. The human arm's impedance matching algorithm generates a corresponding motion vector for the human arm, which is determined by the interaction force measured by a force/torque sensor considering the impedance modeling of the human arm. The impedance control has been adopted for the haptic joystick to match the desired impedance to that of the human arm, which is aimed to minimize the energy consumption of the human arm for long-term teleoperation. By minimizing the fatigue of the operator, the remote control accuracy of the teleoperation can be improved. A PD control with gravity compensation algorithm has been adopted to maintain desired trajectory for the joystick by the operator more conveniently. The effectiveness of matching control has been demonstrated by trajectory following experiments for a mobile robot. [ABSTRACT FROM AUTHOR]

Cutaneous sensory feedback can be used to provide additional sensory cues to a person performing a motor task where vision is a dominant feedback signal. A haptic joystick has been widely used to guide a user by providing force feedback. However, the benefit of providing force feedback is still debatable due to performance dependency on factors such as the user's skill-level, task difficulty. Meanwhile, recent studies have shown the feasibility of improving a motor task performance by providing skin-stretch feedback. Therefore, a combination of two aforementioned feedback types is deemed to be promising to promote synergistic effects to consistently improve the person's motor performance. In this study, we aimed at identifying the effect of the combined haptic and skin-stretch feedbacks on the aged person's driving motor performance. For the experiment, 15 healthy elderly subjects (age 72.8 ± 6.6 years) were recruited and were instructed to drive a virtual power-wheelchair through four different courses with obstacles. Four augmented sensory feedback conditions were tested: no feedback, force feedback, skin-stretch feedback, and a combination of both force and skin-stretch feedbacks. While the haptic force was provided to the hand by the joystick, the skin-stretch was provided to the steering forearm by a custom-designed wearable skin-stretch device. We tested two hypotheses: (i) an elderly individual's motor control would benefit from receiving information about a desired trajectory from multiple sensory feedback sources, and (ii) the benefit does not depend on task difficulty. Various metrics related to skills and safety were used to evaluate the control performance. Repeated measure ANOVA was performed for those metrics with two factors: task scenario and the type of the augmented sensory feedback. The results revealed that elderly subjects' control performance significantly improved when the combined feedback of both haptic force and skin-stretch feedback was applied. The proposed approach suggest the feasibility to improve people's task performance by the synergistic effects of multiple augmented sensory feedback modalities.

This paper presents a virtual excavator simulator featuring hardware-in-the loop-simulation (HILS) technology and haptic joysticks. First, the technical concept of the virtual excavator simulator is proposed. Then, the mathematical relations describing the behavior of the excavator are derived for the software environment. Next, for reflecting the nonlinear and dynamic characteristics of the hydraulic system in the excavator, the simulation software is integrated with the hydraulic system hardware. In addition, for improving the interaction performance between the operator and the simulator, MR fluid actuator based haptic joysticks are employed. The experimental performance evaluation verified that the proposed concept of the virtual excavator simulator is effective and practical from the viewpoint of the reality improvement. [ABSTRACT FROM AUTHOR]

The article proposes a methodology to identify requirements for warning systems aimed at reducing the risk of rollover and runover accidents while moving loads with front-loading tractors. It discusses the implementation of the methodology during the development of a risk mitigation system for farm factors as part of the Italian public funded project PRO-TRACT. The hierarchical task analysis of high-risk situations for farm tractors and the application of function allocation to the riskiest tasks are discussed.

Cutaneous sensory feedback can be used to provide additional sensory cues to a person performing a motor task where vision is a dominant feedback signal. A haptic joystick has been widely used to guide a user by providing force feedback. However, the benefit of providing force feedback is still debatable due to performance dependency on factors such as the user's skill-level, task difficulty. Meanwhile, recent studies have shown the feasibility of improving a motor task performance by providing skin-stretch feedback. Therefore, a combination of two aforementioned feedback types is deemed to be promising to promote synergistic effects to consistently improve the person's motor performance. In this study, we aimed at identifying the effect of the combined haptic and skin-stretch feedbacks on the aged person's driving motor performance. For the experiment, 15 healthy elderly subjects (age 72.8 ± 6.6 years) were recruited and were instructed to drive a virtual power-wheelchair through four different courses with obstacles. Four augmented sensory feedback conditions were tested: no feedback, force feedback, skin-stretch feedback, and a combination of both force and skin-stretch feedbacks. While the haptic force was provided to the hand by the joystick, the skin-stretch was provided to the steering forearm by a custom-designed wearable skin-stretch device. We tested two hypotheses: (i) an elderly individual's motor control would benefit from receiving information about a desired trajectory from multiple sensory feedback sources, and (ii) the benefit does not depend on task difficulty. Various metrics related to skills and safety were used to evaluate the control performance. Repeated measure ANOVA was performed for those metrics with two factors: task scenario and the type of the augmented sensory feedback. The results revealed that elderly subjects' control performance significantly improved when the combined feedback of both haptic force and skin-stretch feedback was applied. The proposed approach suggest the feasibility to improve people's task performance by the synergistic effects of multiple augmented sensory feedback modalities.