Transcranial magnetic stimulation is a powerful and non-invasive technique to stimulate nerve cells in the brain to improve symptoms of depression. It needs accurate contact force and positioning of a stimulating coil to offer effective treatment. Conventionally, a neurologist has to manually place the coil on the patient’s head at a predefined position and orientation throughout the 30-to-45-minute treatment. This paper consequently highlights the design of a robot control framework used for the transcranial magnetic stimulation. The hybrid Proportional-Derivative position and Proportional–Integral force control scheme was successfully implemented on the effective robotic system. The system can track the head movement by using the Kinect camera and simultaneously maintain the contact force between the magnetic coil and the head in real-time. The optimal gain tuning technique was adopted. It delivered PD gains of $K_p$ = 75.0, $K_d$=2.0 and PI gains of $K_p$ = 0.09 and $K_i$ = 0.011, respectively. This provides a faster response of the robot end-effector with a small interactive force error. The experimental results show that when the head has translational and rotational motions, the robot-assisted transcranial magnetic stimulation control system performs with a safe and reliable manner validated by the quantitative measurement. GRAPHICAL ABSTRACT

One of the challenging aspects of robotics research is to successfully establish a human-like behavioural control strategy for human–robot handover, since a robotic controller is further complicated by the dynamic nature of the human response. This paper consequently highlights the development of an appropriate set of behaviour-based control for robot-to-human object handover by first understanding an equivalent human–human handover. The optimized hybrid position and impedance control was implemented to ensure good stability, adaptability and comfort of the robot in the object handover tasks. Moreover, a questionnaire technique was employed to gather information from the participants concerning their evaluations of the developed control system. The results demonstrate that the quantitative measurement of performance of the human-inspired control strategy can be considered acceptable for seamless human–robot handovers. This also provided significant satisfaction with the overall control performance in the robotic control system, in which the robot can dexterously pass the object to the receiver in a timely and natural manner without the risk of harm or injury by the robot. Furthermore, the survey responses were in agreement with the parallel test outcomes, demonstrating significant satisfaction with the overall performance of the robot–human interaction, as measured by an average rating of 4.20 on a five-point scale.

Different types of noise contaminating the surface electromyogram (EMG) signal may degrade the recognition performance. For noise removal, the type of noise has to first be identified. In this paper, we propose a real-time efficient system for identifying a clean EMG signal and noisy EMG signals contaminated with any one of the following three types of noise: electrocardiogram interference, spike noise, and power line interference. Two statistical descriptors, kurtosis and skewness, are used as input features for the cascading quadratic discriminant analysis classifier. An efficient simplification of kurtosis and skewness calculations that can reduce computation time and memory storage is proposed. The experimental results from the real-time system based on an ATmega 2560 microcontroller demonstrate that the kurtosis and skewness values show root mean square errors between the traditional and proposed efficient techniques of 0.08 and 0.09, respectively. The identification accuracy with five-fold cross-validation resulting from the quadratic discriminant analysis classifier is 96.00%.

Recent research results in robotics currently make possible the close interaction between humans and robots. This research focuses on the design and development of an appropriate set of behaviour control strategies for robot-to-human object handover by first understanding how an equivalent human-human object handover can be used to establish a framework for a robotic behaviour-based approach. To achieve this goal, a real-world human-to-human object transferring task has been carried out in order to understand the dynamic and kinematic behavioural characteristics of the haptic interaction between two human participants. The profiles of interactive forces between the giver and receiver were measured as a function of time, and how they are modulated while performing the cooperative tasks, was evaluated. Based on key features as proposed, these can be subsequently used to design and develop the conceptual guideline for a human-like behaviour-based service robot. Position-based force control using proportional plus integral (PI) and fuzzy logic control (FLC) algorithms were adopted to control the robot end effector position and interactive force in real-time. The results allowed a comparison of the PI and FLC control strategies. It can be concluded that the quantitative measurement of the controlled robot performance is close to that of the human and can be considered acceptable for human-robot interaction. The effective object transferring tasks provide robot to be able to successfully pass the object to the human in a safe, reliable and timely manner. However, after careful analysis with regard the test results, the control based FLC scheme was shown to be slightly superior to PI control by actively compensating for the dynamics in the non-linear system and demonstrated better overall performance and stability.

This research focuses on the development of the conceptual frameworks of human-human interaction applied for a robotic behaviour-based approach for safe physical human-robot interaction. The control has been constructed based on understanding the dynamic and kinematic behavioural characteristics of how two humans pass an object to each other. This has enabled a KR-16-KUKA robot to naturally interact with a human so as to facilitate the dexterous transfer of an object in an effective manner. Implicit force control based on Proportional Integral and Fuzzy Logic Control which allows the robot end effector’s trajectory to be moderated based on the applied force in real-time was adopted. The experimental results have confirmed that the quantitative performance of the force-controlled robot is close to that of the human and can be considered acceptable for human-robot interaction. Furthermore, the control based Fuzzy Logic Control was shown to be slightly superior performance compared to Proportional Integral control.

Stroke is a primary cause of death and the leading cause of permanent disability in adults. There are many stroke survivors, who live with a variety of levels of disability and always need rehabilitation activities on daily basis. Several studies have reported that usage of rehabilitation robotic devices shows the better improvement outcomes in upper-limb stroke patients than the conventional therapy-nurses or therapists actively help patients with exercise-based rehabilitation. This research focuses on the development of an autonomous robotic trainer designed to guide a stroke patient through an upper-limb rehabilitation task. The robotic device was designed and developed to automate the reaching exercise as mentioned. The designed robotic system is made up of a four-wheel omni-directional mobile robot, an ATI Gamma multi-axis force/torque sensor used to measure contact force and a microcontroller real-time operating system. Proportional plus Integral control was adapted to control the overall performance and stability of the autonomous assistive robot. External force control was successfully implemented to establish the behavioral control strategy for the robot force and velocity control scheme. In summary, the experimental results indicated satisfactorily stable performance of the robot force and velocity control can be considered acceptable. The gain tuning for proportional integral (PI) velocity control algorithms was suitably estimated using the Ziegler-Nichols method in which the optimized proportional and integral gains are 0.45 and 0.11, respectively. Additionally, the PI external force control gains were experimentally tuned using the trial and error method based on a set of experiments which allow a human participant moves the robot along the constrained circular path whilst attempting to minimize the radial force. The performance was analyzed based on the root mean square error (E_RMS) of the radial forces, in which the lower the variation in radial forces, the better the performance of the system. The outstanding performance of the tests as specified by the E_RMS of the radial force was observed with proportional and integral gains of K_p = 0.7 and K_i = 0.75, respectively.