A Model that Predicts the Material Recognition Performance of Thermal Tactile Sensing

Tactile sensing can enable a robot to infer properties of its surroundings, such as the material of an object. Heat transfer based sensing can be used for material recognition due to differences in the thermal properties of materials. While data-driven methods have shown promise for this recognition problem, many factors can influence performance, including sensor noise, the initial temperatures of the sensor and the object, the thermal effusivities of the materials, and the duration of contact. We present a physics-based mathematical model that predicts material recognition performance given these factors. Our model uses semi-infinite solids and a statistical method to calculate an F1 score for the binary material recognition. We evaluated our method using simulated contact with 69 materials and data collected by a real robot with 12 materials. Our model predicted the material recognition performance of support vector machine (SVM) with 96% accuracy for the simulated data, with 92% accuracy for real-world data with constant initial sensor temperatures, and with 91% accuracy for real-world data with varied initial sensor temperatures. Using our model, we also provide insight into the roles of various factors on recognition performance, such as the temperature difference between the sensor and the object. Overall, our results suggest that our model could be used to help design better thermal sensors for robots and enable robots to use them more effectively.Comment: This article is currently under review for possible publicatio

Robot Assisted Shoulder Rehabilitation: Biomechanical Modelling, Design and Performance Evaluation

The upper limb rehabilitation robots have made it possible to improve the motor recovery in stroke survivors while reducing the burden on physical therapists. Compared to manual arm training, robot-supported training can be more intensive, of longer duration, repetitive and task-oriented. To be aligned with the most biomechanically complex joint of human body, the shoulder, specific considerations have to be made in the design of robotic shoulder exoskeletons. It is important to assist all shoulder degrees-of-freedom (DOFs) when implementing robotic exoskeletons for rehabilitation purposes to increase the range of motion (ROM) and avoid any joint axes misalignments between the robot and human’s shoulder that cause undesirable interaction forces and discomfort to the user. The main objective of this work is to design a safe and a robotic exoskeleton for shoulder rehabilitation with physiologically correct movements, lightweight modules, self-alignment characteristics and large workspace. To achieve this goal a comprehensive review of the existing shoulder rehabilitation exoskeletons is conducted first to outline their main advantages and disadvantages, drawbacks and limitations. The research has then focused on biomechanics of the human shoulder which is studied in detail using robotic analysis techniques, i.e. the human shoulder is modelled as a mechanism. The coupled constrained structure of the robotic exoskeleton connected to a human shoulder is considered as a hybrid human-robot mechanism to solve the problem of joint axes misalignments. Finally, a real-scale prototype of the robotic shoulder rehabilitation exoskeleton was built to test its operation and its ability for shoulder rehabilitation

Some aspects of human performance in a Human Adaptive Mechatronics (HAM) system

An interest in developing the intelligent machine system that works in conjunction with human has been growing rapidly in recent years. A number of studies were conducted to shed light on how to design an interactive, adaptive and assistive machine system to serve a wide range of purposes including commonly seen ones like training, manufacturing and rehabilitation. In the year 2003, Human Adaptive Mechatronics (HAM) was proposed to resolve these issues. According to past research, the focus is predominantly on evaluation of human skill rather than human performance and that is the reason why intensive training and selection of suitable human subjects for those experiments were required. As a result, the pattern and state of control motion are of critical concern for these works. In this research, a focus on human skill is shifted to human performance instead due to its proneness to negligence and lack of reflection on actual work quality. Human performance or Human Performance Index (HPI) is defined to consist of speed and accuracy characteristics according to a well-renowned speed-accuracy trade-off or Fitts’ Law. Speed and accuracy characteristics are collectively referred to as speed and accuracy criteria with corresponding contributors referred to as speed and accuracy variables respectively. This research aims at proving a validity of the HPI concept for the systems with different architecture or the one with and without hardware elements. A direct use of system output logged from the operating field is considered the main method of HPI computation, which is referred to as a non-model approach in this thesis. To ensure the validity of these results, they are compared against a model-based approach based on System Identification theory. Its name is due to being involved with a derivation of mathematical equation for human operator and extraction of performance variables. Certain steps are required to match the processing outlined in that of non-model approach. Some human operators with complicated output patterns are inaccurately derived and explained by the ARX models

Implementation of a sensorized neonatal head model for gynechological training

During labor it is very important to know the exact position and orientation of the fetal head when descending the birth canal. Indeed, incorrect evaluations may lead to dangerous situations for both the infant and the mother. Usually, gynecologists and midwives rely on their experience to determine the head position and to evaluate the risk level of each delivery. In this context, it is essential to train new physicians and midwives to correctly manage different types of delivery. Here, we present the design and implementation of a realistic sensorized neonatal head that could be used on low-cost birth simulators for training and evaluation of residents and midwifery students

Analytical and Experimental Analysis of Magnetorheological Elastomers

Many engineering applications ranging from robotic joints to shock and vibration mitigation can benefit by incorporating components with variable stiffness. In addition, variable stiffness structures can provide haptic feedback (the sense of touch) to the user. In this work, it is proposed to study Magnetorheological Elastomers (MRE), where iron particles within the elastomer compound develop a dipole interaction energy, to be used in a device for haptic feedback. A novel feature of this MRE device is to introduce a field-induced variable shear modulus bias via a permanent magnet and using a current input to the electromagnetic control coil to change the modulus of the elastomer in both directions (softer or harder). In this preliminary work, both computational and experimental results of the proposed MRE design are presented. The design is created in COMSOL to verify that the magnetic field is in the desired direction. MRE was fabricated and characterized using a Bose Dynamic Mechanical Analyzer for the shear modulus. Using this information, it is possible to know how the MRE will react in magnetic fields within the haptic feedback device. Additionally, a model for an MRE is developed in a multi-physics COMSOL program that is linked to a MATLAB function that predicts the shear modulus and incorporates it into the material properties to best simulate the MRE\u27s ability to change shear modulus