Soft Biomimetic Finger with Tactile Sensing and Sensory Feedback Capabilities

The compliant nature of soft fingers allows for safe and dexterous manipulation of objects by humans in an unstructured environment. A soft prosthetic finger design with tactile sensing capabilities for texture discrimination and subsequent sensory stimulation has the potential to create a more natural experience for an amputee. In this work, a pneumatically actuated soft biomimetic finger is integrated with a textile neuromorphic tactile sensor array for a texture discrimination task. The tactile sensor outputs were converted into neuromorphic spike trains, which emulate the firing pattern of biological mechanoreceptors. Spike-based features from each taxel compressed the information and were then used as inputs for the support vector machine (SVM) classifier to differentiate the textures. Our soft biomimetic finger with neuromorphic encoding was able to achieve an average overall classification accuracy of 99.57% over sixteen independent parameters when tested on thirteen standardized textured surfaces. The sixteen parameters were the combination of four angles of flexion of the soft finger and four speeds of palpation. To aid in the perception of more natural objects and their manipulation, subjects were provided with transcutaneous electrical nerve stimulation (TENS) to convey a subset of four textures with varied textural information. Three able-bodied subjects successfully distinguished two or three textures with the applied stimuli. This work paves the way for a more human-like prosthesis through a soft biomimetic finger with texture discrimination capabilities using neuromorphic techniques that provides sensory feedback; furthermore, texture feedback has the potential to enhance the user experience when interacting with their surroundings. Additionally, this work showed that an inexpensive, soft biomimetic finger combined with a flexible tactile sensor array can potentially help users perceive their environment better

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

Anthropomorphic Twisted String-Actuated Soft Robotic Gripper with Tendon-Based Stiffening

Realizing high-performance soft robotic grippers is challenging because of the inherent limitations of the soft actuators and artificial muscles that drive them, including low force output, small actuation range, and poor compactness. Despite advances in this area, realizing compact soft grippers with high dexterity and force output is still challenging. This paper explores twisted string actuators (TSAs) to drive a soft robotic gripper. TSAs have been used in numerous robotic applications, but their inclusion in soft robots has been limited. The proposed design of the gripper was inspired by the human hand. Tunable stiffness was implemented in the fingers with antagonistic TSAs. The fingers' bending angles, actuation speed, blocked force output, and stiffness tuning were experimentally characterized. The gripper achieved a score of 6 on the Kapandji test and recreated 31 of the 33 grasps of the Feix GRASP taxonomy. It exhibited a maximum grasping force of 72 N, which was almost 13 times its own weight. A comparison study revealed that the proposed gripper exhibited equivalent or superior performance compared to other similar soft grippers.Comment: 19 pages, 15 figure

Down-Conditioning of Soleus Reflex Activity using Mechanical Stimuli and EMG Biofeedback

Spasticity is a common syndrome caused by various brain and neural injuries, which can severely impair walking ability and functional independence. To improve functional independence, conditioning protocols are available aimed at reducing spasticity by facilitating spinal neuroplasticity. This down-conditioning can be performed using different types of stimuli, electrical or mechanical, and reflex activity measures, EMG or impedance, used as biofeedback variable. Still, current results on effectiveness of these conditioning protocols are incomplete, making comparisons difficult. We aimed to show the within-session task- dependent and across-session long-term adaptation of a conditioning protocol based on mechanical stimuli and EMG biofeedback. However, in contrast to literature, preliminary results show that subjects were unable to successfully obtain task-dependent modulation of their soleus short-latency stretch reflex magnitude

A review : a comprehensive review of soft and rigid wearable rehabilitation and assistive devices with a focus on the shoulder joint

The importance of the human upper limb role in performing daily life and personal activities is significant. Improper functioning of this organ due to neurological disorders or surgeries can greatly affect the daily activities performed by patients. This paper aims to comprehensively review soft and rigid wearable robotic devices provided for rehabilitation and assistance focusing on the shoulder joint. In the last two decades, many devices have been proposed in this regard, however, there have been a few groups whose devices have had effective therapeutic capability with acceptable clinical evidence. Also, there were not many portable, lightweight and user-friendly devices. Therefore, this comprehensive study could pave the way for achieving optimal future devices, given the growing need for these devices. According to the results, the most commonly used plan was Exoskeleton, the most commonly used actuators were electrical, and most devices were considered to be stationary and rigid. By doing these studies, the advantages and disadvantages of each method are also presented. The presented devices each have a new idea and attitude in a specific field to solve the problems of movement disorders and rehabilitation, which were in the form of prototypes, initial clinical studies and sometimes comprehensive clinical and commercial studies. These plans need more comprehensive clinical trials to become a complete and efficient plan. This article could be used by researchers to identify and evaluate the important features and strengths and weaknesses of the plans to lead to the presentation of more optimal plans in the future

Robotic surface exploration with vision and tactile sensing for cracks detection and characterisation

This thesis presents a novel algorithm for crack localisation and detection based on visual and tactile analysis via fibre-optics. A finger-shaped sensor based on fibre-optics is employed for the data acquisition to collect data for the analysis and the experiments. Three pairs of fibre optics are used to measure the sensor's soft part deformation via changes in the reflected light intensity. A fourth pair of optical fibre cables is positioned at the tip of the finger and it is used to sense the proximity to external objects. To detect the possible locations of cracks a camera is used to scan an environment while running an object detection algorithm. Once the crack is detected, a fully-connected graph is created from a skeletonised version of the crack. Minimum spanning tree is then employed for calculating the shortest path to explore the crack which is then used to develop the motion planner for the robotic manipulator. The motion planner divides the crack into multiple nodes which are then explored one by one. Then, the manipulator starts the exploration and performs the tactile data classification to confirm if there is indeed a crack in that location or just a false positive from the vision algorithm. This is repeated until all the nodes of the crack are explored. If a crack is not detected from vision, then it won't be further explored in the tactile step. Because of this, false negative have the biggest weight and recall is the most import metric in this study. I perform experiments to investigate the improvements for the time required during exploration when using visual and tactile modalities together. The experimental studies demonstrate that exploring a fractured surface with a combination of visual and tactile modalities is four times faster than using solely the tactile mode. The accuracy of detection is also improved when the two modalities were combined. Experiments are also performed in order to develop a robust machine learning model to analyse and classify the tactile data acquired during exploration via the fibre-optics sensor. Frequency domain features are explored to investigate the spectrum of the signal. Results show that when training machine learning models and deep learning networks using these features, the resulting models are more robust when tested across different databases, on which they are not trained. Thus, when computer vision techniques may fail because of light conditions or extreme environments, fibre-optics sensors can be employed to analyse the presence of cracks on explored surfaces via machine learning and deep neural network algorithms. Still, when introducing tactile in extreme environments, caution must be used when making contact with possible fragile surfaces which may break because of the friction produced by the tactile sensor. Proximity may be used in this case to calculate the distance between the sensor and the object and to reduce speed when getting closer to the object. In conclusion, the thesis has contributed to advances in crack detection by introducing a multi-modal algorithm that is used to detect cracks in the environment via computer vision and then confirming the presence of a crack via tactile exploration and machine learning classification of the data acquired from a fibre-optic-based sensor. Few methods currently use tactile sensing for crack characterisation and detection and this is the first study which shows the reliability of tactile-based methodologies for crack detection via machine learning analysis. Furthermore, this is the first method which combines both tactile and vision for crack analysis

Design of a pneumatic soft robotic actuator using model-based optimization

In this thesis, the design and optimization process of a novel soft intelligent modular pad (IntelliPad) for the purpose of pressure injury prevention is presented. The structure of the IntelliPad consists of multiple individual multi-chamber soft pneumatic-driven actuators that use pressurized air and vacuum. Each actuator is able to provide both vertical and horizontal motions that can be controlled independently. An analytical modeling approach using multiple cantilever beams and virtual springs connected in a closed formed structure was developed to analyze the mechanical performance of the actuator. The analytical approach was validated by a finite element analysis. For optimizing the actuator\u27s mechanical performance, firefly algorithm and deep reinforcement learning-based design optimization frameworks were developed with the purpose of maximizing the horizontal motion of the top surface of the actuators, while minimizing its corresponding effect on the vertical motion. Four optimized designs were fabricated. The actuators were tested and validated experimentally to demonstrate their required mechanical performance in order to regulate normal and shear stresses at the skin-pad interface for pressure injury prevention applications

Design a CPW antenna on rubber substrate for multiband applications

This paper presents a compact CPW monopole antenna on rubber substrate for multiband applications. The multi band applications (2.45 and 3.65 GHz) is achieved on this antenna design with better antenna performances. Specially this antenna focused on ISM band application meanwhile some of slots (S1, S2, S3) have been used and attained another frequency band at 3.65 GHz for WiMAX application. The achievement of the antenna outcomes from this design that the bandwidth of 520 MHz for first band, the second band was 76 MHz for WiMAX application and the radiation efficiency attained around 90%. Moreover, the realized gain was at 4.27 dBi which overcome the most of existing design on that field. CST microwave studio has been used for antenna simulation