Passive RFID-based Intelligent Gloves for Alternative and Assistive Communication - A Preliminary Study

We introduce intelligent gloves based on passive ultrahigh frequency (UHF) radio frequency identification (RFID) technology, which comprises of four antenna parts and three RFID integrated circuits (ICs). Each of the ICs (in middle finger, ring finger and small finger) have their unique IDs, which can be activated by gentle touch of thumb, and used to send a specific message, which is displayed on a computer screen. Two users tested the gloves in an office environment with M6 mercury RFID reader and a specially developed software. The achieved success rate in these preliminary tests was 100 %. We consider these results promising first steps for future wearable passive RFID-based augmentative and alternative communication (AAC) solutions.acceptedVersionPeer reviewe

Identification of gait phases with neural networks for smooth transparent control of a lower limb exoskeleton

Lower limbs exoskeletons provide assistance during standing, squatting, and walking. Gait dynamics, in particular, implies a change in the configuration of the device in terms of contact points, actuation, and system dynamics in general. In order to provide a comfortable experience and maximize performance, the exoskeleton should be controlled smoothly and in a transparent way, which means respectively, minimizing the interaction forces with the user and jerky behavior due to transitions between different configurations. A previous study showed that a smooth control of the exoskeleton can be achieved using a gait phase segmentation based on joint kinematics. Such a segmentation system can be implemented as linear regression and should be personalized for the user after a calibration procedure. In this work, a nonlinear segmentation function based on neural networks is implemented and compared with linear regression. An on-line implementation is then proposed and tested with a subject

A Modular Mobile Robotic Platform to Assist People with Different Degrees of Disability

Robotics to support elderly people in living independently and to assist disabled people in carrying out the activities of daily living independently have demonstrated good results. Basically, there are two approaches: one of them is based on mobile robot assistants, such as Care-O-bot, PR2, and Tiago, among others; the other one is the use of an external robotic arm or a robotic exoskeleton fixed or mounted on a wheelchair. In this paper, a modular mobile robotic platform to assist moderately and severely impaired people based on an upper limb robotic exoskeleton mounted on a robotized wheel chair is presented. This mobile robotic platform can be customized for each user’s needs by exploiting its modularity. Finally, experimental results in a simulated home environment with a living room and a kitchen area, in order to simulate the interaction of the user with different elements of a home, are presented. In this experiment, a subject suffering from multiple sclerosis performed different activities of daily living (ADLs) using the platform in front of a group of clinicians composed of nurses, doctors, and occupational therapists. After that, the subject and the clinicians replied to a usability questionnaire. The results were quite good, but two key factors arose that need to be improved: the complexity and the cumbersome aspect of the platform.This work was supported by the AIDE project through Grant Agreement No. 645322 of the European Commission, by the Conselleria d’Educacio, Cultura i Esport of Generalitat Valenciana, by the European Social Fund—Investing in your future, through the grant ACIF 2018/214, and by the Promoción de empleo joven e implantación de garantía juvenil en I+D+I 2018 through the grant PEJ2018-002670-A

Using Distributed Wearable Sensors to Measure and Evaluate Human Lower Limb Motions

This paper presents a wearable sensor approach to motion measurements of human lower limbs, in which subjects perform specified walking trials at self-administered speeds so that their level walking and stair ascent capacity can be effectively evaluated. After an initial sensor alignment with the reduced error, quaternion is used to represent 3-D orientation and an optimized gradient descent algorithm is deployed to calculate the quaternion derivative. Sensors on the shank offer additional information to accurately determine the instances of both swing and stance phases. The Denavit-Hartenberg convention is used to set up the kinematic chains when the foot stays stationary on the ground, producing state constraints to minimize the estimation error of knee position. The reliability of this system, from the measurement point of view, has been validated by means of the results obtained from a commercial motion tracking system, namely, Vicon, on healthy subjects. The step size error and the position estimation accuracy change are studied. The experimental results demonstrated that the extensively existed sensor misplacement and sensor drift problems can be well solved. The proposed self-contained and environment-independent system is capable of providing consistent tracking of human lower limbs without significant drift

A Novel Framework for Mixed Reality–Based Control of Collaborative Robot: Development Study

Background: Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research. Objective: This study aims to present the development of a novel framework for the collaborative robot using mixed reality. Methods: The framework uses Unity and Unity Hub as a cross-platform gaming engine and project management tool to design the mixed reality interface and digital twin. It also uses the Windows Mixed Reality platform to show digital materials on holographic display and the Azure mixed reality services to capture and expose digital information. Eventually, it uses a holographic device (HoloLens 2) to execute the mixed reality–based collaborative system. Results: A thorough experiment was conducted to validate the novel framework for mixed reality–based control of a collaborative robot. This framework was successfully applied to implement a collaborative system using a 5–degree of freedom robot (xArm-5) in a mixed reality environment. The framework was stable and worked smoothly throughout the collaborative session. Due to the distributed nature of cloud applications, there is a negligible latency between giving a command and the execution of the physical collaborative robot. Conclusions: Opportunities for collaborative robots in telerehabilitation and teleoperation are vital as in any other field. The proposed framework was successfully applied in a collaborative session, and it can also be applied in other similar potential applications for robust and more promising performance

Surface Electromyography and Artificial Intelligence for Human Activity Recognition - A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation

Human activity recognition (HAR) has become increasingly popular in recent years due to its potential to meet the growing needs of various industries. Electromyography (EMG) is essential in various clinical and biological settings. It is a metric that helps doctors diagnose conditions that affect muscle activation patterns and monitor patients’ progress in rehabilitation, disease diagnosis, motion intention recognition, etc. This review summarizes the various research papers based on HAR with EMG. Over recent years, the integration of Artificial Intelligence (AI) has catalyzed remarkable advancements in the classification of biomedical signals, with a particular focus on EMG data. Firstly, this review meticulously curates a wide array of research papers that have contributed significantly to the evolution of EMG-based activity recognition. By surveying the existing literature, we provide an insightful overview of the key findings and innovations that have propelled this field forward. It explore the various approaches utilized for preprocessing EMG signals, including noise reduction, baseline correction, filtering, and normalization, ensure that the EMG data is suitably prepared for subsequent analysis. In addition, we unravel the multitude of techniques employed to extract meaningful features from raw EMG data, encompassing both time-domain and frequency-domain features. These techniques are fundamental to achieving a comprehensive characterization of muscle activity patterns. Furthermore, we provide an extensive overview of both Machine Learning (ML) and Deep Learning (DL) classification methods, showcasing their respective strengths, limitations, and real-world applications in recognizing diverse human activities from EMG signals. In examining the hardware infrastructure for HAR with EMG, the synergy between hardware and software is underscored as paramount for enabling real-time monitoring. Finally, we also discovered open issues and future research direction that may point to new lines of inquiry for ongoing research toward EMG-based detection.publishedVersio

Design and Development of an Upper Limb Rehabilitative Robot with Dual Functionality

The design of an upper limb rehabilitation robot for post-stroke patients is considered a benchmark problem regarding improving functionality and ensuring better human–robot interaction (HRI). Existing upper limb robots perform either joint-based exercises (exoskeleton-type functionality) or end-point exercises (end-effector-type functionality). Patients may need both kinds of exercises, depending on the type, level, and degree of impairments. This work focused on designing and developing a seven-degrees-of-freedom (DoFs) upper-limb rehabilitation exoskeleton called ‘u-Rob’ that functions as both exoskeleton and end-effector types device. Furthermore, HRI can be improved by monitoring the interaction forces between the robot and the wearer. Existing upper limb robots lack the ability to monitor interaction forces during passive rehabilitation exercises; measuring upper arm forces is also absent in the existing devices. This research work aimed to develop an innovative sensorized upper arm cuff to measure the wearer’s interaction forces in the upper arm. A PID control technique was implemented for both joint-based and end-point exercises. The experimental results validated both types of functionality of the developed robot

Soft pneumatic muscles for post-stroke lower limb ankle rehabilitation: leveraging the potential of soft robotics to optimize functional outcomes

Introduction: A soft pneumatic muscle was developed to replicate intricate ankle motions essential for rehabilitation, with a specific focus on rotational movement along the x-axis, crucial for walking. The design incorporated precise geometrical parameters and air pressure regulation to enable controlled expansion and motion.Methods: The muscle’s response was evaluated under pressure conditions ranging from 100-145 kPa. To optimize the muscle design, finite element simulation was employed to analyze its performance in terms of motion range, force generation, and energy efficiency. An experimental platform was created to assess the muscle’s deformation, utilizing advanced techniques such as high-resolution imaging and deep-learning position estimation models for accurate measurements. The fabrication process involved silicone-based materials and 3D-printed molds, enabling precise control and customization of muscle expansion and contraction.Results: The experimental results demonstrated that, under a pressure of 145 kPa, the y-axis deformation (y-def) reached 165 mm, while the x-axis and z-axis deformations were significantly smaller at 0.056 mm and 0.0376 mm, respectively, highlighting the predominant elongation in the y-axis resulting from pressure actuation. The soft muscle model featured a single chamber constructed from silicone rubber, and the visually illustrated and detailed geometrical parameters played a critical role in its functionality, allowing systematic manipulation to meet specific application requirements.Discussion: The simulation and experimental results provided compelling evidence of the soft muscle design’s adaptability, controllability, and effectiveness, thus establishing a solid foundation for further advancements in ankle rehabilitation and soft robotics. Incorporating this soft muscle into rehabilitation protocols holds significant promise for enhancing ankle mobility and overall ambulatory function, offering new opportunities to tailor rehabilitation interventions and improve motor function restoration

Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error)

Study and development of sensorimotor interfaces for robotic human augmentation

This thesis presents my research contribution to robotics and haptics in the context of human augmentation. In particular, in this document, we are interested in bodily or sensorimotor augmentation, thus the augmentation of humans by supernumerary robotic limbs (SRL). The field of sensorimotor augmentation is new in robotics and thanks to the combination with neuroscience, great leaps forward have already been made in the past 10 years. All of the research work I produced during my Ph.D. focused on the development and study of fundamental technology for human augmentation by robotics: the sensorimotor interface. This new concept is born to indicate a wearable device which has two main purposes, the first is to extract the input generated by the movement of the user's body, and the second to provide the somatosensory system of the user with an haptic feedback. This thesis starts with an exploratory study of integration between robotic and haptic devices, intending to combine state-of-the-art devices. This allowed us to realize that we still need to understand how to improve the interface that will allow us to feel the agency when using an augmentative robot. At this point, the path of this thesis forks into two alternative ways that have been adopted to improve the interaction between the human and the robot. In this regard, the first path we presented tackles two aspects conerning the haptic feedback of sensorimotor interfaces, which are the choice of the positioning and the effectiveness of the discrete haptic feedback. In the second way we attempted to lighten a supernumerary finger, focusing on the agility of use and the lightness of the device. One of the main findings of this thesis is that haptic feedback is considered to be helpful by stroke patients, but this does not mitigate the fact that the cumbersomeness of the devices is a deterrent to their use. Preliminary results here presented show that both the path we chose to improve sensorimotor augmentation worked: the presence of the haptic feedback improves the performance of sensorimotor interfaces, the co-positioning of haptic feedback and the input taken from the human body can improve the effectiveness of these interfaces, and creating a lightweight version of a SRL is a viable solution for recovering the grasping function