153 research outputs found
Upper limb soft robotic wearable devices: a systematic review
Introduction: Soft robotic wearable devices, referred to as exosuits, can be a valid alternative to rigid exoskeletons when it comes to daily upper limb support. Indeed, their inherent flexibility improves comfort, usability, and portability while not constraining the user’s natural degrees of freedom. This review is meant to guide the reader in understanding the current approaches across all design and production steps that might be exploited when developing an upper limb robotic exosuit. Methods: The literature research regarding such devices was conducted in PubMed, Scopus, and Web of Science. The investigated features are the intended scenario, type of actuation, supported degrees of freedom, low-level control, high-level control with a focus on intention detection, technology readiness level, and type of experiments conducted to evaluate the device. Results: A total of 105 articles were collected, describing 69 different devices. Devices were grouped according to their actuation type. More than 80% of devices are meant either for rehabilitation, assistance, or both. The most exploited actuation types are pneumatic (52%) and DC motors with cable transmission (29%). Most devices actuate 1 (56%) or 2 (28%) degrees of freedom, and the most targeted joints are the elbow and the shoulder. Intention detection strategies are implemented in 33% of the suits and include the use of switches and buttons, IMUs, stretch and bending sensors, EMG and EEG measurements. Most devices (75%) score a technology readiness level of 4 or 5. Conclusion: Although few devices can be considered ready to reach the market, exosuits show very high potential for the assistance of daily activities. Clinical trials exploiting shared evaluation metrics are needed to assess the effectiveness of upper limb exosuits on target users
Biomechatronics: Harmonizing Mechatronic Systems with Human Beings
This eBook provides a comprehensive treatise on modern biomechatronic systems
centred around human applications. A particular emphasis is given to exoskeleton
designs for assistance and training with advanced interfaces in human-machine
interaction. Some of these designs are validated with experimental results which
the reader will find very informative as building-blocks for designing such systems.
This eBook will be ideally suited to those researching in biomechatronic area with
bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design
at post-graduate level
A High-Level Control Algorithm Based on sEMG Signalling for an Elbow Joint SMA Exoskeleton
A high-level control algorithm capable of generating position and torque references from surface electromyography signals (sEMG) was designed. It was applied to a shape memory alloy (SMA)-actuated exoskeleton used in active rehabilitation therapies for elbow joints. The sEMG signals are filtered and normalized according to data collected online during the first seconds of a therapy session. The control algorithm uses the sEMG signals to promote active participation of patients during the therapy session. In order to generate the reference position pattern with good precision, the sEMG normalized signal is compared with a pressure sensor signal to detect the intention of each movement. The algorithm was tested in simulations and with healthy people for control of an elbow exoskeleton in flexion&-extension movements. The results indicate that sEMG signals from elbow muscles, in combination with pressure sensors that measure arm&-exoskeleton interaction, can be used as inputs for the control algorithm, which adapts the reference for exoskeleton movements according to a patient's intention.The research was funded by RoboHealth (DPI2013-47944-C4-3-R) and the EDAM (DPI2016-75346-R) Spanish research projects
Robotics rehabilitation of the elbow based on surface electromyography signals
Physical rehabilitation based on robotic systems has the potential to cover the patient’s need of improvement of upper extremity functionalities. In this article, the state of the art of resistant and assistive upper limb exoskeleton robots and their control are thoroughly investigated. Afterward, a single-degree-of-freedom exoskeleton matching the elbow–forearm has been advanced to grant a valid rehabilitation therapy for persons with physical disability of upper limb motion. The authors have focused on the control system based on the use of electromyography signals as an input to drive the joint movement and manage the robotics arm. The correlation analysis between surface electromyography signal and the force exerted by the subject was studied in objects’ grasping tests with the purpose of validating the methodology. The authors developed an innovative surface electromyography force–based active control that adjusts the force exerted by the device during rehabilitation. The control was validated by an experimental campaign on healthy subjects simulating disease on an arm, with positive results that confirm the proposed solution and that open the way to future researches
Single Lead EMG signal to Control an Upper Limb Exoskeleton Using Embedded Machine Learning on Raspberry Pi
Post-stroke can cause partial or complete paralysis of the human limb. Delayed rehabilitation steps in post-stroke patients can cause muscle atrophy and limb stiffness. Post-stroke patients require an upper limb exoskeleton device for the rehabilitation process. Several previous studies used more than one electrode lead to control the exoskeleton. The use of many electrode leads can lead to an increase in complexity in terms of hardware and software. Therefore, this research aims to develop single lead EMG pattern recognition to control an upper limb exoskeleton. The main contribution of this research is that the robotic upper limb exoskeleton device can be controlled using a single lead EMG. EMG signals were tapped at the biceps point with a sampling frequency of 2000 Hz. A Raspberry Pi 3B+ was used to embed the data acquisition, feature extraction, classification and motor control by using multithread algorithm. The exoskeleton arm frame is made using 3D printing technology using a high torque servo motor drive. The control process is carried out by extracting EMG signals using EMG features (mean absolute value, root mean square, variance) further extraction results will be trained on machine learning (decision tree (DT), linear regression (LR), polynomial regression (PR), and random forest (RF)). The results show that machine learning decision tree and random forest produce the highest accuracy compared to other classifiers. The accuracy of DT and RF are of 96.36±0.54% and 95.67±0.76%, respectively. Combining the EMG features, shows that there is no significant difference in accuracy (p-value 0.05). A single lead EMG electrode can control the upper limb exoskeleton robot device well
Non-linear actuators and simulation tools for rehabilitation devices
Mención Internacional en el tÃtulo de doctorRehabilitation robotics is a field of research that investigates the applications of
robotics in motor function therapy for recovering the motor control and motor capability.
In general, this type of rehabilitation has been found effective in therapy for
persons suffering motor disorders, especially due to stroke or spinal cord injuries. This
type of devices generally are well tolerated by the patients also being a motivation in
rehabilitation therapy. In the last years the rehabilitation robotics has become more
popular, capturing the attention at various research centers. They focused on the development
more effective devices in rehabilitation therapy, with a higher acceptance
factor of patients tacking into account: the financial cost, weight and comfort of the
device.
Among the rehabilitation devices, an important category is represented by the
rehabilitation exoskeletons, which in addition to the human skeletons help to protect
and support the external human body. This became more popular between the
rehabilitation devices due to the easily adapting with the dynamics of human body,
possibility to use them such as wearable devices and low weight and dimensions which
permit easy transportation.
Nowadays, in the development of any robotic device the simulation tools play an
important role due to their capacity to analyse the expected performance of the system
designed prior to manufacture. In the development of the rehabilitation devices,
the biomechanical software which is capable to simulate the behaviour interaction
between the human body and the robotics devices, play an important role. This
helps to choose suitable actuators for the rehabilitation device, to evaluate possible
mechanical designs, and to analyse the necessary controls algorithms before being
tested in real systems.
This thesis presents a research proposing an alternative solution for the current
systems of actuation on the exoskeletons for robotic rehabilitation. The proposed
solution, has a direct impact, improving issues like device weight, noise, fabrication
costs, size an patient comfort. In order to reach the desired results, a biomechanical software based on Biomechanics of Bodies (BoB) simulator where the behaviour of
the human body and the rehabilitation device with his actuators can be analysed,
was developed.
In the context of the main objective of this research, a series of actuators have
been analysed, including solutions between the non-linear actuation systems. Between
these systems, two solutions have been analysed in detail: ultrasonic motors
and Shape Memory Alloy material. Due to the force - weight characteristics of each
device (in simulation with the human body), the Shape Memory Alloy material was
chosen as principal actuator candidate for rehabilitation devices.
The proposed control algorithm for the actuators based on Shape Memory Alloy,
was tested over various configurations of actuators design and analysed in terms of energy
eficiency, cooling deformation and movement. For the bioinspirated movements,
such as the muscular group's biceps-triceps, a control algorithm capable to control
two Shape Memory Alloy based actuators in antagonistic movement, has been developed.
A segmented exoskeleton based on Shape Memory Alloy actuators for the upper
limb evaluation and rehabilitation therapy was proposed to demosntrate the eligibility
of the actuation system. This is divided in individual rehabilitation devices for
the shoulder, elbow and wrist. The results of this research was tested and validated
in the real elbow exoskeleton with two degrees of freedom developed during this thesis.Programa Oficial de Doctorado en IngenierÃa Eléctrica, Electrónica y AutomáticaPresidente: Eduardo Rocón de Lima.- Secretario: Concepción Alicia Monje Micharet.- Vocal: Martin Stoele
Adaptive Compliance Shaping with Human Impedance Estimation
Human impedance parameters play an integral role in the dynamics of strength
amplification exoskeletons. Many methods are used to estimate the stiffness of
human muscles, but few are used to improve the performance of strength
amplification controllers for these devices. We propose a compliance shaping
amplification controller incorporating an accurate online human stiffness
estimation from surface electromyography (sEMG) sensors and stretch sensors
connected to the forearm and upper arm of the human. These sensor values along
with exoskeleton position and velocity are used to train a random forest
regression model that accurately predicts a person's stiffness despite varying
movement, relaxation, and muscle co-contraction. Our model's accuracy is
verified using experimental test data and the model is implemented into the
compliance shaping controller. Ultimately we show that the online estimation of
stiffness can improve the bandwidth and amplification of the controller while
remaining robustly stable.Comment: 8 pages, 9 figures, Accepted for publication at the 2020 American
Control Conference. Copyright IEEE 202
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A Study on Active/Passive Pneumatic Actuators for Assistive Systems
The need for intelligent assistive devices is growing. Due to advances in medicine, people are living longer and able to recover from severe neurological incidents, resulting in an increased population with neuromuscular weakness. In workplaces such as assembly lines, there is a high possibility of work-related fatigue or injury, such as when workers squat down or lift their arms during their work tasks. Assistive devices could help remedy loss of strength on their extremities as well as keep the work environment safe and productive, allowing these growing segments of the population in need of the devices to live more self-sufficient, productive, and higher-quality lives.In the design of assistive systems, an important design goal is prolonged operational time, which requires the minimum usage of energy. Energy consumption can be reduced by modifying the mechanical characteristics of assistive systems according to the dynamic characteristics of the human body, which vary considerably between tasks. This dissertation investigates 1) the design of actuators with adjustable mechanical impedance, 2) control strategies to search for, and adjust to, a suitable mechanical impedance for assistance and 3) sensing technologies for classifying the tasks in which the human engages.The first part of this dissertation characterizes a pneumatic variable stiffness actuator named an Active/Passive Pneumatic Actuator (AP2A). The actuator consists of an air cylinder and an array of solenoid valves. These valves and the corresponding switching algorithms tune the chamber pressures and make the AP2A function as a mechanical spring with desired stiffness. The actuator has a low mechanical impedance compared to geared motors, which enables it to achieve efficient interaction. Control strategies of an assistive system with the AP2A are discussed in the second part. This control framework utilizes the characteristics of the AP2A to provide assistance when necessary and to operate transparently (i.e., neither to assist nor to disturb the users) otherwise. Energy consumed by the AP2A and the assisted system is minimized by solving an optimal control problem. Finally, an estimator is introduced to detect assistive timing for the assistive system with the AP2A. This estimator utilizes physiological signals such as surface electromyogram and prior knowledge of a muscular model, classifying if the user is under the specified condition to be assisted by the AP2A. It demonstrates that the user's effort can be saved, also reducing the number of procedures to collect training data for the estimator before using assistive systems. The performance of the actuator, the controller, and the estimator proposed in this dissertation are verified through experiments.From the above, this dissertation contributes to developing the AP2A that provides assistance and saves energy usage of assistive systems by working as a mechanical spring with stiffness optimized for achieving effective interaction under specific conditions. This actuator supports assistive devices that can be deployed in the real world, properly assisting the users when needed
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