Dynamic Field Programmable Logic-Driven Soft Exosuit

The next generation of etextiles foresees an era of smart wearable garments where embedded seamless intelligence provides the ability to sense, process and perform. Core to this vision is embedded textile functionality enabling dynamic configuration. In this paper we detail a methodology, design and implementation of a dynamic field programmable logic-driven fabric soft exosuit. Dynamic field programmability allows the soft exosuit to alter its functionality and adapt to specific exercise programs depending on the wearers need. The dynamic field programmability is enabled through motion based control arm movements of the soft exosuit triggering momentary sensors embedded in the fabric exosuit at specific joint placement points (right arm: wrist, elbow).The embedded circuitry in the fabric exosuit is implemented using a layered and interchangeable approach. This includes logic gate patches (AND,OR,NOT) and a layered textile interconnection panel. This modular and interchangeable design enhances the soft exosuits flexibility and adaptability. A truth table aligning to a rehabilitation healthcare use case was utilised. Tests were completed validating the field programmability of the soft exosuit and its capability to switch between its embedded logic driven circuitry and its operational and functionality options controlled by motion movement of the wearers right arm (elbow and wrist). Iterative exercise movement and acceleration based tests were completed to validate the functionality of the field programmable logic driven fabric exosuit. We demonstrate a working soft exosuit prototype with motion controlled operational functionality that can be applied to rehabilitation applications.Comment: 20 pages, 9 figure

Adaptive model-based myoelectric control for a soft wearable arm exosuit:A new generation of wearable robot control

Despite advances in mechatronic design, the widespread adoption of wearable robots for supporting human mobility has been hampered by 1) ergonomic limitations in rigid exoskeletal structures and 2) the lack of human-machine interfaces (HMIs) capable of sensing musculoskeletal states and translating them into robot-control commands. We have developed a framework that combines, for the first time, a model-based HMI with a soft wearable arm exosuit that has the potential to address key limitations in current HMIs and wearable robots. The proposed framework was tested on six healthy subjects who performed elbow rotations across different joint velocities and lifting weights. The results showed that the model-controlled exosuit operated synchronously with biological muscle contraction. Remarkably, the exosuit dynamically modulated mechanical assistance across all investigated loads, thereby displaying adaptive behavior

Soft Wrist Exosuit Actuated by Fabric Pneumatic Artificial Muscles

Recently, soft actuator-based exosuits have gained interest, due to their high strength-to-weight ratio, inherent safety, and low cost. We present a novel wrist exosuit actuated by fabric pneumatic artificial muscles that can move the wrist in flexion/extension and ulnar/radial deviation. We derive a model representing the torque exerted by the exosuit and introduce a model-based optimization methodology for the selection of placement parameters of the exosuit muscles. We evaluate the accuracy of the model by measuring the exosuit torques throughout the full range of wrist flexion/extension. When accounting for the displacement of the mounting points, the model predicts the exosuit torque with a mean absolute error of 0.279 Nm, which is 26.1% of the average measured torque. To explore the capabilities of the exosuit to move the human body, we measure its range of motion on a passive human wrist; the exosuit is able to achieve 55.0% of the active biological range in flexion, 69.1% in extension, 68.6% in ulnar deviation, and 68.4% in radial deviation. Finally, we demonstrate the device controlling the passive human wrist to move to a desired orientation in the flexion/extension plane and along a two-degree-of-freedom trajectory.Comment: 16 pages, 15 figure

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

Emerging trends in upper-limb embedded devices: A qualitative research study

Framework This paper explores how a qualitative systematic literature review (SLR) can contribute to our understanding of the trends in upper-limb wearable devices. These devices are pieces of electronic equipment that can be worn as accessories, such as watches, or embedded in clothing, including gloves and sleeves, and could play an essential role in subjects' quality of life after any occurrence that affects their possibility to perform basic activities autonomously. Moreover, these devices can be used to improve manual performance tasks like surgical or precision tasks, and even more so when performed under extreme ambient temperature conditions. Goals and Methods: A SLR on upper-limb embedded devices was conducted based on scientific documents retrieved from the Scopus database. Two research questions were outlined: "How has this technology been evolving?" and "What is the trend according to the fields of application?". The combination of keywords (upper-limb* AND wearable* AND device*) was used in the title, abstract, and keywords fields. Results: A total of 555 documents were obtained. Descriptive statistical and bibliometric analyses were conducted, identifying trends, knowledge gaps, and the future direction of research. The free software VOSviewer was used to construct data visualization bibliometric maps of the co-authorship and co-citation network. A subset of 26 documents was considered for the critical qualitative synthesis. This step facilitated the visualization and exploration of the interconnectedness among authors and the citation patterns within the literature. Combining the information gathered enables addressing the extent and the emerging trends in upper-limb embedded devices' development according to the field they are applied. Final considerations: With this research, a starting point in developing a proof of concept of a novel device aimed at improving dexterity in challenging environments is established

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

Intelligent upper-limb exoskeleton using deep learning to predict human intention for sensory-feedback augmentation

The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper extremities. Although there are a few examples of exoskeletons, they need manual operations due to the absence of sensor feedback and no intention prediction of movements. Here, we introduce an intelligent upper-limb exoskeleton system that uses cloud-based deep learning to predict human intention for strength augmentation. The embedded soft wearable sensors provide sensory feedback by collecting real-time muscle signals, which are simultaneously computed to determine the user's intended movement. The cloud-based deep-learning predicts four upper-limb joint motions with an average accuracy of 96.2% at a 200-250 millisecond response rate, suggesting that the exoskeleton operates just by human intention. In addition, an array of soft pneumatics assists the intended movements by providing 897 newton of force and 78.7 millimeter of displacement at maximum. Collectively, the intent-driven exoskeleton can augment human strength by 5.15 times on average compared to the unassisted exoskeleton. This report demonstrates an exoskeleton robot that augments the upper-limb joint movements by human intention based on a machine-learning cloud computing and sensory feedback.Comment: 15 pages, 6 figures, 1 table, Submitted for possible publicatio