Design considerations for an active soft orthotic system for shoulder rehabilitation

Home-Based Rehabilitation of the Shoulder Using Auxiliary Systems and Artificial Intelligence: An Overview

Advancements in modern medicine have bolstered the usage of home-based rehabilitation services for patients, particularly those recovering from diseases or conditions that necessitate a structured rehabilitation process. Understanding the technological factors that can influence the efficacy of home-based rehabilitation is crucial for optimizing patient outcomes. As technologies continue to evolve rapidly, it is imperative to document the current state of the art and elucidate the key features of the hardware and software employed in these rehabilitation systems. This narrative review aims to provide a summary of the modern technological trends and advancements in home-based shoulder rehabilitation scenarios. It specifically focuses on wearable devices, robots, exoskeletons, machine learning, virtual and augmented reality, and serious games. Through an in-depth analysis of existing literature and research, this review presents the state of the art in home-based rehabilitation systems, highlighting their strengths and limitations. Furthermore, this review proposes hypotheses and potential directions for future upgrades and enhancements in these technologies. By exploring the integration of these technologies into home-based rehabilitation, this review aims to shed light on the current landscape and offer insights into the future possibilities for improving patient outcomes and optimizing the effectiveness of home-based rehabilitation programs.

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.

Soft Exoskeletons: Development, Requirements, and Challenges of the Last Decade

In this article, various investigations on soft exoskeletons are presented and their functional and structural characteristics are analyzed. The present work is oriented to the studies of the last decade and covers the upper and lower joints, specifically the shoulder, elbow, wrist, hand, hip, knee, and ankle. Its functionality, applicability, and main characteristics are exposed, such as degrees of freedom, force, actuators, power transmission methods, control systems, and sensors. The purpose of this work is to show the current trend in the development of soft exoskeletons, in addition to specifying the essential characteristics that must be considered in its design and the challenges that its construction implies.

A review of soft wearable robots that provide active assistance: Trends, common actuation methods, fabrication, and applications

This review meta-analysis combines and compares the findings of previously published works in the field of soft wearable robots (SWRs) that provide active methods of actuation for assistive and augmentative purposes. A thorough investigation of major contributions in the field of an SWR is made to analyze trends in the field focused on fluidic and cable-driven systems, prevalent and successful approaches, and identify the future direction of SWRs and active actuation strategies. Types of soft actuators used in wearables are outlined, as well as general practices for fabrication methods of soft actuators and considerations for human-robot interface designs of garment-like exosuits. An overview of well-known and emerging upper body (UB)- and lower body (LB)-assistive technologies is categorized by the specific joints and degree of freedom (DoF) assisted and which actuator methodology is provided. Different use cases for SWRs are addressed, as well as implementation strategies and design applications.

A review of cable-driven rehabilitation devices

Purpose: To conduct a survey on the research and development of cable-driven rehabilitation devices (CDRDs). Method: This review searches in the databases of PubMed, IEEE Xplore Digital Library, Science Direct, and Google Scholar using various combinations of the following keywords: cable, wire, rehabilitation, assistance, therapy, training, robot, elastic, and pneumatic. Searches in the above databases for references cited by the above-searched references are also conducted to include a larger context of CDRDs. Results: CDRDs are classified into four categories, namely, serial exoskeleton-based, parallel exoskeleton-based, serial end-effector-based, and parallel end-effector-based CDRDs. Each category of CDRDs are further grouped based on the part of the human body to be rehabilitated. All four categories of CDRDs are examined and compared and their advantages and shortcomings are discussed based on popular rehabilitation requirements on weight, adaptability, versatility, misalignment, and safety. Open issues of CDRDs are also discussed.Conclusions: Each category of CDRDs has its own advantages and shortcomings. The selection of a CDRD highly depends on the specific application. Regarding the convenience of setting up a CDRD for rehabilitation, parallel CDRDs usually have better adaptability than serial ones. However, uncertainties come with parallel CDRDs, which makes the control of parallel CDRDs more challenging. Moreover, the strategy of inherent safety has a great potential to further improve the safety of CDRDs.Implications for rehabilitationCDRDs (and general RRDs) can deliver high-intensity training while therapists usually cannot. With up-to-date human-robot interaction techniques (e.g., virtual reality), CDRDs are more interesting and motivating to trainees than conventional manual rehabilitation therapies. CDRDs also provide financial benefits in the long-run. Currently existing RRDs available for clinical practice are mainly designed for the rehabilitation of shoulders, elbows, and knees. Parallel exoskeleton-based CDRDs can also be used for the rehabilitation of many other parts of trainees. Thus, CDRDs extend the coverage of RRDs in rehabilitation. Owing to their simple structures and light weights, CDRDs can be portable and used for rehabilitation at home. In this way, CDRDs can improve the duration and intensity of rehabilitation for those with limited access to rehabilitation institutes. As well known, the higher intensity of training leads to a higher rate of recovery.

Bio-inspired upper limb soft exoskeleton to reduce stroke-induced complications

Stroke has become the leading cause of disability and the second-leading cause of mortality worldwide. Dyskinesia complications are the major reason of these high death and disability rates. As a tool for rapid motion function recovery in stroke patients, exoskeleton robots can reduce complications and thereby decrease stroke mortality rates. However, existing exoskeleton robots interfere with the wearer's natural motion and damage joints and muscles due to poor human-machine coupling. In this paper, a novel ergonomic soft bionic exoskeleton robot with 7 degrees of freedom was proposed to address these problems based on the principles of functional anatomy and sports biomechanics. First, the human motion system was analysed according to the functional anatomy, and the muscles were modelled as tension lines. Second, a soft bionic robot was established based on the musculoskeletal tension line model. Third, a robot control method mimicking human muscle control principles was proposed and optimized on a humanoid platform manufactured using 3D printing. After the control method was optimized, the motion trajectory similarities between humans and the platform exceeded 87%. Fourth, the force-assisted effect was tested based on electromyogram signals, and the results showed that muscle signals decreased by 58.17% after robot assistance. Finally, motion-assistance experiments were performed with stroke patients. The joint movement level increased by 174% with assistance, which allowed patients to engage in activities of daily living. With this robot, stroke patients could recover their motion functions, preventing complications and decreasing fatality and disability rates.

A computerized recognition system for the home-based physiotherapy exercises using an RGBD camera

Computerized recognition of the home based physiotherapy exercises has many benefits and it has attracted considerable interest among the computer vision community. However, most methods in the literature view this task as a special case of motion recognition. In contrast, we propose to employ the three main components of a physiotherapy exercise (the motion patterns, the stance knowledge, and the exercise object) as different recognition tasks and embed them separately into the recognition system. The low level information about each component is gathered using machine learning methods. Then, we use a generative Bayesian network to recognize the exercise types by combining the information from these sources at an abstract level, which takes the advantage of domain knowledge for a more robust system. Finally, a novel postprocessing step is employed to estimate the exercise repetitions counts. The performance evaluation of the system is conducted with a new dataset which contains RGB (red, green, and blue) and depth videos of home-based exercise sessions for commonly applied shoulder and knee exercises. The proposed system works without any body-part segmentation, bodypart tracking, joint detection, and temporal segmentation methods. In the end, favorable exercise recognition rates and encouraging results on the estimation of repetition counts are obtained.

Handmade task tracking applied to cognitive rehabilitation

This article presents research focused on tracking manual tasks that are applied in cognitive rehabilitation so as to analyze the movements of patients who suffer from Apraxia and Action Disorganization Syndrome (AADS). This kind of patients find executing Activities of Daily Living (ADL) too difficult due to the loss of memory and capacity to carry out sequential tasks or the impossibility of associating different objects with their functions. This contribution is developed from the work of Universidad Politécnica de Madrid and Technical University of Munich in collaboration with The University of Birmingham. The KinectTM for Windows© device is used for this purpose. The data collected is compared to an ultrasonic motion capture system. The results indicate a moderate to strong correlation between signals. They also verify that KinectTM is very suitable and inexpensive. Moreover, it turns out to be a motion-capture system quite easy to implement for kinematics analysis in ADL.