A Wearable Device for Upper Limb Rehabilitation and Assistance Based on Fluid Actuators and Myoelectric Control

Abstract

Wearable exoskeleton solutions for upper limb rehabilitation or assistance, particularly for the hand area, have become increasingly attractive to researchers, proving to be effective over time in treating hand movement impairments following various neurological diseases. Our aim in the present work is to design a wearable exoskeleton device for active hand rehabilitation/assist control based on myoelectric signal (EMG) capture from forearm muscles, which is easy to wear by the user, comfortable, lightweight, and relatively inexpensive to make. The actuators use two different lengths to increase biocompatibility with the anatomy of the hand, and PneuNets fluid actuators are used. Their design to meet force and bending requirements was based on finite element numerical simulations, and the actuators were designed based on a clear design methodology to achieve the best possible quality. Tests on healthy subjects show that the EMG-based control strategy meets the needs of rehabilitation/assistive hand therapy, finding a comfortable and easy-to-use device. Future directions will focus on developing the device to meet rehabilitation needs for the entire upper limb and integrating the device into virtual reality (VR) through immersive devices.

1. Introduction

In today’s social context, where several factors such as sedentarism, diet, and stress can cause traumas or impairments in the population, this is at the root of the increasing number of people suffering from various forms of disability. According to statistics reported in 2017, the most significant cause leading to various forms of disability is due to stroke [1]. Similarly, in 2012, according to a report by the American Health Association (AHA) in the United States of America, around 4 million people were suffering from a range of injuries due to stroke [2]. Other studies show that more than 80% of people who have suffered a stroke remain with some movement impairment in their upper limbs [3,4], hampering or limiting the performance of activities of daily living (ADL). They need repeated rehabilitation exercises over a long period in order to be able to regain their original mobility [5,6]. Due to the increase in the number of people needing post-stroke rehabilitation, as well as limited specialist medical staff to treat stroke [7,8], there is a need to identify effective methods to help them regain autonomous movement. This “self-help” type of training has the potential to reduce some of the burdens on the healthcare system, namely by offering stroke patients the opportunity to perform rehabilitation training alone at home or at their desired place.

Both commercially and in the literature, several robotic variants meet rehabilitation or assistive needs by guiding repetitive movements with different ranges of motion in the hands, elbow, or shoulder joints. Available variants such as PEXO [9] or TENEXO [10], HANDEXOS [11], or the exoskeleton-type device proposed by Ho, N. et al. [12], use rigid joint links driven by DC motors. These variants have advantages in terms of good modeling and control capability providing accuracy, but they also have some less favorable aspects in terms of size, shape, weight, and comfort of the devices. These are important criteria for the success of such a device. Based on these considerations, over the past decade, researchers have been concerned with developing various robotic systems (with rigid or soft elements) that possess features inspired by the living world, which provide a transition from nonredundant to redundant, hyper-redundant, continuous, and soft systems to increase the flexibility, adaptability, and biocompatibility of the systems [13,14]. Wearable hand rehabilitation/assistance devices and other devices using soft actuators represent an important research direction in the field of soft robotics and there is a large diversity of such devices. In part, soft robotic devices for hand rehabilitation use fluidic soft actuators such as the handheld device of the Wyss Institute at Harvard University called “Wyss Soft Robotic Glove” [15] or the commercially available devices of Syrebo called “Syrebo Hand” [16].

Analyzing the literature, we have also found several publications addressing handheld devices with soft actuators for hand rehabilitation/assistance at different levels of implementation, some of which will be presented shortly. Hong Kai. Yap et al. developed a portable glove-type device based on pneumatically actuated fluid actuators made of flexible thermoplastic materials and coated with thermoplastic polyurethane (TPU) that have the ability to provide bidirectional assistance of both flexion and extension movements [17]. Another approach integrating bidirectional fluid actuators is presented in the work of Heung KHL et al. who proposed a wearable glove designed for everyday activities (ADL). The actuators are made of elastomer with two inner chambers that are individually pressurized to actively assist both flexion and extension of the fingers. Their structure is uniform throughout their length, and they are fitted with a flexible sensor that monitors the level of flexion of the actuators, whose insertion is between the two chambers. The authors proposed an analytical model that aims to quantify the output force of the actuators in the case of squeezing in relation to the relationship between the input pressure, the flexure angle, and the output force. All these analyses have been validated numerically and experimentally [18]. Jiangbei Wang et al. proposed a wearable lower limb rehabilitation device based on 5 PneuNets fluid actuators that actively assist flexion and passively assist extension. The actuators are designed according to the anatomical characteristics of human fingers, with the actuators having three joint segments corresponding to the three finger joints (2–5), two for the thumb, and four rigid segments corresponding to the phalangeal and metacarpal bone segments, respectively. This approach allowed the actuator characteristics to be designed to have kinematics and a range of motion similar to those of human fingers, a feature achieved through numerical and experimental modeling [19]. The literature review also identified hybrid wearable glove actuation solutions using both pneumatic and cable actuation. In the article by Lucas Grez et al., such a hybrid drive wearable exoskeleton device with variable stiffness for ADL activities is proposed. Furthermore, the glove is designed to assist both flexion/extension and abduction/adduction movements of the fingers. Their tests aimed to improve the grip of various objects by using an additional telescopic finger that is pneumatically actuated. According to the results presented by the collective, the robotic glove in the configuration presented has considerably improved patients’ grip ability [20]. Computerized textile manufacturing technologies have been integrated into the creation of wearable actuators and devices to be incorporated into clothing. Hend M. Elmoughni et al. proposed a pneumatic actuator produced entirely by computerized knitting without using adjacent cutting or sewing technologies. Different knitting configurations were analyzed, and a grip assist device was invented able to perform active flexion movement at a pressure of 150 kPa and grip objects weighing up to 125 g [21]. In the specialist literature, wearable devices intended for the rehabilitation of the thumb have also been identified. Paxton Maeder-York et al. have constructed a wearable device intended to rehabilitate the flexion movement of the thumb through active control. The device uses a pneumatically actuated fluidic actuator composed of elastomer and reinforced with fibers to increase force characteristics. The configuration of the reinforcement braid is different to be able to achieve the kinematics of the human thumb as faithfully as possible [22]. Another article focusing on the thumb is by Yuanyuan Wang et al., which proposes two different approaches and analyzes them comparatively on the realization of flexion/extension and abduction/adduction movements. The two variants are based on soft fluidic actuators, the first variant being one that uses two elastomeric and fiber-reinforced actuators for the thumb and index finger corresponding to the flexion and extension motion of the fingers and a fan-shaped fiber-reinforced soft fluidic actuator. Once it is pressurized, it makes an angular movement driving the thumb into an abduction/adduction movement. The second configuration uses a reinforced soft actuator that performs flexion/extension motion of the finger and a fiber-reinforced actuator with three internal cavities that can perform both flexion/extension and abduction/adduction motion. Results show that each variant conveys advantages in some aspects of rehabilitation and a combined configuration would lead to substantial improvement [23]. Panagiotis Polygerinos et al. proposed a control system for a rehabilitation/assist glove that is based on the capture of myoelectric signals (EMG) from the forearm muscles through surface electrodes. The glove is made of elastomeric material fluid actuators that assist in flexion and extension movements of the hand phalanges [24]. The analysis identified a wide variety of soft wearable systems intended for hand rehabilitation or assistance. Most of these use air-pressurized fluidic actuators as actuation methods in a variety of configurations and features to meet the patient’s needs for strength, range of motion, and comfort in the recovery process. Also, most devices focus on assisting finger flexion/extension movements and less on abduction/adduction ones. The main component of these soft wearable exoskeleton devices are soft actuators, and with the accelerated development of the field of soft robotics in the last decade, a multitude of types of fluidic actuators have been designed for various applications. However, with the development of the field, their complexity in terms of manufacturing, actuation, modeling, and control has been of spectacular growth. Based on these considerations, the present work focuses on the creation and validation of a wearable device that is easy to wear and use and foremost has low manufacturing costs.

Generally, in the rehabilitation field, devices that provide sufficient biocompatibility and biomimeticity are predominantly used due to the provided comfort and lightweight. In terms of soft actuator systems, the most common types in the rehabilitation field are those based on fluid actuation, shape memory alloys, and dielectric elastomers [25]. Regarding the modalities of intention detection and sensors used in the control of the device, the majority of publications use control based on the capture of EMG muscle signals from the forearm muscle area, with this method being the most predominant in wearable devices. Another prevailing method is based on pressure and flex sensors [26].

The use of soft robotic systems in portable devices has the great advantage of offering patients the possibility of rehabilitation/assistance in the comfort of their own homes. Moreover, the use of fluid actuators made of elastomeric materials grants patients a safe interaction due to highly elastic soft materials and a large number of degrees of freedom, which implicitly provides the possibility of performing a wide range of movements, the low cost of producing such a device, and the corresponding high portability and manufacturing devices customized to the patient’s anatomical data. Based on the aspects presented above, this work addresses the realization of a low-priced portable device intended for rehabilitation/assistance of hands at the patient’s home for people who have suffered a stroke or other neurological diseases following which they are left with a range of mobility impairments. The wearable device is made with PneuNets fluid actuators that assist in the flexion and extension motion of the phalanges, specifically the DIP (distal interphalangeal) joint, PIP (proximal interphalangeal) joint, and MCP (metacarpophalangeal) joint at fingers 2–5 and IP (interphalangeal) joint and MCP (metacarpophalangeal) joint at finger 1 (thumb finger) in Figure 1. The actuators are positioned on the dorsal side of the hands, being actuated from a single actuator source, and the control is based on EMG signal capture with a commercial module with surface electrodes positioned in the forearm muscle area. This gadget also includes a pressure sensor and a flex sensor to monitor the air supply pressure and identify the amplitude of phalangeal movement. Its weight is 180 g, putting it in the optimal range for such devices [27,28].

2. Materials and Methods

2.1. Actuator Design and Fabrication

PneuNets actuators belong to the bellows actuator category, in which the specific bending movement is achieved by the deformation of the inner cavities. The actuator used benefits from a uniform bending movement over the entire surface, and it generally possesses the advantage of a fast response when pressurized, usually at low actuating pressure values. To achieve uniform deformation, the existing inextensible layer limits axial expansion during pressurization. Therefore, the bending motion is actively controlled during actuator pressurization. The internal pressures cause bending and lead to the production of mechanical energy while also accumulating elastic energy. As for the extension movement, it is passively controlled by different levels of pressurization as well as by the elastic energy accumulated by the actuator through bending movement, more precisely at the moment of pressurization. The choice of this type of actuator with this geometry was based on a number of advantages related to the simplicity of construction, low cost of realization, and meeting the needs of force and range of movement in the case of finger actuation, where the forces are relatively small. In addition, this type of configuration has proven over time to be the appropriate variant for such cases, receiving validation from the scientific community.

The 3D design of the actuators was carried out in the SolidWorks 2021 software in two different lengths. The actuators were chosen to be made in two different lengths due to the different anatomical dimensions of the human fingers. The main dimensions of the actuator and its components can be found in Figure 2.

The four elements that make up the actuator are the main body, through which the specific bending movement is achieved; two layers of silicone that enclose the main body; and between these two 3 mm thick layers of silicone, there is an inextensible layer of paper inserted to limit the axial extension, helping to achieve the bending movement. The dimensions of the main body are l—8 mm, d—2 mm, t—2.5 mm, and b—15 mm. These sizes were determined by numerical and experimental finite element modelling in the section below. They are identical for all 5 actuators and, in addition to these dimensions, the actuators have a width of 20 mm, while the lengths are 95 mm for fingers 2 (index), 3 (middle), and 4 (ring), and 75 mm for fingers 1 (thumb) and 5 (little), respectively. These dimensions were determined experimentally, encompassing the DIP, PIP, and MCP joints at which the flexion/extension movement is performed.

In the field of soft robotics, the most commonly used manufacturing processes are casting and 3D printing. In most situations, these two techniques are used collectively, with 3D printing being the technology for producing molds [29]. In this case, too, we have used casting as the main process to manufacture the actuators, and the molds were fabricated by 3D printing technology using polylactic acid (PLA) as the material. The 3 component molds needed to make the actuators were previously 3D modeled and 3D made using a CraftBot printer with a single extruder. The material from which the actuators were made is a two-component RTV ZA 22A silicone with a Shore hardness of A 22, composed of two elements A and B (base and catalyst), which are dosed in equal quantities. The specifications of this material are given in

Table 1. Material characteristics RTV ZA 22 A.

Characteristics	Value
Color	Blue
Shore Hardness	22
Mixing rate	1:1
Pot life	14–17 min
Cure time	2 h
Material type	Bicomponent

.

The manufacturing process by casting is based on a rigorous realization process. Following the mixing of the two components, which have been weighed beforehand, in equal quantities, a series of air bubbles accumulate in the material, which, once they enter the actuator, can alter its behavior or, worse still, cause it to break during pressurization. Therefore, an intermediate process was carried out to remove the air bubbles utilizing a vacuum system. Inside the vacuum plant, the silicone was left for 2 min at a negative pressure of −85 kPa. The manufacturing process is shown in Figure 3.

After the homogenized silicone was removed from the vacuum plant, it was poured into the 3D-printed mold, both for the main body of the actuator and for the two silicone layers that enclose the actuator. Two hours later, the actuator was removed from the mold and assembled. As an inextensible layer between the two silicone layers, we have used one made of paper.

2.2. Material Properties and Finite Elements Methods (FEA)

RTV ZA 22A is a high-compliance hyperelastic material that possesses the ability to elastically deform several times more than its initial value at the moment of stress. Determining the mechanical characteristics of the material is important to establish and validate the real and simulated behavior employing the FEA of the actuators used. For this purpose, the tensile mechanical characteristics of the RTV ZA 22A material were determined using the ASTM D12 (method A) standard [30]. The dimensions of the specimens are 3 mm in thickness, a total length of 115 mm (where the length of the calibrated part is 33 mm), a width at the ends of 25 mm, and a width of the calibrated part of 6 mm. The tests were performed on a sample of 5 specimens on the Titan 2 Universal tensile testing machine (software version 7.0.4.14642) at a speed of 50 mm/min and with a cell force of 600 N, as shown in Figure 4b. From the experimental data obtained, for the accuracy of the results, outliers were removed. Based on the experimental results taken from the machine software, the characteristic curve in the form of engineering stress and engineering strain was plotted, with the curve representing the average of the values of the 5 specimens tested. The test results are shown in Figure 4a.

The experimental data obtained from the tensile tests provide the possibility of determining a number of characteristics such as material elongation, Young’s modulus of elasticity, and material constants needed to perform finite element simulation of the actuator. This is essential to determine the approximate behavior of the actuators tested. Also, another element to consider was to identify the actuating pressure required to achieve amplitude of motion values as close as possible to the phalanx amplitude of motion values. In the case of the PIP joint, the value is approximately 120°. The conformity between the two values leads implicitly to greater efficiency and to a shorter recovery time.

Obtaining this information was achieved using the finite element program Abaqus (Dassault Systems—6.13-1), and the simulation is identified in Figure 5a. Modeling of the PneuNets actuators was performed using the Yeoh hyperelastic model [31], for non-linear and incompressible materials based on the experimental data of the graph in Figure 4 engineering stress and engendering strain. The strain energy function for determining the 2nd-order material constants is shown below in Equation (1):

$$\mathrm{W}={\sum }_{\mathrm{i}=1}^{\mathrm{N}}{\mathrm{C}}_{\mathrm{i}0}{\left(\overline{{\mathrm{I}}_1}-3\right)}^{\mathrm{i}}+{\sum }_{\mathrm{k}=1}^{\mathrm{N}}\frac{1}{{\mathrm{d}}_{\mathrm{k}}}{(\mathrm{J}-1)}^{2\mathrm{k}}$$

(1)

where W is a strain energy density, C_i0 are coefficients of the polynomial function, $\overline{{\mathrm{I}}_1}$ is the first strain invariant, and J is the determinant of the elastic deformation gradient. The values of the Yeoh material constants of degree 2 obtained are C₁₀ = 48.673 (Pa) and C₂₀ = 962.03 (Pa). From the simulations, it can be seen compared to Figure 5b that the simulated bending amplitude is approximately equal to the actual bending one.

2.3. Relationship between Pressure/Angle/Force in Bending Phase

To identify the characteristics that hold the actuating pressure, the amplitude of movement, and the force that the actuator can develop at different input pressures, an experimental test was carried out. Thus, two graphs (Figure 6a,b) were made in which the output characteristics, namely the bending angle and the force as a function of the input pressure, were determined.

Determination of the bending angle so that the value is as close as possible to the amplitude of the natural motion was accomplished according to the inlet pressure. A resistive flex sensor was used and integrated between the actuator layers. This sensor provides relatively accurate information about the angular position of the actuator as a function of the pressure inside the actuator chamber. We have used the Honeywell ABPDANV150PGSA3 pressure sensor, which has a pressure range of 0–150 psi. From the graph in Figure 6a, it can be seen that at a pressure of 0.06 MPa, an actuator deflection value of 117° is obtained, which represents an amplitude similar to that of the PIP joint. Regarding the output force as a function of the input pressure, it was measured using a force-sensing resistor. The force measurement occurs at the distal end of the actuator, and the characteristic curve is shown in Figure 6b, where it can be seen that at an input pressure of 0.06 MPa, the output force of the actuator measures 1.3 N.

2.4. Pneumatic Glove Control System

The control of the soft glove is presented in Figure 7. This control unit was realized at a low cost, which was possible due to the efficiency and use of alternative equipment that meets the specific requirements of the study but also has a relatively low cost. The approximate cost of making this control unit is 100 €. This control unit allows the supply of the simultaneous actuators through a pneumatic control system, which is composed of an electro-pneumatic valve (SMC—SYJ5120—24 V) that allows the pressurization and depressurization of the actuators. The pressure sensors (Honeywell—ABPDANV150PGSA3) and the pressure supply are taken from an air compressor. As for the control system, it consists of a development board equipped with a microcontroller (ATmega 328P), control relay, EMG module with three surface electrodes, a display for exhibiting information related to the number of repetitions performed by the patient, the pressure inside the actuators, and data from the flex sensor integrated into the actuator layers (grade °). Closed-loop control in the case of rehabilitation is achieved by the flex sensor closing the feedback loop. In the case of ADL, the feedback loop is closed by the pressure sensor, which provides pressure data inside the actuators. Since several voltage levels are required to supply the components, both pneumatic and control, 12 V and 24 V voltage regulators were used, respectively.

3. Results

3.1. Integration of Actuators within Wearable Gloves

This equipment with wearable glove-type soft actuators is based on PneuNets actuators that perform the specific flex motion by driving the phalanges of the five fingers in flexion. The schematic diagram of the operation is shown in Figure 8, where the positioning of the actuator on the dorsal side of the hands can be seen. The actuators are connected through hoses to the compressor, and when the pressure operates above 0, the actuator starts to perform the flex movement by flexing the phalanges. Between the layers of the actuator, the flex sensor (FS, Spectra Symbol, Salt Lake City, UT, USA) is positioned, which monitors the amplitude of the flexing movement.

The soft actuator wearable consists of a textile glove with an elastomer layer on the palm for a better grip owing to the higher coefficient of friction between the glove and objects. As far as the attachment of the glove actuators is concerned, it has been considered that the grip should influence their behavior as little as possible. That is why we have implemented nylon fibers to fix the actuators, as they provide good stability over time as well as minimal impact on the actuator. Actuator supply hoses with a diameter of 4 mm were used, and to avoid pressure losses from the inside, a silicone rubber zone was made at the inlet end of the hose into the actuator. The components of the wearable soft actuator glove device are shown in Figure 9.

A medium-sized glove was used to cover as wide a range of patients as possible. In stroke patients, anthropometric dimensions are smaller due to muscle atrophy. However, the size of the actuators as well as the glove can be adapted according to these anthropometric data.

3.2. Control Strategy Based on EMG Signals

The wearable device relies on actuators controlling an EMG controller (EMG Sensor Controller) that captures the muscle signal from the forearm to identify the user’s intention in performing a certain action. The EMG module consists of a signal processing unit and three gel surface electrodes that are positioned in the muscle area of interest (Flexor Digitorum Superficialis—FDS) of the forearm. Before positioning the surface electrodes, the skin was cleaned with medical alcohol to ensure that there were no layers of fat or impurities that would prevent or alter the quality of the captured signal. The EMG module provides an analog output signal of between 0 V and 9 V and has a supply voltage of between 3.5 V and 9 V—DC. The module is based on an integrated H124SG. To visualize the analog output signal from the EMG module captured from the FDS muscle of the forearm, a digital oscilloscope (DS1054, Rigol, Beijing, China) was used, which is shown in Figure 10 below. Experimental data were taken from the right hand of a healthy subject in a sitting position performing repeated finger flexion and extension exercises.

EMG signals were captured with a sampling frequency of 100 Hz, and the amplitude of the signal at the time of contraction (corresponding to the realization of the flexion movement) varied considerably in contrast to the baseline amplitude, which showed a much lower amplitude. Based on the variation of this signal at the time of FDS muscle contraction, the control strategy of the wearable device can be constructed by EMG control. The device control strategy based on control using EMG is one of relatively low complexity but has provided satisfactory results in rehabilitation phases. The strategy for control powering the actuators is based on closed-loop control, which is shown in Figure 11a, where the main components of the system are also pictured. The black arrows represent the electrical signal, and the green arrows serve as the compressed air supplying the actuators.

In order to better highlight the control logic via EMG signals, a flowchart has been created, shown in Figure 11b, highlighting the conditions that must be met to achieve the flex motion. The three conditions necessary to be able to supply air to the actuators and achieve the finger flexion movement are related to the amplitude value of the captured EMG signal (A) exceeding the set threshold (B). This threshold is chosen in advance based on initial assessments of the patient’s condition. The second condition is that the pressure sensor (P.S.) has a nominal value of 0 MPa and the flex sensor (F.S.) has a nominal value of 0°. For example, if the patient intends to grip an object, the electrical signal of the muscles is continuously monitored, and when the signal amplitude exceeds a certain amplitude value (particularly pre-set in advance for each patient), the electro valve opens, supplying the five actuators that drive the fingers in flexion movement with pre-selected power. The pressure supply to the actuators is switched off when the flex sensor value reaches approximately 120°. When reaching this value, the actuators will be held in position for two seconds by switching the solenoid valve to the intermediate position. After two seconds, the solenoid valve will open and depressurize the actuators. If controlled depressurization is desired, the solenoid valve will switch between the intermediate and open positions. When the amplitude value of the EMG sensor exceeds that value again, the above steps are repeated.

3.3. Rehabilitation/Assistance Training Using the Wearable Device

Rehabilitating stroke patients’ hands to regain mobility and the ability to perform daily activities involves rehabilitation training based on repeated flexion/extension movements of the hands. Our tests were performed on healthy subjects who have residual EMG signals on the forearm muscles, being able to easily implement EMG signal-based control with high signal amplitudes. In patients who have suffered strokes or other neurological diseases, the residual EMG signals in the forearm area are relatively low, making it difficult to capture and process the signal, in which case there is the possibility of positioning the EMG electrodes on the biceps muscle area as it has a higher density of muscle fibers and therefore higher signal amplitudes for control. Based on this, it was decided to perform rehabilitation training consisting of several sets of flexion/extension finger movements with an amplitude of approximately 120°. Prior to the start of the rehabilitation training, gel surface electrodes are placed on the patient’s FDS muscle of the forearm corresponding to flexion/extension finger movements. The surface is degreased beforehand, and the analog value from which the actuator pressurization will start is determined based on the patient’s varying signal. Actuator pressurization is raised up to a value of 0.06 MPa and a movement amplitude of approximately 120°, monitored by the flex sensor. All of these data are displayed in real time together with the number of repetitions performed by the patient on the display of the control unit. Closed-loop control provides good and efficient controllability of the wearable device for flexion/extension finger movement training. Once the analog value is set (EMG signals) according to the patient, the wearable device is positioned on the patient’s hand so that it grants comfort. After positioning the device, the rehabilitation training may stop when the set analog value of the EMG signal is exceeded.

Regarding the use of the wearable device for hands-on assistance in everyday activities, a series of tests were conducted on different objects to identify the potential of such a device. The two cases of rehabilitation and assistance share some similarities in this case, namely from the perspective of the movements performed by the patient (flexion/extension), therefore the testing of the device in the cases of assistance on different objects with different geometries was approached. The tests can be identified below in Figure 12.

Several objects with different geometries were tested, representing items that are more commonly found in a home and with which patients are most likely to interact. These objects also have different masses, namely a plastic cup (42 g) in Figure 12a, a water bottle (280 g) in Figure 12b, a ball (17 g) in Figure 12c, and a cylindrical piece (110 g) in Figure 12d. To grip these objects, all five fingers were pressurized with a lower amount of pressure than in the rehabilitation case. From the tests performed, the device offers sufficient flexibility, but the limiting aspects are related to the control unit, which allows a reduced working space due to the length of the actuator cable, with this being an important future direction.

4. Conclusions and Future Works

Due to the social context in which an increasing number of people suffer strokes or other neurological diseases that leave them with various impairments affecting mobility, especially that of the upper limbs, it is necessary to carry out rehabilitation training to regain mobility and perform activities of daily living (ADL) anew. In this particular context, the present work addressed the realization of a wearable exoskeleton-type device for the upper limb to assist the patient during rehabilitation exercises, as well as the possibility of facilitating different daily activities, providing the possibility of re-enabling one’s personal environment. The device provides active rehabilitation/assistance, being based on capturing the patient’s muscle intention within the forearm muscles. PneuNets actuators provide flexion motion actuation of the phalanges (DIP, PIP, MCP) for fingers 2–5 and (IP, MCP) for finger 1, and the extension motion is passively controlled by depressurizing the actuators. Two different actuator lengths were used depending on the anatomical dimensions of the fingers. A rigorous procedure was followed for the manufacture of the actuators, based on a series of steps to ensure that the actuators would be consistent and reliable over a long period. The two main manufacturing technologies in the field of soft robotics were used, namely casting and 3D printing to produce the casting mold. Two-component elastomeric material (RTV ZA 22 A) with a Shore hardness of 22 A was subjected to uniaxial tensile testing until breakage to determine the material’s mechanical characteristics and to perform FEA simulation based on material constants. It was taken into account that the actuator performs the bending motion with an amplitude approximately equal to the amplitude of motion of the PIP joint, so the actuator behavior was determined as a function of the input pressure, and the degree of bending of the actuator as well as the output force developed by the actuator were monitored with a flex sensor. At a pressure of 0.06 MPa, the bending angle is 117° and the force developed is 1.3 N. The control unit of the wearable device is mainly based on microcontroller-based information processing electronics and flow control elements such as pneumatic valves that distribute fluid to actuators. The exoskeleton-type wearable device has a relatively elementary structure consisting of a textile glove with a silicone rubber on the palmar area to improve grip and PneuNets actuators, which are positioned on the back of the hands using nylon fibers. This structure is designed to be effortless to use and wear while being comfortable for the patient. Moreover, the cost of its realization is really low. The wearable device is mainly intended for active rehabilitation of the patient’s hands, but it is also possible to assist in various daily activities. The tests were performed on healthy subjects and were based on a series of flexion/extension movements of the fingers in the three joints (DIP, PIP, MCP, and IP), as well as through the use of different objects with different geometries and masses. These movements are based on the capture of the patient’s muscle intention through EMG surface electrodes.

As a future direction, the authors intend to increase the modularity and flexibility of the control unit, as currently, this unit brings some limitations in terms of its volume as well as its portability, also limiting the patient’s room for action in case of assistance. Another issue the authors are considering as a future direction is related to the integration of the wearable device with virtual reality (VR) and the LeapMotion controller. By using VR and the LeapMotion controller, a series of specific rehabilitation games can be designed tailored to the patient’s needs. The LeapMotion controller as an immersive element in VR provides the possibility for the patient to interact with different objects in VR, improving motivation and the level of interaction. Another aspect that the authors wish to develop is the integration of the wearable glove into a wearable upper limb device that assists both the wrist (flexion/extension and abduction/adduction) and the elbow joint in flexion/extension movement. It is also intended to validate the device by performing tests on patients with hand movement impairments due to stroke or other neurological diseases.