Novel soft bending actuator-based power augmentation hand exoskeleton controlled by human intention

In the increasingly rapid development of recent decades, one important area of innovation has been alleviating heavy tasks [1]. Human–robot power assistance refers to the utilization of robotics systems to raise human functionalities in various operational cases [2]. Generally, the power assistance of people’s functionalities using robotic systems refers exclusively to augmentation in mechanical abilities including movement control and limbs output force. This involves complex functionalities and taking advantage of perceptual abilities. There are cases of working conditions in which human–robot assistance can form a fundamental part of the operation, e.g. tasks for wreckage elimination after an earthquake in which human employees, wearing such devices, can move and work in a more adaptable and precise way than machines like excavators or cranes.

Different situations are exemplified by functions in which the number of repetitive actions, rather than the scale of loads to be lifted, is high and turns into the critical factor for diminishing fatigue, and thus maintaining long-term levels of optimal functioning. A wearable robot is a particular kind of wearable gadget that is utilized to improve a human’s movement as well as physical capabilities. Wearable robots are otherwise called bionic-robots or exoskeletons. One of the general features of an exoskeleton is that it includes physical equipment for supporting the human movement. Exoskeletons can help people to walk, which might be utilized for post-surgery or rehabilitation issues.

Researchers on exoskeleton robots have received a strong contribution from the improvement in haptic interfaces, i.e. exoskeleton systems that can create forces on the human body while maintaining a high level of naturalness of wearer’s motions. The researchers on haptic technologies in the last decade have concentrated on the significance of designing exoskeleton robots able to produce forces at the level of the upper-limbs or different parts of the wearer body. As a result, an expanded definition of the robotic community to include exoskeleton robots has been registered. The major part of recent research on wearable robots derives from their application in neuro-rehabilitation, where the exoskeleton system is utilized to dynamically help the patient’s appendage with the execution of particular activities [3, 4].

This paper depicts the development of novel bending pneumatic soft actuators for utilization in a power augmentation soft glove. A mathematical model of the new bending actuator has been developed and verified against experimental results. This soft actuator is then deployed in an exoskeleton glove for hand power augmentation for healthy or partially disabled individuals. This soft exoskeleton is able to fit any adult hand size without the need for any mechanical system changes or calibration. A hybrid cascaded position/force intelligent control system has been developed to control the proposed prototype. The performance of the proposed system has been proven experimentally. The potential beneficiaries of the system are post-stroke patients and the elderly who have reduced strength and the exoskeleton may allow for increased independent daily living. There are potentially significant philological benefits to be gained from enhanced patient independence.

The human hand consists of four fingers and a thumb [5] and each finger has three joints (see Fig. 1); the thumb has a different bone structure and its joints are named differently. The index, middle, ring, and little finger joints are named as follows:

The thumb finger joints are named:

Interphalangeal joint (IP) terminal joint of the thumb; connects the proximal phalange to the distal phalange.

For the fingers (index through little), the carpals and metacarpals can be assumed to be fixed at the wrist joint. In reality, there is a small degree of abduction in the metacarpals (which can be distinguished by pressing the little and index fingers together); however, this is a generally a minor part of the general hand movement. The three bones in the finger are the proximal phalange, the middle phalange, and the distal phalange. For the thumb, the metacarpal moves significantly and there are just two phalangeal bones: the proximal phalange and the distal phalange (proximal, middle, and distal phalanges).

During the last two decades, force augmentation or rehabilitation exoskeletons have been attracting more interest. Ergin and Patoglu [6] introduced an exoskeleton device for rehabilitation exercises for shoulder and elbow segments called ASSISTON-SE. ASSISTON-SE is designed to amplify the force movements of both passive translational movements of the centre of the glenohumeral joint, and independent active control has been used. Implementation details for their prototype have been provided, as well as the results of numerous experiments done for their prototype, to prove the ability of this device to track movements of the shoulder girdle. Martinez et al. [7] designed and presented implementation and specifications of the forearm and wrist rehabilitation device for stroke patients. This device provides 3 DOF movements for the wrist joint and forearm segment to support pronation/supination, flexion/extension, and adduction/abduction joint motions. Using three Maxon DC motors for actuation, the design focused on the safety requirements by using mechanical rubber parts and an easily accessible emergency stop switch. Yamamoto et al. [8] developed a rehabilitation robot for supporting patients when performing their rehabilitation exercises. This design is capable of detecting the human intentions and supports any movements of the wrist joint. This device is also small and lightweight, so that patients can use it in a clinic or at home. Actuator units receive a biological signal from the muscles to decide which movement the patient needs. Xiang et al. [9] presented a one DOF wrist and forearm rehabilitation device for stroke patients. This device is capable of providing different training modes and creating a virtual-reality game for the patient to perform the training. The device had been examined by two stroke patients and one chronic patient with left hemiplegia, and the results were successful. The robot is reconfigurable to each rehabilitation mode, and it is portable. Chen et al. [10] presented a rehabilitation robot called NTUH-ARM to rehab the human upper-limb. This robot has 7 DOFs actuated by FAULHABER DC motors to perform most upper-limb rehabilitation exercises. Two 6-axis force/torque sensors were used to provide a movement capture for the monitoring and the controller. This device was tested clinically by six patients to evaluate the performance of the robot, and the results were examined by physical therapists, also revealing promising results. Otten et al. [11] developed a hydraulically powered self-aligning upper-limb exoskeleton for identifying the reflex properties of the shoulder and elbow joints in stroke patients. Powerful hydraulic motors are used to actuate this rehabilitation device and also to generate high torques and power using lightweight actuators. This exoskeleton is also used for diagnostic purposes to diagnose the level of muscle/neuron damage in the upper-limb. Vitiello et al. [12] proposed a powered elbow exoskeleton designed for post-stroke physical rehabilitation, ensuring maximum safety and comfort to the patient. They used lightweight mechanical materials to minimize the pressure on the skin. This device has 4 DOFs to drive elbow flexion and extension movements. These DOFs are actuated hydraulically by using two cylinders and tendons to enable suitable control for the joint movements under rehabilitation training conditions.

Force assistive or rehabilitation exoskeletons have to be: (i) inherently safe because they are in direct contact with humans, (ii) lightweight, for easy use and portability, and (iii) able to fit a person without extensive adjustment and calibration. Unfortunately, conventional actuators and inflexible, serial connection manipulators are not fully appropriate for these prerequisites. In any case, the generally new field of soft robotics technology may be able to address these issues.

Polygerinos et al. [13] developed an exoskeleton glove using soft muscles made from elastic materials such as liquid silicon. The actuator design is complex meaning the manufacturing process is slow and laborious. The Actuation force is comparatively low at 8N although the authors say this is sufficient for many tasks of daily living. The overall glove weighs 285 g which although lighter than other hand exoskeletons is still a significant load to support at the hand for extended periods, especially for the elderly or weak. The same authors used EMG signals to control their exoskeleton [14]. However, the research faced problems with the releasing movements because the EMG signal characteristics of the release movements are similar to the wrist joint movements and this resulted in a conflict between the signals.

Toya et al. [15] also developed force assistance glove based on moulded bending muscles. The actuators used in this prototype meant the glove was more lightweight than Polygerinos’ at 180 g but the output force was just 3 N per finger, and the operating pressure was limited to 300 kPa. A 3D-printed soft actuator is also presented by Polygerinos et al. [16] to create a rehabilitation glove. This prototype is lighter still (160 g) but still suffers from comparatively low force output. There is therefore a need to develop gloves with higher force output and reduced weight which can be manufactured simply and rapidly.

Pneumatic soft artificial muscles are driven by air pressure and are created from soft materials such as rubber tubes acting as a bladder and braided sleeves. They are inherently safe actuators for direct human interaction because of their lightweight and lack of rigid parts. These features also make them suited to use in exoskeleton robots. The pneumatic artificial muscle (PAM) [17], also called McKibben Muscle, is a tube-like an actuator that is characterized by an increase or decrease in length when inflated or deflated [18]. The PAM is generally designed and constructed from a rubber or latex bladder tube which is encased in a braided sleeve and secured to rigid caps at both terminal ends. The PAMs transform applied pneumatic pressure into either a compressive or tensile force. These actuators have many advantages, for example, high power to weight ratios, the ability to be used as a direct drive, inherent safety, compliance and low cost.

One of the main problems of using soft pneumatic muscle actuators is in the mathematical modelling which is highly complex because of their nonlinear behaviour. There have been many attempts to model their behaviour but there is still no accurate and definitive model. Indeed Tondu [19] stated that “difficulty in modelling could be peculiar to a large range of artificial muscles and might be the price to pay for substituting ‘soft’ actuators for rigid actuators”. This suggests that control techniques that rely on accurate mathematical models may not be suited to controlling pneumatic muscles.

As a rule, the majority of natural physical devices are nonlinear. Nonetheless, if the range of control operation systems is limited, and if the nonlinearity of these systems is smooth, then the controller of these devices may be a set of linear controllers which serve the purpose and produces acceptable results. The numerous advantages of PAMs have impelled researchers to investigate the control area of PAMs; the nonlinearity of these actuators is the major challenge in modelling and controlling them. Numerous control methods have been developed to solve this problem.

Shen [17] presented a nonlinear model-based method of control of PAM servo devices. This technique proposed a controller method for servo systems-based PAMs to deal with the four major processes in these devices: the flow, pressure, force, and load dynamics. Depending on these processes, a fully nonlinear model and the efficient controller were created. A position control approach depending on sliding mode controller relay type for a robot arm manufactured by soft pneumatic actuators was developed by Sárosi et al. [20, 21]. An adaptive controller based on fast hybrid fuzzy technique was proposed by Hosovsky et al. [22] as a nonlinear controller for PAM, and the adaptive control was also done by using Genetic Algorithms. Nuchkrua and Leephakpreeda [23] presented a real-time conventional PID self-tuning by using fuzzy logic to create an efficient nonlinear controller for PAMs. A tracking controller for PAM was presented by Qian et al. [24], who used a combination between a conventional sliding mode control method and a fuzzy control to create a robust adaptive controller. Finally, a fuzzy-PID self-tuning controller was also used by Sun et al. [25] to control a damping seat based on PAMs, used in a crawler construction vehicle. Many controller methods have been developed in this area (soft pneumatic actuators) in many industrial and medical applications because this is an interesting field for researchers to create safer robots and machines for human direct interaction.

The novel bending artificial muscle has been developed using a braided sleeve with a minimum diameter of 10 mm and a maximum of 23 mm, a tubular rubber bladder with length of 16 cm and diameter of 10 mm and 3D-printed endcaps (one has a hole for air supplied pressure). However, the sleeve length was double that of the bladder, giving the muscles a resting diameter of 23 mm. Due to the braid being longer than the bladder, the muscle will extend in length when the supplied pressure is increased; this type of muscle is known as an extensor. First, the rubber tube is secured at each end to the two endcaps, the sleeve in then places around this and then again secured to the two endcaps using a cable tie. Due to the fact the braid is longer than the bladder, it must be compressed in length. Figure 2 illustrates the behaviour of the proposed extensor artificial muscle under different supplied pressures (without any load attached). It can be seen that the extensor artificial muscle expands as the supplied air pressure is increased. The maximum increase in the muscle length was measured experimentally to be 68% under 500 kPa supplied pressure. Figure 3 shows the characteristic behaviour of the proposed extensor actuator in relation to the supplied pressure. During this operation, an axial extensile force is produced at the free end of the muscle.

The bending artificial muscle is derived from the extending muscle by reinforcing one side of it using a fixed length thread (inextensible) with a 500N breaking strength and leaving the other side free. Figure 4 shows the same bending muscle with different applied pressures.

When pressure is increased to the bending artificial muscle, the bending angle starts to increase in proportion with increasing pressure, because it is forced by the thread to extend on the free side only. The curving angle of the extensor bending muscle increases with increasing supply pressure, and this relationship was investigated experimentally. The pressure inside the muscle was progressively increased, and the angle of the remote terminal of the actuator with respect to its initial position was measured. Figure 5 outlines the relationship between the provided pressure and the curve angle of the proposed bending actuator. It can be seen that the bend angle is proportional to the air pressure. The bending angle was measured by photographing the actuator at each pressure and then manually measuring the angle between a line extended from the free end of the actuator and the vertical axis.

Whilst there have been pneumatic bending actuators produced in the past, as discussed in Sect. 2, this work presents a novel McKibben muscle-based bending actuator. As stated, the intended application for the actuators is in a force augmentation glove. Therefore, the design specifications of the glove will determine the required actuator specifications. The glove is intended to be lighter in weight than the systems described in Sect. 2, with a target weight of 150 or 50 g per digit. The glove should provide greater force output at the fingertip than existing soft systems with a target of 25 N per finger. The system should also operate at the full range of common pneumatic pressures (0–500 kPa), be easy and rapid to construct, be formed from low-cost materials and allow the full range of human finger flexion.

One of the drawbacks of most exoskeletons is that they have discrete joints, which must be effectively aligned to the wearer’s limb joints [28‐30]. The wrong alignment may harm the wearer’s limb joints, and calibration is therefore required when another individual uses the exoskeleton. The main advantage of our novel bending actuator is that it does not have discrete joints, rather the whole actuator flexes. If the muscle is placed at the back of a finger, when pressurized it will apply force to the whole back of the finger bringing about bending at the joints. In view of this idea, a soft exoskeleton glove was constructed. The power augmentation soft glove was constructed depending on our novel bending pneumatic artificial muscle, as shown in Fig. 11. Four bending actuators are reinforced (sewn) on the back surface of a traditional worker’s leather glove; one muscle for each finger except the little finger.

The supplied air for all muscles is controlled by a solenoid valve type MATRIX \(3\times 3\). The proposed soft exoskeleton glove has a weight of approximately 0.12 kg. The proposed exoskeleton sensing data are helpful to gather enough data about a hand motion to create appropriate assistance by the controller.

There are two (2.2 in. SEN-10264) flex bend sensors placed between the soft actuator and the glove on the index finger joints; one bend sensor to capture the bending angle of the root of the finger is named MP joint (metacarpophalangeal Joint) and the others are for the PIP middle joint (proximal interphalangeal joint) and the DIP terminal joint (distal interphalangeal joint). An assumption of the controller is that all fingers bend together at the same time for full grasping movement; therefore, the index finger flex sensors provide the feedback of grasp movement angle of the hand. A force sensitive sensor (SEN-09375 with sensing area: \(0.5''\)) on the front side of the glove (on index fingertip) is also included. This sensor is attached to feedback the tactile press force of the index finger when grasping an object. Air pressure sensor is attached to the soft bending actuators for real-time feedback of the pressure inside the glove muscles.

A significant problem of power augmentation and rehabilitation exoskeletons is the grasping release movement. Due to power augmentation (when actuators are pressurized), the wearer may not be able to deliver enough force in the opposite direction to the actuated muscles to open the fingers or release the grasped object. For instance, if the wearer grasps an object with the exoskeleton’s support, he may not be able to open his hand to release this object until the assisted muscles deflate, because his fingers’ force may be less than the assistive force.

Noritsugu, Takaiwa and Sasaki developed an exoskeleton for human hand grasping assistance without any information about a release movement [28]. Numerous hand exoskeletons such as [13, 31‐33], use manual control to release the grasps. Some rehabilitation hand exoskeletons such as [30, 34, 35] do not need controllers to release the grasp as movements are based on open-loop controllers for repetitive movements to grasp and release.

According to Kiminori et al. [29], the release movement was solved by estimating the grasping time needed before releasing the fingers (fixed time for all grasping types), which was not an effective solution, in light of the fact that there is no settled time for human hand grasping for one individual, and in this way it is difficult to gauge the time for numerous different people. Other researchers [14, 36, 37] have attempted to solve this problem using electromyography (EMG) signals, however, this encountered problems because the features of human hand EMG signals for releasing a grip are similar in a wide range to the features of human wrist movement, so they assumed that if the release grip signal occurred, the wrist was fixed and vice versa.

The proposed solution of the release movement problem is to leave the little finger without any assistance muscle. Instead of the assistive muscle, we placed a flex bend sensor size \(4.4''\) on the back of the little finger to provide the controller with real-time feedback on the little finger bending angle. As normal, when grasping an object, all fingers will bend, then the controller will start working. If we need to release the object, at first we release the little finger (because it is free of assisted force) then depending on the angle of the little finger (it will reach zero bend angle) the controller will understand it is a release movement then deflate all actuators.

At all events, the control precision and stabilization of the pneumatic actuator, for example, pneumatic cylinder or McKibben muscle presents a major challenge, because of both the nonlinear working behaviour and the compressibility of air. The classical control techniques are regularly created via simple models of the actuator functionality that fulfil the important assumptions and through the uncommon tuning of comparatively basic linear or nonlinear controllers.

Figure 12 illustrates the proposed controller system flow chart. The initial state of the control system has vented all actuators (all bending muscles with zero air pressure). The next state is reading all sensors, flex bend sensors (on the index and little fingers), force sensitive sensor (on index fingertip) and the pressure sensor.

The first conditional block in the proposed algorithm is testing the little finger bending angle (if little finger bending \(\hbox {angle} = 0\)) if there is no bend or return to the straight position, the release movement state will occur as explained in the previous section. If there is bending in the little finger, continue to the next condition. The second condition is to test the force sensitive sensor; whether the index fingertip is in contact with the object or not (the \(\hbox {force} > 0\)). If there is no force applied, start the position controller, and if there is a force, start the force controller. In other words, when the wearer needs to grasp an object, the wearer start to bend his/her fingers to touch the object (the position controller system, in this case, actuate the muscles to follow the fingers until the fingers be in contact with the object then the controller system stop the position controller and switch on the force controller depending on the index fingertip press force).

Human assistance innovation is essential in an increasingly ageing society and one technology that may be applicable is exoskeletons. However, traditional rigid exoskeletons have many drawbacks. This paper has introduced a bending pneumatic muscle which can be used to develop soft exoskeletons.

The paper has described the construction of a novel bending pneumatic muscle which is based on an extending McKibben muscle. By reinforcing one side of the muscle to prevent extension, a bending motion is produced when the actuator is pressurized. The performance of the proposed actuator has been experimentally assessed, and an output force mathematical model for the proposed actuator has been developed. This model relies upon the geometrical parameters of the extending bending pneumatic muscle to determine the output force as a function of the input pressure. The model has been verified against experimental results for the proposed actuator.

The control system of this exoskeleton was created by hybridization between both cascaded position and force closed-loop intelligent controllers. The cascaded position controller is designed for the bending actuators to follow the fingers in their bending movements. The force controller was developed to control the grasping force augmentation. Validation of the control system with the exoskeleton has been done experimentally in this research. EMG signals were monitored to validate whether the proposed exoskeleton system decreased the muscular efforts of the wearer during the experiments.

Future work will seek to improve the mathematical model further by considering other losses such as rubber bladder impedance, the friction between the bladder and the braided sleeve, and the friction between the fibre threads in the braid. This will be done to improve the mathematical model and decrease the average percentage error. The controller system should also be developed to be more effective at decreasing the steady state errors with overshoots.