Wearable upper limb robotics for pervasive health: a review

Neuromuscular disorders are diseases that pathologically originate in the musculoskeletal system, the nervous system, or the interfacing between the two, such as amyotrophic lateral sclerosis (ALS), muscular dystrophy, spinal cord injury (SCI), etc [1]. Strokes are now reclassified as a neurological disease according to the World Health Organization's new International Classification of Diseases (ICD-11) [2]. Although the clinical presentation of these diseases may vary, they normally affect muscle control and sensory feedback and result in symptoms such as muscle weakness or paralysis, muscle spasticity, and even non-functional muscular systems [1, 3]. Some neuromuscular disorders cannot be fully cured and thus rely on enhancing rehabilitation and quality of life [3, 4]. Although still growing, exoskeletons are promising assistive technologies for maintaining patients' activities of daily living (ADL) [5] as well as therapeutic devices for rehabilitation and treatment [6]. Thus, exoskeletons provide new and/or alternative solutions for both in-hospital and out-of-hospital healthcare towards pervasive health. By definition in the dictionary, pervasive refers to 'existing in or spreading through every part'. Pervasive health is defined as 'healthcare to anyone, anytime, and anywhere by removing locational, time, and other restraints while increasing both its coverage and quality' [7]. Although pervasive health sometimes has a broader definition including preventive proactive healthcare [7], our literature review is mainly focused on the studies within this direct definition—care anywhere beyond clinical settings for patients and their caregivers.

Neuromuscular disorders, cerebrovascular diseases, and other injuries and disabilities can lead to upper and/or low limb motor impairments and dysfunctions. Upper limbs are the key to human beings' manipulation with the environment and thus their motility is crucial to patients' independence, self-esteem, and quality of life. Upper limb exoskeletons have been developed and applied for assistance and rehabilitation in the past two decades [8] and have shown their feasibility and effectiveness [9, 10]. They have also been combined with other therapeutic solutions for neurological or neuromuscular treatment [10, 11]. However, not all robotic systems are initially designed with the full consideration of clinical use [12] and thus iterations are often required. Therefore, understanding both technical and patients' needs in both in-hospital and out-of-hospital settings is highly desired for wearable robot design.

Traditional exoskeletons for upper limbs are normally built upon rigid links and joints and apply forces and torques to upper limbs. Soft exoskeletons also called exosuits that are flexible, portable, and lightweight have also been successfully developed in recent years for various applications. Inspired by the controllable structures of textile materials, a variety of new textile actuators and softer and more compliant wearable robotics have been reported with remarkable studies ranging from material design, actuator design, to robotic design towards wearable textile robotics like everyday garments.

For wearable robotics, both rigid and soft, a number of literature review articles have summarized the recent research progress with different focuses, ranging from robotic designs to their applications. Gopura et al [13] and Muhammad et al [14] comprehensively reviewed upper limb exoskeleton system designs and Rasedul et al [15] compared the research prototypes and commercial types of upper limbs. Some articles specifically performed literature reviews for key components for wearable upper limb robotics including mechanical designs [16], control strategies [17], actuation systems [18], and motion planning [19]. Most of these articles are for rigid exoskeletons. In recent years, soft and textile exoskeletons are emerging. Due to the softness and compliance of such robots' materials, various studies have been performed with special focuses on smart materials and structures for motion generation. To this degree, some remarkable literature review articles have been performed for summarizing new materials, structures, actuators, and designs of soft and compliant structures and robotics [20–23]. Only one has reviewed both rigid and soft exoskeletons with a focus on the shoulder joint [5].

Wearable upper limb robotics by nature provide additional human mobility, which has broad applications. For pervasive healthcare, the applications of wearable robotics mainly include ADL assistance and patients' rehabilitation in various settings. Some specific domains have been studied in both research and clinical field. Mekki et al [10] reviewed robotic rehabilitation for SCI including both upper and lower limb robotics. Zuccon et al [24] and Xu et al [25] performed surveys about robotic rehabilitation after strokes. Gassert and Dietz [26] focused on studies using robotic devices for the recovery of sensorimotor function. Some other articles reviewed general rehabilitation robotics from different angles and with different scopes [27–34]. Although wearable upper limb exoskeletons have been used for both daily assistance and rehabilitation in clinical studies, there are still gaps between wearable robot design and patients' needs. For long-term use in both in-hospital and at-home settings, new technologies are highly desired with the consideration of comfort, biocompatibility, and operability. The recent progress in soft exoskeletons may provide new opportunities for pervasive health.

This paper aims to provide a systematic literature review of wearable upper limb exoskeletons for pervasive health considering both technical design perspectives and healthcare applications. The recent progress in both rigid exoskeletons and emerging soft wearable robotics and their applications in pervasive health is summarized. Particularly, this article covers the robot designs with the consideration of typical disease treatments, rehabilitation therapies, and assistance, aiming to provide integrated technical and clinical guidance. Upper limb exoskeletons provide not only effective therapies but also a promising solution for improving patients' self-esteem and quality of life. However, there are still technical challenges, and research contributions are highly expected towards affordable, ubiquitous, and comfortable assistance and treatment.

The rest of the paper is organized as follows: section 2 summarizes the upper limb kinematics and biomechanics. Section 3 classifies exoskeletons and gives an overall introduction. Sections 4 and 5 cover the technical designs of rigid and soft exoskeletons, respectively. Section 6 summarizes the research progress of wearable robotics as therapeutic devices for practical pervasive health applications. Section 7 discusses the challenges and opportunities, and section 8 concludes the paper.

Exoskeleton designs require careful consideration of human biomechanics and kinematics. The upper limb motion involves the interaction of the nervous systems, musculoskeletal systems, and their interfaces. In human anatomy, the muscles that move the upper arm are those connecting to the humerus, which include the pectoralis major, latissimus dorsi, deltoid, and rotator cuff muscles. And the forearm movement is based on the triceps brachii, biceps brachii, brachialis, and brachioradialis. The overall upper limb movement can be considered as a kinematic chain consisting of three joints, the shoulder joint, elbow joint, wrist, and two links, the upper arm and forearm as shown in figure 1. The mobility can be simplified to seven degrees-of-freedom (DOFs). The shoulder joint has three DOFs, the shoulder internal/external rotation (β₁), shoulder abduction/adduction (β₂), and shoulder flexion/extension (β₃); the elbow joint allows a two-DOF motion, elbow flexion/extension (β₄) and elbow supination/pronation (β₅). The wrist joint allows two-DOF wrist flexion/extension (β₆) and wrist abduction/adduction deviation (β₇) motion, which are associated with hand motion and will not be discussed in detail in this paper. This kinematic model is the basis for upper limb exoskeleton design as well as for rehabilitation estimation. The actual human joint mobility given by bones coupled with muscles usually involves a limited translation and cannot achieve a fully rotational motion. Using Denavit–Hartenberg (D–H) convention in figure 1, we briefly list the physiological range of motion (ROM) of each angle and the four corresponding D–H parameters in table 1. Robotic ROMs are often used as evaluation metrics for rehabilitation robotic design. A number of great reviews have been performed for hand rehabilitation [35, 36], which will not be the main focus of this paper.

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Table 1. Human upper limb ROM and D–H parameters [37].

Joint	Physiological ROM of ${\beta _i}$	i	${\alpha _i}$	${a_i}$	${d_i}$	${\theta _i}$
Base	Zero	${1_{\left( {0 \to 1} \right)}}$	0	${a_0}$	${d_0}$	0
Shoulder	Internal rotation (−90°), external rotation (+90°)	${2_{\left( {1 \to 2} \right)}}$	−90°	0	0	${\beta _1}$+90°
Shoulder	Abduction (−180°), adduction (+50°)	${3_{\left( {2 \to 3} \right)}}$	+90°	0	0	${\beta _2}$+90°
Shoulder	Flexion (−180°), extension (+80°)	${4_{\left( {3 \to 4} \right)}}$	0	${l_u}$	0	${\beta _3}$+90°
Elbow	Extension (−10°), flexion (+145°)	${5_{\left( {4 \to 5} \right)}}$	+90°	0	0	${\beta _4}$+90°
Elbow	Pronation (−90°), supination (+90°)	${6_{\left( {5 \to 6} \right)}}$	+90°	0	${l_f}$	${\beta _5}$+90°
Wrist	Flexion (−90°), extension (+70°)	${7_{\left( {6 \to 7} \right)}}$	+90°	0	0	${\beta _6}$+90°
Wrist	Abduction (−15°), adduction (+40°)	${8_{\left( {7 \to 8} \right)}}$	0	${l_h}$	0	${\beta _7}$

Although the above-mentioned seven DOF kinematic model has been widely used for understanding and analyzing human upper limb motion, the physical joint designs can be various for different DOF generations in both rigid and soft exoskeletons. In traditional rigid exoskeletons, the DOFs can be generated by actuators with rigid mechanical support or housing systems. In soft exoskeleton designs, more ideas have been proposed including those inspired by the human musculoskeletal system itself. For example, the shoulder, elbow, and wrist joint were modeled as a spheroid joint, a hinge joint, and an oval joint, respectively in [38]. More detailed analyses of human upper limb anatomy can be referred to [5, 39].

Among rehabilitation and ADL assistive robots, there are two major types of robot designs, end effector based systems and wearable exoskeletons. End effector type robots are more like independent robotic manipulators and apply forces and torques to the human distal end and measure at the interface [40]; whereas exoskeletons are wearable and operated in parallel with upper limbs with multiple points connected. End effector types of rehabilitation robotics can provide motion trajectory of hand for therapy and estimation and are relatively easy to be customized to individual tasks; however, the human joint angles (β_i ) during tasks cannot be directly not known from the robotic device [41]. Wearable exoskeletons are attached to and move with human arms and joints and provide forces and enhance mobility as a human-in-the-loop system. They can control human joints and provide multi-DOFs but are normally more complex to design and control [32]. Exoskeleton designs are more wearable, portable, and lightweight in recent years, enabling more application scenarios towards pervasive health.

Exoskeletons can be active (i.e. powered) or passive. With the first powered exoskeleton developed in the 1960s [42], there have been a great number of exoskeletons developed for ADL assistance, therapy and rehabilitation, to capability augmentation. Most of the powered exoskeletons are controlled by human physical or physiological conditions detected by sensors. These control inputs include force and torque, position and velocity, joint angle, electromyography (EMG), electroencephalogram (EEG), impedance, or hybrid. Different sensors and signal processing and data fusion methods have been developed to estimate human inputs and conditions for robotic control. As human interfaces, surface EMG (sEMG) measures electrical signals on the skin that are generated in the underneath muscle during its contraction and relaxation, which is generally believed to be able to represent neuromuscular activities including motor intention [43–45]. In addition, sEMG has also been studied to estimate torque and impedance for robotic control [46]. EEG detects electrical activities on the human scalp and is widely used as a noninvasive brain interface. EEG coupled with EMG has also been attempted for motor intention estimation for robotic control [47, 48]. Passive exoskeletons have no actuation systems and are often used for ergonomic support to prevent injuries, reduce workload, and augment human capabilities, which have been used in different industrial sectors. Some passive exoskeletons are available in the market for occupational health and safety [49, 50]. Designed with the consideration of human biomechanics, it is believed that these industrial use passive exoskeletons can prevent work-related musculoskeletal disorders for occupants [51]. This literature review article will not cover preventive health cases. Occupational exoskeletons have been explicitly reviewed by [51–53].

Exoskeletons can be rigid or soft. Most of the exoskeletons used for pervasive health applications are still rigid. In the recent decade, soft exoskeletons, also called exosuits, have been emerging with the fast growth of soft robotics technology. The term 'soft robotics' includes two types of designs, compliant joints or actuators within rigid robots and continuum robotics [54]. Continuum robots consist of actuatable structures with constitutive materials (rigid or soft) able to achieve controllable curves. Actually, as a broad concept, soft robots are not always completely soft and sometimes contain rigid structures. Similarly, exosuits are not fully soft and sometimes use rigid cables and support structures. The power and batteries are also rigid and often hidden in the backpack. Sometimes the term 'exosuit' can be broader and refer to all types of wearable robots that are like 'suits' even with rigid parts in the design. Thus, the boundary between rigid and soft exoskeletons is actually vague. In this article, we will use the following definition to classify rigid and soft exoskeletons: rigid-joint exoskeletons, simplified as rigid exoskeletons sometimes, refer to those that use motors, linear actuators, traditional pneumatic/hydraulic actuators, and other rigid actuators at human joints to generate human motions and DOFs and thus the main robot body is rigid as a whole; whereas soft exoskeletons (i.e. exosuits) refer to those that use flexible materials and/or structures for motion and DOF generation although such flexible structures can include small rigid parts and cables and the overall wearable robot body is soft and/or flexible like a garment.

For a fully autonomous exoskeleton with human interaction, no matter rigid or soft, the overall robot design needs to consider all the key components in a systematic way as shown in figure 2. To this end, exoskeletons can also be classified according to their control methods, mechanical designs (i.e. actuation and power transmission methods), as well as target pervasive health applications. Although human inputs have been explored for active soft exoskeletons, most of the existing studies of advanced controls are mainly focused on rigid-joint exoskeletons due to the emerging state of the soft exoskeletons. The typical mechanical designs are also mainly for rigid-joint exoskeletons. Thus, we will start with traditional rigid-joint exoskeletons and illustrate the key components for exoskeleton designs in section 4 followed by soft exoskeletons in section 5. The knowledge from traditional rigid designs may provide practical experience and inspire new ideas for soft exoskeleton designs.

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Given target application scenarios, the physical design of upper-limb exoskeletons needs to consider DOF, sensing and control methods, actuators, power transmission, and the upper-limb segment to be controlled according to the needs and specifications. For example, exoskeletons for back support in work conditions emphasize reducing the applied loads and/or muscle activities [55] whereas rehabilitation robots are designed to achieve targeted rehabilitation tasks such as planned motion trajectory and joint movement. To this end, the hardware design and the control methods vary. Also when targeting assisting different joint motions, the physical design can be different as well. In this section, we technically review the key components for rigid-joint upper limb exoskeletons. Notably, Muhammad et al [14], Maciejasz et al [28], Gopura et al [13], and Xu et al [25] comprehensively listed typical exoskeleton designs including both hardware and control; in this section, we will not list designs and will focus on the necessary techniques for designing key components of exoskeletons. Although such key techniques are mainly used for rigid-joint exoskeletons, the knowledge may inspire new soft designs as well. Figure 3 shows typical rigid-joint exoskeletons in recent five years.

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The rehabilitation robots should be capable of providing the patients with repetitive and task-oriented treatment. To this end, the controller design should be adaptable considering to patient's personalization and rehabilitation stages. Considering patients' conditions, the control strategies for such robots can be categorized into (1) patient-passive and (2) patient-cooperative control strategies [6] depending on the patient's muscle control conditions. Patient-passive controlled exoskeletons can assist patients to achieve predefined trajectories with zero required efforts and thus are suitable for those without any muscle control. A number of control strategies ranging from proportional–integral–derivative (PID) controller [56] to more advanced methods have been considered for this type [17, 57]. Patient-cooperative control strategies consider patients' motion intention and should enhance human-robot interaction towards better treatment thus motion intention estimation is significant in rehabilitation exoskeleton design. Feedback control with motion intention estimation thus has been broadly studied [58]. Considering technical designs, upper limb exoskeletons need to consider human input signals, controller design, actuation, and power transmission. According to the patient's condition and the associated rehabilitation tasks, the overall design can be varied. Different human sensing and inputs have been studied to estimate human motion intention to enhance human-exoskeleton interaction.

4.1. Human inputs

4.1.1. EMG

EMG is the record of the electrical activities of muscles and motor neurons. Electrical activation potentials of skeletal muscles can be measured through EMG signals, showing spikes in signals when the muscle cells are electrically stimulated and no signals when these muscles are at rest. EMG-based continuous motion prediction methods can be generally categorized into two types, (1) model-based approaches that use muscle, kinematic or dynamic models to estimate certain motion parameters and (2) model-free approaches that are mainly data-driven [59]. sEMG is the detection of signals from the skin surface and is predominately utilized for non-medical applications in contrast to intramuscular EMG. sEMG has been widely used as the input for exoskeleton control or human-robot interaction. EMG electrode placement also needs to be well considered to optimize signal acquisition for targeted motions [60].

When using sEMG as real-time control inputs, there are generally three steps: (1) data segment for real-time analysis; (2) signal pre-processing for generating EMG features including both time-domain and frequency-domain features; and (3) linear regression and/or classification with results inputting to or combined with the controller. Figure 4 shows the overall architecture of using EMG-based model-free methods for upper limb exoskeleton control. Most real-time control systems implement linear classifiers. Some classification methods like neural network, motion direction estimation, common spatial pattern, minimum distance classifier, support vector machines, blind source separation, linear discriminant analysis (LDA), random forest, and neuro-fuzzy, dynamic recurrent neural network are common classifiers applied in EMGs and EEGs.

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EMG intensity is directly related to the muscle force [61] and thus has been used to estimate joint angles [62–65], torques [66–71], forces [72] in exoskeleton control and actuation as well as for impedance control [73]. Classification-based control methods can be single or multifunctional and there is a performance trade-off. For classification, the training datasets need to be carefully collected and also sufficient to predict desired motion classes or parameters. A number of studies directly feed EMG features into classification or regression algorithms to train the desired output. Liu et al [74] developed a myoelectric control method for rehabilitation robots using linear/nonlinear regression trained with EMG and position. Lei [75] extracted time, frequency, and time–frequency domain features of EMG to train a backpropagation neural network. In [73], EMG was used as the control input for a hybrid impedance-admittance control method for an exoskeleton. In [76], an upper extremity motion pattern recognition using a modified multi-channel EMG feature was proposed, in which time and frequency domain features were used. A modified rank of the short-time Fourier transform feature was implemented through modification of the conventional features. Loconsole et al [77] proposed a torque control of the shoulder and elbow joints through online prediction using sEMG based method with feedforward time delay neural networks. Forces exerted in the sagittal plane when the length of the muscle remained relatively constant under tension were recorded using the sEMG signals. In [78], an algorithm based on LDA and backpropagation was developed for training multilayer perceptron neural networks for pattern recognition and feature extraction of EMG signals. In some studies, physical models are also considered in the data-driven method. Kiguchi and Quan [79] developed an effective EMG-based controller for a four-DOF upper limb exoskeleton using a muscle-model adjusted weight matrix fed into a neuro-fuzzy modifier.

For EMG-based rehabilitation exoskeletons, a great number of remarkable designs have been proposed and developed in the past two decades. Some of them have also been used and studied in clinical trials as discussed in section 5. In these therapies, exoskeletons need to support patients' rehabilitation tasks and/or patient-exoskeleton cooperation and be programmable and adaptive to patients' personalization and rehabilitation stages and EMG can help facilitate the interaction. In addition, EMG signals are also useful for fatigue monitoring but can be limited to some individuals or become extremely weak after suffering a stroke. In addition to rehabilitation therapies, some exoskeletons have shown positive effects on affecting human mental state by improving quality of life and ADL. Some exoskeletons assist patients by providing mobility to the target limb, which is more lightweight and portable. In [80], a low-cost real-time embedded EMG-driven RobHand was developed, which provides an active-assistive technology to improve the range of arm motion, strength, motor function of the upper limbs and provide improved ADL. More examples of ADL are discussed in section 5.3.

4.1.2. EEG

EEG measures the electrical activities along the scalp, which is believed to represent brain activities and is often used as a primary tool for brain-computer interfaces (BCIs). A robot is commonly interfaced with EEG signals for human intention estimation. BCIs are often used to control the robot to achieve the desired motion by selecting prior constructed motion patterns such as reaching targets [81, 82], and upper limb motions [83–86]. Low-frequency portion in EEG signals contains the important feature of the motion. For controlling exoskeletons, EEG signals are normally processed for upper limb motion estimation using classification or pattern recognition algorithms. Al-Quraishi et al [87] reviewed EEG-based control for both upper and lower limb exoskeletons and prosthesis. EEG-based exoskeletons are actually not as extensively studied as EMG-based designs. In [88], an exoskeleton named MAHI Exo-II was developed using EEG to estimate the intentions of a stroke survivor while identifying movement-related cortical potentials. Xiao et al [89] adopted an Emotiv EEG headset to control a four-DOF exoskeleton for sequential upper limb imagined motions. In rehabilitation robots and therapies, EEG is sometimes used as a diagnostic and/or tool in addition to control inputs for exoskeletons. In these cases, the rehabilitation robots may be passive [90].

Although EEG has been used for robotic arm control, EEG-controlled exoskeletons overall are still in an emerging stage due to the complicated neural states and the corresponding signal processing, pattern recognition, and classification. Although muscle atrophy and absence of EMG signals are notable possibilities with patients, EMG signals are specifically applicable in areas with a high signal-to-noise ratio (SNR), fatigue monitoring, and less to no training required. Consequently, EEG signals are preferred for patients with the absence of EMG signals. In rehabilitation therapies, EEG-based motor imagery may provide opportunities for enhancing neuroplasticity and synchronized monitoring and analytics. In addition, EEG, as one of the primary BCIs, is still believed to be promising in predicting motion intention for robotic devices.

4.1.3. Hybrid biological signals

The hybrid inputs can be achieved through a sequential or simultaneous fusion of two human biological signals, mostly EMG-EEG, to provide a more comprehensive human interface for robot control. Lalitharatn et al [91] conducted a literature review on the hybrid fusion of EMG-EEG-based control approaches that can be used in bio-robotics, in which design methods were discussed, buttressing the main features with advantages of this approach. Although EMG-EEG has been studied as a promising human interface for manipulators and end effector type robotic devices [92], it has not been widely attempted yet for wearable upper limb exoskeletons. Kawase et al [93] developed a real-time controlled exoskeleton for paresis using hybrid EMG-EEG with EMG being used to estimate joint angles. In [94], a regularized cost function of artificial neuron network is presented as an estimation method for instinctual control of an upper limb exoskeleton with EMG and EEG signals tested in an arm simulator, the model predicted grab motion from EMG signals and arm movement motions from EEG signals.

EMG-EEG hybrid inputs provide unique features for exoskeleton control including those from both muscle and brain activities. Such inputs can not only provide motor estimation and intention prediction but also be used as an evaluation tool for rehabilitation therapies. However, synchronized EMG and EEG detection requires relatively more complicated sensing system design and control strategies.

4.1.4. Other bio-signals

In addition to EMG and EEG, some other biological signals also show the potential as human inputs for robotic device control. Among them, sonomyography (SMG) is the detection of change in muscle thickness or deformation using ultrasound imaging, depicted by temporal and spatial features. SMG has been studied to estimate and predict human motion intention and motion pattern recognition recently [95, 96]. Engdahl et al [97] classified user performance during clinical tests of upper limb transradial procedure, based on analogous SMG spatial features, while exploring the repeatability isolation of SMG control signal over a short period of time during pre-prosthetic training. An experimental comparison was also performed in [98] to evaluate the effect of SMG-based and sEMG-based human-machine interface (HMI) on finger motion classification for more precise control and manipulation. Mechanomyography (MMG) is a technique that involves recording and estimating the mechanical activities of active skeletal muscle fibers using specific acoustic systems. Due to its discriminative power, higher bandwidth, and SNR, it can be considered a control method for signal extraction. Castillo et al [99] designed an MMG armband, which enabled the control of normal force distribution applied in varied sections of an upper limb loss of a transracial patient.

The above-mentioned other biological signals have not been applied directly to the upper extremity exoskeleton but have been used in upper limb prosthesis control. However, the mode of signal extraction and control methods may be considered to be applied to exoskeleton design for upper limbs. In exoskeleton designs, EMG is still the most commonly used biological signal for providing human inputs in control methods.

4.1.5. Non-biological inputs

In many cases of rehabilitation exoskeletons, patients' joint angles and torques need to be measured for both human inputs for control and rehabilitation estimation. Thus, encoders for joint angle detection and/or torque sensors are also a typical hardware design with a number of notable studies using different control strategies. Crea et al [100] developed and clinically validated a powered exoskeleton with two control modes, position control and torque control for elbow spasticity treatment. Wu et al [101] developed a gravity-balanced exoskeleton for active rehabilitation training of the upper limb using both encoders and torque sensors.

Non-biological signal based control methods can also estimate the motion intention of users by analyzing the force and/or torque signals through a variety of sensors attached to the upper limb exoskeleton. Direct measurement of mechanical outcomes of muscles can be achieved by using force sensors, load sensors, strain gauges, or measuring intended limb joint torque and acceleration. Among them, the muscle circumference sensor (MCS) measures the circumference change caused by muscle, which can be used to estimate force and torque. Kim et al [102] developed an MCS to estimate the torque of the human elbow joint and then some studies started to leverage MCS in exoskeleton control. In [103], a model was developed for a reference-based adaptive control algorithm to drive the exoskeleton in the desired impedance fashion and the radial basis function neural network was employed to extract the desired motion intention (DMI). Khan et al [104] extracted the human DMI through a damped least square algorithm implementing a passive adaptive control algorithm as an estimation method. They also presented a MCS with load cells to estimate the DMI generated using passivity adaptive control [57].

Another notable non-biological sensor for feedback control is the force sensing resistor (FSR), which is a device able to measure changeable resistance with applied force and has been adopted for detecting forces exerted in human-exoskeleton interaction. In [105], a power-assist robot was proposed with an intentional reaching direction intention method using FSRs for real-time estimation of motion intention. Christensen and Bai [106] developed a shoulder exoskeleton using a double parallelogram linkage using FSR for interaction force measurement. Among all these non-biological signal based controls, sensors have been selected and utilized together with the control strategies.

4.2. Control

4.3. Actuation and power transmission

Exosuits are soft exoskeletons that are more portable, wearable, and biocompatible. Although soft actuators have been used in rigid-joint exoskeletons, they are normally used for connecting rigid links and designs. Exosuits are normally textile- or fabric-based, which has promised results for both enhancing performances and compensating for neuromuscular deficiencies. Most of the exosuits are composed of an actuation system and wearable components, and the absence of a rigid structure omits the issue of misalignment between the joints of users and the robot so the safety and kinematic transparency stand out among the many benefits of using exosuits [130]. As aforementioned, the exoskeleton's principal objective is passive rehabilitation. While performing these tasks, the exoskeleton user leaves his arm immobile, and the exoskeleton is in control of moving the arm articulations in accordance with a therapist-preprogrammed pattern [131]. The performance of these exosuits is evaluated by their weight and muscle need reduction. Figure 5 shows typical exosuits in recent years and table 3 summarizes the designs and applications.

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5.1. Cable-based actuation system

5.2. Pneumatic and hydraulic actuators

5.3. Shape memory materials

Neuromuscular disorders, cerebrovascular diseases, injuries, and other disabilities can cause motor function deficits with different onsets including hemiparesis, bradykinesia, paralysis, etc. Some progressive neuromuscular disorders such as Parkinson's disease, ALS, pathologically originate in the nervous system or neuromuscular junctions and thus the desired assistance and/or therapies vary. Due to the patients' conditions and the corresponding training modules, there are four major types according to [6]: (1) patient passive robot active, (2) patient-robot cooperative, (3) patient active robot passive, and (4) robot resistive. Powered exoskeletons are active robotics and thus the second type of patient-robot cooperation is the most considered case using rehabilitation robots. This section aims to discuss the expected treatment outcomes, the desired training tasks, and the corresponding exoskeleton designs considering specific treatment of diseases.

6.1. Stroke rehabilitation

6.2. SCI

6.3. Neuromuscular disorders and ADL

According to the Centers for Disease Control and Prevention stroke fact [200], there is a new stroke patient every 40 s. Other neuromuscular disorders affect 14 million people globally [200]. The high expenditure of in-hospital healthcare for chronic conditions calls for new technologies for pervasive health for both patients and their families and caregivers. Wearable robotics provide a powerful and promising solution for pervasive health including both rehabilitation and ADL assistance in different settings. Remarkable studies have been done in the past half-century since the first active exoskeleton was developed in the 1960s. Although emerging, soft exoskeletons have shown big advantages and high potential to provide assistance and healthcare in everyday life. Notable products have been available in the market, however, most of the current exosuits are passive and designed for back support and injury prevention in short- or long-term use. There are still challenges as well as opportunities for exoskeletons to enable pervasive health.

7.1. Mechanical design and adaptability

7.2. Difficulty in ADL assistance for patients

7.3. Affordability

In this paper, we systematically reviewed the recent progress on wearable robotics for pervasive health with a focus on the design and development of both exoskeletons and soft exosuits. Clinical studies of using exoskeletons for different rehabilitation therapies and ADL are also summarized and discussed. For rehabilitation purposes, upper limb exoskeletons can contribute to human motor recovery by enhancing motor relearning by leveraging neuroplasticity. RCTs using exoskeletons for post-stroke rehabilitation therapies have been conducted and promising results have been shown. Although the rehabilitation of other common neuromuscular disorders and SCIs may also utilize human neuroplasticity, not many RCTs for exoskeleton-based rehabilitation are attempted, and clinical evidence is still lacking. Considering different purposes and domains, we discuss the challenges and opportunities of designing exoskeletons for practical applications in pervasive health. This article also hopes to inspire new ideas for wearable robot designs with new human interfaces and compliant materials with the consideration of clinical needs.

This study is funded by the National Science Foundation (Grant Nos. 2222110 and 2135620). We sincerely appreciate the support to enable this study.

The data cannot be made publicly available upon publication because they are not available in a format that is sufficiently accessible or reusable by other researchers. The data that support the findings of this study are available upon reasonable request from the authors.