While initially conceived for human motion augmentation, wearable robots have gradually evolved as technological aids in motion assistance and rehabilitation. There are increasing real-world applications in industrial and medical scenarios. Though efforts have been made on wearable robotic systems, e.g. robotic prostheses and exoskeletons, there are still several challenges in kinematics and actuation solutions, dynamic analysis and control of human-robot systems, neuro-control and human-robot interfaces; ergonomics and human-in-the-loop optimization. Meanwhile, real-world applications in industrial or medical scenarios are facing difficulties considering effectiveness.

Wearable devices are fast evolving to address mobility and autonomy needs of elderly people who would benefit from physical assistance. Recent developments in soft robotics provide important opportunities to develop soft exoskeletons (also called exosuits) to enable both physical assistance and improved usability and acceptance for users. The XoSoft EU project has developed a modular soft lower limb exoskeleton to assist people with low mobility impairments. In this paper, we present the design of a soft modular lower limb exoskeleton to improve person’s mobility, contributing to independence and enhancing quality of life. The novelty of this work is the integration of quasi-passive elements in a soft exoskeleton. The exoskeleton provides mechanical assistance for subjects with low mobility impairments reducing energy requirements between 10% and 20%. Investigation of different control strategies based on gait segmentation and actuation elements is presented. A first hip–knee unilateral prototype is described, developed, and its performance assessed on a post-stroke patient for straight walking. The study presents an analysis of the human–exoskeleton energy patterns by way of the task-based biological power generation. The resultant assistance, in terms of power, was 10.9% ± 2.2% for hip actuation and 9.3% ± 3.5% for knee actuation. The control strategy improved the gait and postural patterns by increasing joint angles and foot clearance at specific phases of the walking cycle.

In this paper, we present a passive lower extremity exoskeleton with a simple structure and a light weight. The exoskeleton does not require any external energy source and can achieve energy transfer only by human body’s own gravity. The exoskeleton is self-adaptive to human gait to achieve basic matching therewith. During walking, pulling forces are generated through Bowden cables by pressing plantar power output devices by feet, and the forces are transmitted to the exoskeleton through a crank-slider mechanism to enable the exoskeleton to provide torques for the ankle and knee joints as required by the human body during the stance phase and the swing phase. Our self-developed gait detection system is used to perform experiments on kinematics, dynamics and metabolic cost during walking of the human body wearing the exoskeleton in different states. The experimental results show that the exoskeleton has the greatest influence on motion of the ankle joint and has the least influence on hip joint. With the increase in elastic coefficient of the spring, the torques generated at the joints by the exoskeleton increase. When walking with wearing k3EF exoskeleton at a speed of 0.5 m/s, it can save the most metabolic cost, reaching 13.63%.

In this work we present NEUROExos, a novel generation of upper-limb exoskeletons developed in recent years at The BioRobotics Institute of Scuola Superiore Sant’Anna (Italy). Specifically, we present our attempts to progressively (i) improve the ergonomics and safety (ii) reduce the encumbrance and weight, and (iii) develop more intuitive human–robot cognitive interfaces. Our latest prototype, described here for the first time, extends the field of application to assistance in activities of daily living, thanks to its compact and portable design. The experimental studies carried out on these devices are summarized, and a perspective on future developments is presented.

This paper studies the problem of optimizing the kinematic structure of an eight degree-of-freedom upper-limb rehabilitation exoskeleton. The objective of optimization is achieving minimum volume and maximum dexterity in the workspace of daily activities specified by a set of upper-arm configurations. To formulate the problem, a new index is proposed for effective characterization of kinematic dexterity for wearable robots. Additionally, a set of constraints are defined to ensure that the optimal design can cover the desired workspace of the exoskeleton, while singular configurations and physical interferences are avoided. The formulated multi-objective optimization problem is solved using an evolutionary algorithm (Non-dominated Sorting Genetic Algorithm II) and the weighted sum approach. Among the resulted optimal points, the point with least sensitivity with respect to the variations of design variables is chosen as the final design.

This paper presents a lower-limb exoskeleton that is actuated by pneumatic muscle actuators (PMAs). This exoskeleton system is composed of the mechanical structures, a treadmill, and a weight support system. With the cooperative work of the three parts, the system aims to assist either the elderly for muscle strengthening by conducting walking activities or the stroke patients during a rehabilitation training program. A mechanism is developed to separate the PMAs from the wearer’s legs to reduce the subject’s physical exertion. Furthermore, considering the difficulty in the modeling of proposed PMAs-driven exoskeleton, a safe and model-free control strategy called proxy-based sliding mode control (PSMC) is used to ensure proper control of the exoskeleton. However, the favorable performances are strongly dependent on the appropriate control parameters, which may be difficult to obtain with blind tuning. Therefore, we propose a global parameters optimization algorithm called switch-mode firefly algorithm (SMFA) to automatically calculate the pre-defined object function and attain the most applicable parameters. Experimental studies are conducted, and the results show the effectiveness of the proposed method.

Nonlinear articular geometries of biological joints have contributed to highly agile and adaptable human-body motions. However, human–machine interaction could potentially distort natural human motions if the artificial mechanisms overload the articular surfaces and constrain biological joint kinematics. It is desired to better understand the deformable articular geometries of biological joints in vivo during movements for design and control of wearable robotics. An articular geometry reconstruction method is proposed to measure the effective articular profile with a wearable compliant device and illustrated with its application to knee-joint kinematic analysis. Regarding the joint articulation as boundary constraints for the compliant mechanism, the equivalent articular geometry is constructed from the beam deformations driven by knee motions, where the continuous deformations are estimated with strain data from the embedded sensors. Both simulated analysis and experimental validation are presented to justify the proposed method.

This study investigates the effectiveness of a robot-mediated strategy aimed at promoting balance recovery after multidirectional slippages. Six older adults were asked to manage anteroposterior and mediolateral slippages while donning an active pelvis orthosis (APO). The APO was set up either to assist volunteers during balance loss or to be transparent. The margin of stability, in sagittal and frontal planes, was the main metric to assess the effectiveness of balance recovery. Results showed that the assistive strategy is effective at promoting balance recovery in the sagittal plane, for both perturbing paradigms; however, it is not effective at controlling stability in the frontal plane.

People with severe neuromuscular trunk impairment cannot maintain or control upright posture of the upper body in sitting while reaching. Passive orthoses are clinically available to provide support and promote the use of upper extremities in this population. However, these orthoses only position the torso passively without any degree of trunk movement.

We introduce for the first time a novel active-assistive torso brace called Wheelchair Robot for Active Postural Support (WRAPS). It consists of two rings over the hips and chest connected by a 2RPS-2UPS parallel robotic device. WRAPS can modulate the displacement of the upper ring and/or the forces applied on the torso through the ring in four degrees-of-freedom (DOF), including rotations and translation in the sagittal and frontal planes.

In the present study, we evaluate the design of WRAPS and its functions. Moreover, we discuss the potential effectiveness of WRAPS as a therapeutic robotic tool in people with severe trunk control deficits. The performance of WRAPS was evaluated in seated healthy subjects. Kinematics and surface electromyography (sEMG) were collected when the participants performed selective trunk movements. First, the torso range of motion (tROM) was calculated with WRAPS in transparent mode—zero-force control mode—which was compared with free-guided tROM (no WRAPS) with motion capture system. Second, a position control mode was configured to mobilize the torso along the trajectories obtained with the transparent mode.

Our results show that the design of WRAPS suited well the subject’s anthropometrics while supporting the weight of the torso. Importantly, WRAPS can be programmed to replicate the subject’s tROM, without the full activation of torso muscles. This can be critical in individuals with no trunk control. Altogether, these preliminary results indicate the potential applicability of WRAPS to promote active-assistive trunk mobility in people who cannot sit independently because of trunk dysfunction.

This paper deals with the control of an active ankle foot orthosis (AAFO) for paretic patients. State of the art methods using an AAFO try to track a predefined trajectory of the ankle joint while guaranteeing the wearer’s safety in the presence of a large tracking error. Combining the wearer’s safety and tracking accuracy is generally difficult to achieve at the same time, hence a trade-off should be found. Proxy-based sliding mode control (PSMC) offers great performances in both position tracking and safety guarantee. However, its tracking performance is subject to the influences of parameter uncertainties and external disturbances that generally occur during walking. This paper introduces an adaptation interaction method to the basic PSMC with an online adaptation of the proportional, integral and derivative parameters. At the same time, a gait phase-based ankle reference generation algorithm was proposed to adjust the joint reference trajectory in real time. The experiments using the AAFO show better tracking results with respect to basic PSMC while guaranteeing the safety.

Exoskeleton robots have been widely used in many fields at present. When wearing the exoskeleton to operate, the wearer may be unconscious of the position of exoskeleton or affected by the surrounding environment, causing collision between two arms of exoskeleton or between arms and environment. The collision may result in the exoskeleton destroyed or even the wearer injured. This paper proposes a hierarchical safety control strategy for exoskeleton robots based on maximum correntropy Kalman filter and bounding box to ensure safe operation. Accurate joint angle prediction can be obtained by filtering out non-Gaussian impulsive noise using maximum correntropy criterion as evaluation criterion. Relative position relationship of the arms can be derived based on bounding box to realize hierarchical safe control. Enough experiments have been carried out, and the results validated the feasibility of the proposed method.

The purpose of our study was to get deeper insights into sprinting with and without running-specific prostheses and to perform a comparison of the two by combining analysis of known motion capture data with mathematical modeling and optimal control problem (OCP) findings. We established rigid multi-body system models with 14 bodies and 16 degrees of freedom in the sagittal plane for one unilateral transtibial amputee and three non-amputee sprinters. The internal joints are powered by torque actuators except for the passive prosthetic ankle joint which is equipped with a linear spring–damper system. For each model, the dynamics of one sprinting trial was reconstructed by solving a multiphase least squares OCP with discontinuities and constraints. We compared the motions of the amputee athlete and the non-amputee reference group by computing characteristic criteria such as the contribution of joint torques, the absolute mechanical work, step frequency and length, among others. By comparing the amputee athlete with the non-amputee athletes, we found reduced activity in the joints of the prosthetic limb, but increased torques and absolute mechanical work in the arms. We also compared the recorded motions to synthesized motions using different optimality criteria and found that the recorded motions are still far from the optimal solutions for both amputee and non-amputee sprinting.

Using wearable robots is an effective means of rehabilitation for stroke survivors, and effective recognition of human motion intentions is a key premise in controlling wearable robots. In this paper, we propose an inertial measurement unit (IMU)-based gait phase detection system. The system consists of two IMUs that are tied on the thigh and on the shank, respectively, for collecting acceleration and angular velocity. Features were extracted using a sliding window of 150 ms in length, which was then fed into a quadratic discriminant analysis (QDA) classifier for classification. We recruited five stroke survivors to test our system. They walked at their own preferred speed on the level ground. Experimental results show that our proposed system has the ability of recognizing the gait phase of stroke survivors. All recognition accuracy results are above 96.5%, and detections are about 5–15 ms in advance of time. In addition, using only one IMU can also give reliable recognition results. This paper proposes an idea about the further research on human–computer interaction for the control of wearable robots.

This paper presents a novel three-degree of freedom (DOF) sensorized remote-center-of-motion (RCM) ankle module for a military-purpose lower-limb exoskeleton. A military-purpose exoskeleton should assist and follow the fast and dexterous motion of a soldier in rough terrain environments. Among the lower-limb joints, the ankle joint plays an important role in stabilizing walking motion during the stance phase. Thus, aligning the rotation center of the ankle module with the center of the wearer’s ankle is very important to reduce potential risks and fatigue of a soldier. To this end, the ankle exoskeleton was designed using two spherical chains. The two spherical chains were designed such that the intersection of all revolute pairs, which consist of the spherical chains, is located close to the rotation center of the wearer’s ankle. In addition, three encoders are attached to each of the three revolute pairs of a spherical chain to measure the three-DOF orientation of the ankle mechanism. For the design, the required range of motion is analyzed via gait analysis in three environment conditions. Forward and inverse kinematic relations are derived, and the workspace of the ankle is analyzed. For the prototype ankle mechanism, the range of motion and measurement performance are verified by a static experiment. In addition, comparative studies between the proposed RCM ankle and an ankle mechanism with offset rotation center are performed for three walking conditions: level walking, ascending and descending stairs, and a vertical slope. Via comparative study, it is confirmed that, compared to the ankle mechanism with offset center of rotation, the proposed RCM ankle is advantageous for sensing wearer’s ankle motion and reducing mechanical interference to wearer’s natural ankle motion.