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The accurate teleoperation of robotic devices requires simple, yet intuitive and reliable control interfaces. However, current human–machine interfaces (HMIs) often fail to fulfill these characteristics, leading to systems requiring an intensive practice to reach a sufficient operation expertise. Here, we present a systematic methodology to identify the spontaneous gesture-based interaction strategies of naive individuals with a distant device, and to exploit this information to develop a data-driven body–machine interface (BoMI) to efficiently control this device. We applied this approach to the specific case of drone steering and derived a simple control method relying on upper-body motion. The identified BoMI allowed participants with no prior experience to rapidly master the control of both simulated and real drones, outperforming joystick users, and comparing with the control ability reached by participants using the bird-like flight simulator Birdly.

peer-reviewed The aim of this study was to evaluate the effect of an industrial exoskeleton on muscle activity, perceived musculoskeletal effort, measured and perceived contact pressure at the trunk, thighs and shoulders, and subjective usability for simple sagittal plane lifting and lowering conditions. Twelve male participants lifted and lowered a box of 7.5 kg and 15 kg, respectively, from mid-shin height to waist height, five times, both with and without the exoskeleton. The device significantly reduced muscle activity of the Erector Spinae (12%-15%) and Biceps Femoris (5%). Ratings of perceived musculoskeletal effort in the trunk region were significantly less with the device (9.5%-11.4%). The measured contact pressure was highest on the trunk (91.7 kPa-93.8 kPa) and least on shoulders (47.6 kPa-51.7 kPa), whereas pressure was perceived highest on the thighs (35-44% of Max LPP). Six of the users rated the device usability as acceptable. The exoskeleton reduced musculoskeletal loading on the lower back and assisted with hip extensor torque during lifting and lowering. Contact pressures fell below the Pain Pressure Threshold. Perceived pressure was not exceptionally high, but sufficiently high to cause discomfort if used for long durations. peer-reviewed

Background Powered exoskeletons are a promising approach to restore the ability to walk after spinal cord injury (SCI). However, current exoskeletons remain limited in their walking speed and ability to support tasks of daily living, such as stair climbing or overcoming ramps. Moreover, training progress for such advanced mobility tasks is rarely reported in literature. The work presented here aims to demonstrate the basic functionality of the VariLeg exoskeleton and its ability to enable people with motor complete SCI to perform mobility tasks of daily life. Methods VariLeg is a novel powered lower limb exoskeleton that enables adjustments to the compliance in the leg, with the objective of improving the robustness of walking on uneven terrain. This is achieved by an actuation system with variable mechanical stiffness in the knee joint, which was validated through test bench experiments. The feasibility and usability of the exoskeleton was tested with two paraplegic users with motor complete thoracic lesions at Th4 and Th12. The users trained three times a week, in 60 min sessions over four months with the aim of participating in the CYBATHLON 2016 competition, which served as a field test for the usability of the exoskeleton. The progress on basic walking skills and on advanced mobility tasks such as incline walking and stair climbing is reported. Within this first study, the exoskeleton was used with a constant knee stiffness. Results Test bench evaluation of the variable stiffness actuation system demonstrate that the stiffness could be rendered with an error lower than 30 Nm/rad. During training with the exoskeleton, both users acquired proficient skills in basic balancing, walking and slalom walking. In advanced mobility tasks, such as climbing ramps and stairs, only basic (needing support) to intermediate (able to perform task independently in 25% of the attempts) skill levels were achieved. After 4 months of training, one user competed at the CYBATHLON 2016 and was able to perform 3 (stand-sit-stand, slalom and tilted path) out of 6 obstacles of the track. No adverse events occurred during the training or the competition. Conclusion Demonstration of the applicability to restore ambulation for people with motor complete SCI was achieved. The CYBATHLON highlighted the importance of training and gaining experience in piloting an exoskeleton, which were just as important as the technical realization of the robot., Journal of NeuroEngineering and Rehabilitation, 15 (1), ISSN:1743-0003

The aim of this study was to evaluate the effect of an industrial exoskeleton on muscle activity, perceived musculoskeletal effort, measured and perceived contact pressure at the trunk, thighs and shoulders, and subjective usability for simple sagittal plane lifting and lowering conditions. Twelve male participants lifted and lowered a box of 7.5 kg and 15 kg, respectively, from mid-shin height to waist height, five times, both with and without the exoskeleton. The device significantly reduced muscle activity of the Erector Spinae (12%-15%) and Biceps Femoris (5%). Ratings of perceived musculoskeletal effort in the trunk region were significantly less with the device (9.5%-11.4%). The measured contact pressure was highest on the trunk (91.7 kPa-93.8 kPa) and least on shoulders (47.6 kPa-51.7 kPa), whereas pressure was perceived highest on the thighs (35-44% of Max LPP). Six of the users rated the device usability as acceptable. The exoskeleton reduced musculoskeletal loading on the lower back and assisted with hip extensor torque during lifting and lowering. Contact pressures fell below the Pain Pressure Threshold. Perceived pressure was not exceptionally high, but sufficiently high to cause discomfort if used for long durations.

Wearable robotic systems are being developed to prevent injury to the low back. Designing a wearable robotic system is challenging because it is difficult to predict how the exoskeleton will affect the movement of the wearer. To aid the design of exoskeletons, we formulate and numerically solve an optimal control problem (OCP) to predict the movements and forces of a person as they lift a 15 kg box from the ground both without (human-only OCP) and with (with-exo OCP) the aid of an exoskeleton. We model the human body as a sagittal-plane multibody system that is actuated by agonist and antagonist pairs of muscle torque generators (MTGs) at each joint. Using the literature as a guide, we have derived a set of MTGs that capture the active torque–angle, passive torque–angle, and torque–velocity characteristics of the flexor and extensor groups surrounding the hip, knee, ankle, lumbar spine, shoulder, elbow, and wrist. Uniquely, these MTGs are continuous to the second derivative and so are compatible with gradient-based optimization. The exoskeleton is modeled as a rigid-body mechanism that is actuated by a motor at the hip and the lumbar spine and is coupled to the wearer through kinematic constraints. We evaluate our results by comparing our predictions with experimental recordings of a human subject. Our results indicate that the predicted peak lumbar-flexion angles and extension torques of the human-only OCP are within the range reported in the literature. The results of the with-exo OCP indicate that the exoskeleton motors should provide relatively little support during the descent to the box but apply a substantial amount of support during the ascent phase. The support provided by the lumbar motor is similar in shape to the net moment generated at the L5/S1 joint by the body; however, the support of the hip motor is more complex because it is coupled to the passive forces that are being generated by the hip extensors of the human subject. The simulations developed in this study are specific to lifting motion and a lower back exoskeleton. However, the framework is applicable for simulating a large range of robotic-assisted human motions.

Background In human-centered robotics, exoskeletons are becoming relevant for addressing needs in the healthcare and industrial domains. Owing to their close interaction with the user, the safety and ergonomics of these systems are critical design features that require systematic evaluation methodologies. Proper transfer of mechanical power requires optimal tuning of the kinematic coupling between the robotic and anatomical joint rotation axes. We present the methods and results of an experimental evaluation of the physical interaction with an active pelvis orthosis (APO). This device was designed to effectively assist in hip flexion-extension during locomotion with a minimum impact on the physiological human kinematics, owing to a set of passive degrees of freedom for self-alignment of the human and robotic hip flexion-extension axes. Methods Five healthy volunteers walked on a treadmill at different speeds without and with the APO under different levels of assistance. The user-APO physical interaction was evaluated in terms of: (i) the deviation of human lower-limb joint kinematics when wearing the APO with respect to the physiological behavior (i.e., without the APO); (ii) relative displacements between the APO orthotic shells and the corresponding body segments; and (iii) the discrepancy between the kinematics of the APO and the wearer’s hip joints. Results The results show: (i) negligible interference of the APO in human kinematics under all the experimented conditions; (ii) small (i.e., < 1 cm) relative displacements between the APO cuffs and the corresponding body segments (called stability); and (iii) significant increment in the human-robot kinematics discrepancy at the hip flexion-extension joint associated with speed and assistance level increase. Conclusions APO mechanics and actuation have negligible interference in human locomotion. Human kinematics was not affected by the APO under all tested conditions. In addition, under all tested conditions, there was no relevant relative displacement between the orthotic cuffs and the corresponding anatomical segments. Hence, the physical human-robot coupling is reliable. These facts prove that the adopted mechanical design of passive degrees of freedom allows an effective human-robot kinematic coupling. We believe that this analysis may be useful for the definition of evaluation metrics for the ergonomics assessment of wearable robots.

Robotics is increasingly involving many aspects of daily life and robotic-based assistance to physically impaired people is considered one of the most promising application of this largely investigated technology. However, the World Health Organization reports that, so far, only 10% of people in need can get access to the so-called assistive technology also due to its high costs. This work aims to tackle the aforementioned point presenting an innovative control strategy for a low-cost hand exoskeleton system based on surface electromyography signals. Most of the activities of daily living are, in fact, carried out thanks to the hands while the exploitation of surface electromyography signals represents a non-invasive technique in straightforwardly controlling wearable devices. Although such approach results deeply studied in literature, it has not been deeply tested on real patients yet. The main contribution of this article is hence not only to describe a novel control strategy but also to provide a detailed explanation of its implementation into a real device, ready to be used. Finally, the authors have evaluated and preliminary tested the proposed technique enrolling a patient in a single-case study.

Gyroscopic actuation is appealing for wearable applications due to its ability to impart free moments on a body without exoskeletal structures on the joints.We recently proposed an unobtrusive balancing aid consisting of multiple parallelmounted control moment gyroscopes (CMGs) contained within a backpack-like orthopedic corset. Using conventional CMG control techniques, geometric singularities result in a number of performance issues, including either unintended oscillations or freezing of the gimbals at certain alignments, which are typically mitigated by the addition of redundant actuators or by allowing errors in the generated moment; however, because of the minimalistic design of the proposed device and focus on accurate moment tracking, a new methodology is required. In this paper, a control scheme is proposed for non-redundant CMG systems in which oscillations at saturated states are avoided and all remaining singularities are efficiently escaped by exploiting the system geometry; due to its use of classification-specific singularity proximity measures that account for the command moment orientation, it is named the directional singularity-robust (DSR) control law. The performance of this control law is assessed in both simulations and hardware testing. The proposed method is suitable for a wide range of CMG systems, including both balancing and aerospace applications.

International audience; Upon the appearance of nylons, silicon, tendons, smart and soft materials, wearable robotics is going closer to the human body, leading robotics and biomechanics to provide us new physical rehabilitations and improvements. In the area that concerns soft robotic prosthesis, the main challenge is the design of well sized mechatronic limbs and smart controllers that should help people to achieve desired movements. As a consequence, we present a hybrid model that allows different ways of representing hand poses, according to special interactions that arise from soft robotics chains. Our hybrid model uses the positions of finger's parts computed with the Denavit-Hartenberg (DH) method mixed with the quaternions representation to avoid singularities and to reduce the number of DH parameters. Kinematic and dynamic of finger motions are evaluated using an experimental setup with mechanical parts produced by 3D printing and different actuators. Finally, experimental results are compared with the theoretical values and demonstrate the accuracy of our model.

An increasing number of wearable devices performing eye gaze tracking have been released in recent years. Such devices can lead to unprecedented opportunities in many applications. However, staying updated regarding the continuous advances and gathering the technical features that allow to choose the best device for a specific application is not trivial. The last eye gaze tracker overview was written more than 10 years ago, while more recent devices are substantially improved both in hardware and software. Thus, an overview of current eye gaze trackers is needed. This review fills the gap by providing an overview of the current level of advancement for both techniques and devices, leading finally to the analysis of 20 essential features in six head-mounted eye gaze trackers commercially available. The analyzed characteristics represent a useful selection providing an overview of the technology currently implemented. The results show that many technical advances were made in this field since the last survey. Current wearable devices allow to capture and exploit visual information unobtrusively and in real time, leading to new applications in wearable technologies that can also be used to improve rehabilitation and enable a more active living for impaired persons.

Prosthetic hand control based on the acquisition and processing of surface electromyography signals (sEMG) is a well-established method that makes use of the electric potentials evoked by the physiological contraction processes of one or more muscles. Furthermore intelligent mobile medical devices are on the brink of introducing safe and highly sophisticated systems to help a broad patient community to regain a considerable amount of life quality. The major challenges which are inherent in such integrated system's design are mainly to be found in obtaining a compact system with a long mobile autonomy, capable of delivering the required signal requirements for EMG based prosthetic control with up to 32 simultaneous acquisition channels and — with an eye on a possible future exploitation as a medical device — a proper perspective on a low priced system. Therefore, according to these requirements we present a wireless, mobile platform for acquisition and communication of sEMG signals embedded into a complete mobile control system structure. This environment further includes a portable device such as a laptop providing the necessary computational power for the control and a commercially available robotic hand-prosthesis. Means of communication among those devices are based on the Bluetooth standard. We show, that the developed low cost mobile device can be used for proper prosthesis control and that the device can rely on a continuous operation for the usual daily life usage of a patient.

Lower-limb exoskeletons and powered orthoses in gait assistance applications for patients with locomotive disorders possess the potential to significantly affect society in the near future. This paper presents the primary features of a lower-limb exoskeleton under development as an active orthosis to enable paralysed children to walk. Because these patients are unable to move their limbs, the device generates their basic motions in everyday life, e.g. standing up, sitting down and stable ambulation. Novel features of this prototype include synergic biarticular actuation in the ankle, a compliance controller based on the force measured using insoles and the definition of parameterised hip and foot trajectories that facilitate the adaptation of gait characteristics. The improved gait performance that results from the proposed approach was experimentally validated. © 2014 Taylor & Francis and The Robotics Society of Japan., This work has been partially funded by the Spanish National Plan for Research, Development and Innovation through grant DPI2010-18702 which has funded personnel costs and by AECID through grant PCI-Iberoamerica A1/039883/11 which has funded equipment and consumable costs.

This paper describes the design and development of an exoskeleton that can deliver assistance-as-needed to patients or elderly with muscle weakness. Since the proof-of-concept is a first step towards the development of a final commercial prototype, the design had to be adaptable for patients with different heights, be comfortable for the patients, safe in use, energy-efficient and affordable in production. For this reason a modular system was built, using the same compliant actuator system in all joints. This paper describes the global design decisions made and the construction of the actual prototype.

We describe a method for the online classification of sleep/wake states based on cardiorespiratory signals produced by wearable sensors. The method was conceived in view of its applicability to a wearable sleepiness monitoring device. The method uses a Fast Fourier Transform as the main feature extraction tool and a feed-forward Artificial Neural Network as a classifier. We show that when the method is applied to data collected from a single young male adult, the system can correctly classify on average 95.4% of unseen data from the same user. When the method is applied to classify data from multiple users with the same age and gender, its accuracy is reduced to 85.3%. However, a Receiver Operating Characteristic analysis shows that, compared to actigraphy, the proposed method produces a more balanced correct classification of sleep and wake periods. Additionally, by adjusting the classification threshold of the neural classifier, 86.7% of correct classification is obtained.

We present a sensor technology for the measure of the physical human-robot interaction pressure developed in the last years at Scuola Superiore Sant’Anna. The system is composed of flexible matrices of opto-electronic sensors covered by a soft silicone cover. This sensory system is completely modular and scalable, allowing one to cover areas of any sizes and shapes, and to measure different pressure ranges. In this work we present the main application areas for this technology. A first generation of the system was used to monitor human-robot interaction in upper- (NEUROExos; Scuola Superiore Sant’Anna) and lower-limb (LOPES; University of Twente) exoskeletons for rehabilitation. A second generation, with increased resolution and wireless connection, was used to develop a pressure-sensitive foot insole and an improved human-robot interaction measurement systems. The experimental characterization of the latter system along with its validation on three healthy subjects is presented here for the first time. A perspective on future uses and development of the technology is finally drafted.

Increased fatigue of pilots during long flights can place both humans and machine at high risk. In this paper, we describe our research on a SymBodic (SYMbiotic BODies) system designed to minimize pilot fatigue in a simulated 48 hour mission. The system detected the pilot’s sleep breaks and used this information to plan future sleep breaks. When fatigue could not be prevented, the SymBodic system assisted the pilot by providing relevant flight information through a vibro-tactile vest. Experi- ments showed that it was difficult for the pilot to adapt to the suggested sleep schedule within the duration of the experiment, and fatigue was not avoided. However, during periods of severe sleep deprivation, the SymBodic system significantly improved piloting performance.

In this paper we describe a method to classify online sleep/wake states of humans based on cardiorespiratory signals for wearable applications. The method is designed to be embedded in a portable microcontroller device and to cope with the resulting tight power restrictions. The method uses a Fast Fourier Transform as the main feature extraction method and an adaptive feed-forward Artificial Neural Network as a classifier. Results show that when the network is trained on a single user, it can correctly classify on average 95.4% of unseen data from the same user. The accuracy of the method in multi-user conditions is lower (89.4%). This is still comparable to actigraphy methods, but our method classifies wake periods considerably better.

A novel solution to compensate hand grasping abilities is proposed for chronic stroke patients. The goal is to provide the patients with a wearable robotic extra-finger that can be worn on the paretic forearm by means of an elastic band. The proposed prototype, the Robotic Sixth Finger, is a modular articulated device that can adapt its structure to the grasped object shape. The extra-finger and the paretic hand act like the two parts of a gripper cooperatively holding an object. We evaluated the feasibility of the approach with four chronic stroke patients performing a qualitative test, the Frenchay Arm Test. In this proof of concept study, the use of the Robotic Sixth Finger has increased the total score of the patients by two points in a five points scale. The subjects were able to perform the two grasping tasks included in the test that were not possible without the robotic extra-finger. Adding a robotic opposing finger is a very promising approach that can significantly improve the functional compensation of the chronic stroke patient during everyday life activities.

Vibrotactile displays can extend the perception capabilities of visually impaired persons. Placing such devices on the head promises easy attachment and detachment without reducing other interaction abilities. However, the effectiveness of head-attached vibrotactile displays has never been thoroughly tested. This paper presents the results obtained from experiments with 22 subjects equipped with a display containing 12 cointype motors equally-spaced in a horizontal plane around the upper head region. Our display allowed single- as well as multimotor activation with up to six simultaneously active motors. We identified the minimum and comfort strength of vibrotactile stimulation, measured the precision in perceiving the accurate number of active motors as well as the precision in localizing the stimuli on the head. While subjects identified the correct number of active motors in 94% of the cases when presented with only one active motor, this precision dropped to 40% for two, down to 5% for five simultaneously active motors. This strongly suggests to avoid multi-point stimulation even though the precision of localizing a position of a stimulus on the head is barely affected by the number of simultaneously active motors. Localization precision, however, varied significantly with the region of the head suggesting that the most front and most back regions of the head should be avoided if high precision is required.

For soft robots to leave the lab and enter unstructured environments, proprioception is required to understand how interactions in the field affect the soft structure. In this work, we present sensor-embedded soft pneumatic actuators (sSPA) that can observe both extension and bending. The sensors are strain sensitive capacitors, which are bonded to the interior of fiber-reinforced extension actuators on opposing faces. This construction allows extension and bending to be measured by calculating the mean and difference in sensor responses, respectively. The sSPAs are bonded together to form a flat fascicle to increase the force output and prevent buckling under load, and are robust to component failure by incorporating redundancy. In this paper, we discuss the fabrication of the sensors and their subsequent integration into the actuators. We also report the work capacity and sensor. response of the sSPA fascicles under extension, bending, and the combination of both modes of deformation. The sensor- embedded soft pneumatic actuators presented here will advance the field of soft robotics by enabling closed-loop control of soft robots.

Sleep/wake classification systems that rely on phys- iological signals suffer from inter-subject differences that make accurate classification with a single, subject-independent model difficult. To overcome the limitations of inter-subject variability we suggest a novel on-line adaptation technique that updates the sleep/wake classifier in real-time. The objective of the present study was to evaluate the performance of a newly developed adaptive classification algorithm that was embedded on a wearable sleep/wake classification system called SleePic. The algorithm processed electrocardiogram and respiratory effort signals for the classification task and applied behavioral mea- surements (obtained from accelerometer and press-button data) for the automatic adaptation task. When trained as a subject- independent classifier algorithm, the SleePic device was only able to correctly classify 74.94% ± 6.76 of the human rated sleep/wake data. By using the suggested automatic adaptation method the mean classification accuracy could be significantly improved to 92.98% ± 3.19. A subject-independent classifier based on activity data only showed a comparable accuracy of 90.44% ± 3.57. We demonstrated that subject-independent models used for on- line sleep and wake classification can successfully be adapted to previously unseen subjects without the intervention of human experts or off-line calibration.

Clothing with integrated high-force actuators enables wearable haptics for immersive virtual reality (VR) and enables soft exoskeletons for rehabilitation or human augmentation. Electrostatic clutches (ESClutches) offer a very-low-energy solution to block motion and are mm thin. Challenges for ESClutches in wearables are 1) effective integration in clothing to accurately block body motion while ensuring comfort, 2) well-controlled sliding for variable stiffness rendering, and 3) adaptation to shoulder or hip joints. Here, the control of sliding friction of soft ESClutches is demonstrated, using materials that enable both integration in textile and efficient force transfer to the user. We present a 1.3 mm-thick soft glove with five ESClutches, providing up to 50 N of kinesthetic feedback per finger. A clutch in a thin haptic sleeve that controls elbow extension is reported. Eight cable-format ESClutches on a shoulder are shown, selectively blocking multiple degrees of freedom. VR tests demonstrate that the glove and the sleeve give the user the ability to rank the softness and weight of virtual objects. In a teleoperation scenario, the glove enables the user to remotely feel an object's stiffness. The ESClutches and textile integration pave a path toward socially acceptable and comfortable kinesthetic haptics.

Actigraphy for long-term sleep/wake monitoring fails to correctly classify situations where the subject displays low activity, but is awake. In this paper we propose a new algorithm which uses both accelerometer and cardio-respiratory signals to overcome this restriction. Acceleration, electrocardiogram and respiratory effort were measured with an integrated wearable recording system worn on the chest by three healthy male subjects during normal daily activities. For signal processing a Fast Fourier Transformation and as classifier a feed-forward Artificial Neural Network was used. The best classifier achieved an accuracy of 96.14%, a sensitivity of 94.65% and a specificity of 98.19%. The algorithm is suitable for integration into a wearable device for long-term home monitoring.

Telerobotics is the process by which human operators control the movement of robots to achieve specific tasks. However, conventional control interfaces, such as joysticks and remote controllers, are not intuitive to use for novice users. Training to use these interfaces requires a considerable amount of training. Wearable control interfaces are more inclusive because they allow humans to control the movement of robots with their own body movements through intuitive gestures without the need for excessive training. Haptic devices integrated in these interfaces can help users learn to teleoperate robots by applying forces to the parts of the body where corrective action is needed. Typically, these devices require the use of bulky actuators that can hinder natural user mobility and induce fatigue over prolonged usage. This thesis presents a framework in which users can successfully learn to teleoperate robots using fabric-based wearable haptic interfaces composed of electroadhesive clutches and free of actuators.

In biomedical signal analysis, artificial neural networks are often used for pattern classification because of their capability for nonlinear class separation and the possibility to efficiently implement them on a microcontroller. Typically, the network topology is designed by hand, and a gradient-based search algorithm is used to find a set of suitable parameters for the given classification task. In many cases, however, the choice of the network architecture is a critical and difficult task. For example, hand-designed networks often require more computational resources than necessary because they rely on input features that provide no information or are redundant. In the case of mobile applications, where computational resources and energy are limited, this is especially detrimental. Neuroevolutionary methods which allow for the automatic synthesis of network topology and parameters offer a solution to these problems. In this paper, we use analog genetic encoding (AGE) for the evolutionary synthesis of a neural classifier for a mobile sleep/wake discrimination system. The comparison with a hand-designed classifier trained with back propagation shows that the evolved neural classifiers display similar performance to the hand-designed networks, but using a greatly reduced set of inputs, thus reducing computation time and improving the energy efficiency of the mobile system.

Variable stiffness technologies help to solve the conflicting requirements of strength, comfort, and safety in soft exoskeletons. Here, we propose a modular variable stiffness strip based on layer jamming as a mechanical solution to constrain users' degrees of freedom and/or to implement wearable anchor points. Equipped with integrated capacitive strain sensors in the longitudinal direction, the strip is 30 mm wide and 3 mm thick and has a modular design that enables its adaptability to different morphologies and force requirements. If negative pressure in the order of tens of KPa is applied, the strip undergoes a stiffness change by a factor of 600, thus ranging between stretchability typical of elastane and inextensibility up to a maximum resisting force of 60 N. The versatility of the strip is shown by integration in an upper body exosuit.

Lower-limb exoskeletons are wearable devices whose main purposes are human rehabilitation and bilateral locomotion assistance. In particular, there is a growing interest for their use to symmetrize the gait of hemiparetic patients. This often consists in using the kinematics of the less affected side as a reference for the most affected one. In this work, we followed this approach to design a symmetrization algorithm using the formalism of motor primitives, i.e. a low-dimensional set of signals that provide the desired assistance through their combination. The amount of variables to be stored in memory is thus intrinsically limited, and this framework is particularly adapted to include other modes of assistance and/or transitions between locomotion tasks. In this paper, we report the preliminary validation of this newly developed algorithm with a hip exoskeleton and a single participant replicating hemiparetic walking. Results show that the algorithm effectively managed to reduce both temporal and spatial gait asymmetry

A lightweight, textile fabricated, haptic device consisting of an electrostatic adhesive clutch is introduced, which can constrain body movement when activated at low power (≈1 mW). The clutch electrodes are composite structures, prepared by coating copper‐plated polyester fabric with thin films of high‐κ dielectric ink. When voltage is applied across a pair of overlapping electrodes, the charge separation created between the overlapped surfaces gives rise to adhesive forces that resist tensile loads along the electrode surface. The clutch is arranged in parallel with a sheet of knitted fabric, which exhibits low‐stiffness spring‐like characteristics, thus decreasing load resistance when the clutch is deactivated. Mechanical tests are carried out to assess the dependency on scaling and loading rate at different voltages. The load‐bearing capacity of the device is experimentally shown to sustain a 10 kg load for a clutch pair with 120 × 70 mm2 dielectric overlap, when activated at 400 V. Current‐dependent charging and discharging times that can be as low as 15 ms are presented. To exemplify its pertinence in wearable applications, the device is used as an elbow joint constraint, exhibiting its conformability to curvatures and suitability for skin‐mounted applications.

Nowadays, lower-limb prostheses are reaching real-world usability especially on ground-level walking. However, some key tasks such as stair walking are still quite demanding. Providing haptic feedback about the foot placement on the steps might reduce the cognitive load of the task, compensating for increased dependency on vision and lessen the risk of falling. Experiments on intact subjects can be useful to define the feedback strategies prior to clinical trials, but effective methods to assess the efficacy of the strategies are few and usually rely on the emulation of the disability condition. The present study reports on the design and testing of a wearable haptic feedback system in a protocol involving intact subjects to assess candidate strategies to be adopted in clinical trials. The system integrated a sensorized insole wirelessly connected to a textile waist belt equipped with three vibrating motors. Three stimulation strategies for mapping the insole pressure data to vibrotactile feedback were implemented and compared in terms of intuitiveness and comfort perceived during level and stair walking. The strategies were ranked using a relative rating approach, which highlighted the differences between them and suggested guidelines for their improvement. The feedback evaluation procedure proposed could facilitate the selection and improvement of haptic feedback strategies prior to clinical testing.