This article presents the design of a hand exoskeleton that features its modularity and the possibility of integrating a force sensor in its frame. The modularity is achieved by dividing the exoskeleton in separate units, each one driving a finger or pair of them. These units or “finger modules” have a single degree of freedom and may be easily attached or removed from the robot frame and human fingers by snap-in fixations. As for the force sensing capability, the device relies on a novel force sensor that uses optical elements to amplify and measure small elastic deformations in the robot structure. This sensor can be fully integrated as a structural element of the finger module. The proposed technology has been validated in two experimental sessions. A first study was performed in a clinical environment in order to check whether the hand exoskeleton (without the integrated force sensor) can successfully move an impaired hand in a “Mirror Therapy” environment. A second study was carried with healthy subjects to check the technical feasibility of using the integrated force sensor as a human–machine interface.

In this paper, the analysis of the intensity of muscle activations in different subjects when they perform an industrial task in a repetitive way assisted by a robotic upper-limb exoskeleton is presented. To do that, surface electromyography (EMG) signals were monitored with and without a robotic upper-limb exoskeleton for 10 subjects during a drilling task, a typical tedious maintenance or industrial task. Our results show that wearing the upper-limb exoskeleton substantially reduces muscle activity during a drilling task above head height. Specifically, there is statistically significant differences in the pectoralis major and rhomboids muscles between the groups wearing or not wearing the robotic upper-limb exoskeleton.

This paper presents a novel kinematic reconstruction of the human arm chain with five degrees of freedom and the estimation of the shoulder location during rehabilitation therapy assisted by end-effector robotic devices. This algorithm is based on the pseudoinverse of the Jacobian through the acceleration of the upper arm, measured using an accelerometer, and the orientation of the shoulder, estimated with a magnetic angular rate and gravity (MARG) device. The results show a high accuracy in terms of arm joints and shoulder movement with respect to the real arm measured through an optoelectronic system. Furthermore, the range of motion (ROM) of 50 healthy subjects is studied from two different trials, one trying to avoid shoulder movements and the second one forcing them. Moreover, the shoulder movement in the second trial is also estimated accurately. Besides the fact that the posture of the patient can be corrected during the exercise, the therapist could use the presented algorithm as an objective assessment tool. In conclusion, the joints’ estimation enables a better adjustment of the therapy, taking into account the needs of the patient, and consequently, the arm motion improves faster.

When combined with assistive robotic devices, such as wearable robotics, brain/neural-computer interfaces (BNCI) have the potential to restore the capabilities of handicapped people to carry out activities of daily living. To improve applicability of such systems, workload and stress should be reduced to a minimal level. Here, we investigated the user’s physiological reactions during the exhaustive use of the interfaces of a hybrid control interface. Eleven BNCI-naive healthy volunteers participated in the experiments. All participants sat in a comfortable chair in front of a desk and wore a whole-arm exoskeleton as well as wearable devices for monitoring physiological, electroencephalographic (EEG) and electrooculographic (EoG) signals. The experimental protocol consisted of three phases: (i) Set-up, calibration and BNCI training; (ii) Familiarization phase; and (iii) Experimental phase during which each subject had to perform EEG and EoG tasks. After completing each task, the NASA-TLX questionnaire and self-assessment manikin (SAM) were completed by the user. We found significant differences (p-value < 0.05) in heart rate variability (HRV) and skin conductance level (SCL) between participants during the use of the two different biosignal modalities (EEG, EoG) of the BNCI. This indicates that EEG control is associated with a higher level of stress (associated with a decrease in HRV) and mental work load (associated with a higher level of SCL) when compared to EoG control. In addition, HRV and SCL modulations correlated with the subject’s workload perception and emotional responses assessed through NASA-TLX questionnaires and SAM.

This article researches the feasibility of use of a multimodal robotic system for upper-limb neurorehabilitation therapies in physical environments, interacting with real objects. This system consists of an end-effector upper-limb rehabilitation robot, a hand exoskeleton, a gaze tracking system, an object tracking system, and electromyographic measuring units. For this purpose, the system architecture is stated, explaining the detailed functions of each subsystem as well as the interaction among them. Finally, an experimental scenario is designed to test the system with healthy subjects in order to check whether the system is suitable for future experiments with patients.

This paper presents the development of an optical force sensor architecture directed to prototyping and cost-effective applications, where the actual force requirements are still not well defined or the most suitable commercial technologies would highly increase the cost of the device. The working principle of this sensor consists of determining the displacement of a lens by measuring the distortion of a refracted light beam. This lens is attached to an elastic interface whose elastic constant is known, allowing the estimation of the force that disturbs the optical system. In order to satisfy the requirements of the design process in an inexpensive way, this sensor can be built by fast prototyping technologies and using non-optical grade elements. To deal with the imperfections of this kind of manufacturing procedures and materials, four fitting models are proposed to calibrate the implemented sensor. In order to validate the system, two different sensor implementations with measurement ranges of ±45 N and ±10 N are tested with the proposed models, comparing the resulting force estimation with respect to an industrial-grade load cell. Results show that all models can estimate the loads with an error of about 6% of the measurement range.