A high-level control algorithm capable of generating position and torque references from surface electromyography signals (sEMG) was designed. It was applied to a shape memory alloy (SMA)-actuated exoskeleton used in active rehabilitation therapies for elbow joints. The sEMG signals are filtered and normalized according to data collected online during the first seconds of a therapy session. The control algorithm uses the sEMG signals to promote active participation of patients during the therapy session. In order to generate the reference position pattern with good precision, the sEMG normalized signal is compared with a pressure sensor signal to detect the intention of each movement. The algorithm was tested in simulations and with healthy people for control of an elbow exoskeleton in flexion–extension movements. The results indicate that sEMG signals from elbow muscles, in combination with pressure sensors that measure arm–exoskeleton interaction, can be used as inputs for the control algorithm, which adapts the reference for exoskeleton movements according to a patient’s intention.

Nowadays, robotic technologies are used in many fields, one of them is medicine. The breakthrough of technologies has enabled the development of robotic devices for rehabilitation therapies. The exoskeletons can be found as robotic device for rehabilitation therapy, whose rigidity limits the user's freedom of movement, complicating his interaction with the environment in an easy and natural way. To overcome this current limitation, researchers invest effort in the development of soft devices, wearable, portable and comfortable for patients, maintaining the rigid exoskeletons performance. The soft exo-glove proposed in this work, accomplishes three main previous features. It is actuated by Shape Memory Alloys (SMA) that reduce the device weight and turn it into a portable exoskeleton. The exo-glove is easier to wear and it is comfortable. The proposed exo-glove represents a safe and adaptable solution for the human hand rehabilitation therapy with special emphasis on the activities of daily living (ADL). We are going to analyze exo-glove performance during ADL movements, e.g. gripping different objects with diverse dimensions, in which, other exoskeletons have some limitations. In terms to address this challenge, a specific position controller was designed which enables the single displacement of each finger with a linear actuator precision of 5%.

Shape memory alloy (SMA) actuators have recently demonstrated their potential for various applications in fields such as robotics, medicine, aerospace, and automotive. Its features, such as low weight and high force, simplicity, noiseless operation, and low cost compared with other conventional actuator, are only a few advantages of this actuator, which is receiving increasing interest among researchers. However, the use of these actuators is still limited by some of their characteristics: high position error in the cooling stage when the actuator works at frequencies that exceed the necessary cooling time and high electrical energy consumption. Different actuator configurations can help minimize these disadvantages through modifying the length, the number of cables, or the sheath used in the actuator, which modify the characteristics of the complete system. In this work, we developed different configurations of SMA actuators and tested their performance in terms of efficiency and the position error in the cooling stage. The findings demonstrate that over-dimensioned actuators are more energetically efficient and present a faster initial form recovery. The multi-wires actuator configuration produce a better response in terms of position but are less energy efficient. These conclusions allow for the selection of the most appropriate configuration based on the requirements of each particular application.

The movement of the human hand offers various degrees of freedom, enabling efficient performance of dynamic tasks and robust interaction with the environment in a compliant and continuous manner. However, the rigid exoskeleton used in hand rehabilitation limits the user’s freedom of movement, complicating their natural interaction with the environment. In this study, we present a soft exo-glove for assistive rehabilitation actuated by Shape Memory Alloys (SMA), controlled by a surface electromyography (sEMG) hand gesture classifier. Thanks to the actuator type, the soft exo-glove enables slow, smooth, and controlled movements when activated and provides complete control transparency when the device is not active. This advantage enhances the comfort and acceptance of the exo-glove by the patient. On the other hand, the classifier, in conjunction with the control algorithm and the soft exo-glove, offers the potential to use the exo-glove in assistive hand rehabilitation therapy. For user-friendly use, an interface has been developed, enabling the acquisition of new sEMG data from new users, retraining of the classifier, and connection with the soft exo-glove for rehabilitation therapy. The main objective of this study is to demonstrate that the proposed wearable soft exo-glove, along with the control algorithm and the employed classifier, constitutes an effective solution for assistive rehabilitation tasks, as demonstrated with healthy subjects. Furthermore, this solution can be easily adapted to the users’ characteristics and requirements.