Robotic devices are being employed in more and more sectors to enhance, streamline, and augment the outcomes of a wide variety of human activities. Wearable robots arise indeed as of-vital-importance tools for telerehabilitation or home assistance targeting people affected by motor disabilities. In particular, the field of “Robotics for Medicine and Healthcare” is attracting growing interest. The development of such devices is a primarily addressed topic since the increasing number of people in need of rehabilitation or assistive therapies (due to population aging) growingly weighs on the healthcare systems of the nation. Besides, the necessity to move to clinics represents an additional logistic burden for patients and their families. Among the various body parts, the hand is specially investigated since it most ensures the independence of an individual, and thus, the restoration of its dexterity is considered a high priority. In this study, the authors present the development of a fully wearable, portable, and tailor-made hand exoskeleton designed for both home assistance and telerehabilitation. Its purpose is either to assist patients during activities of daily living by running a real-time intention detection algorithm or to be used for remotely supervised or unsupervised rehabilitation sessions by performing exercises preset by therapists. Throughout the mechatronic design process, special attention has been paid to the complete wearability and comfort of the system to produce a user-friendly device capable of assisting people in their daily life or enabling recorded home rehabilitation sessions allowing the therapist to monitor the state evolution of the patient. Such a hand exoskeleton system has been designed, manufactured, and preliminarily tested on a subject affected by spinal muscular atrophy, and some results are reported at the end of the article.

In recent years, the number of people with disabilities has increased hugely, especially in low- and middle-income countries. At the same time, robotics has made significant advances in the medical field, and many research groups have begun to develop low-cost wearable solutions. The Mechatronics and Dynamic Modelling Lab of the Department of Industrial Engineering at the University of Florence has recently developed a new version of a wearable hand exoskeleton for assistive purposes. In this paper, we will present a new regression method to predict the finger angle position of the first joint from the value of the sEMG of the forearm and the previous position of the finger itself. To acquire the dataset necessary to train the regressor a specific graphical user interface was developed which was able to acquire sEMG data from a Myo armband and the finger position from a Leap Motion Controller. Two long short-term memory (LSTM) models were compared, one in its standard configuration and the other with a convolutional layer, yielding significantly better performance for the second one, with an increase in $R^2$ coefficient from an average value of $0.746$ to $0.825$, leading to the conclusion that a convolutional layer could increase performance when few sensors are available.

In the new era of Industry 4.0, digitization and automation are the tipping points for addressing rapidly evolving challenges. The momentum of this movement has underscored the critical importance of developing new technologies to collect, understand and use the enormous amount of data created. This revolutionary wave has also taken root in the healthcare sector, giving rise to the so-called Health 4.0, based on smart machines to provide patients with better, more value-added and more efficient healthcare services. This scenario has contributed to making innovative robot-augmented therapies a hot research topic with potentially significant societal impact. In this context, the authors present the implementation of a new prototype of a kinaesthetic exoskeleton to enhance and recover sensorimotor skills to perform grasping and manipulation via force stimuli from serious rehabilitation games. The discussion includes an initial overview of the system's hardware, both from a mechanical and electronic point of view, motivating the choices made in the design phase. It continues by moving on to a description of the system's low-level and high-level software architecture, along with the control strategy applied to implement progressive resistant force therapy. Finally, an example of serious game was developed as an application case for evaluating the system's usability and extrapolating empirical parameters indicative of responsiveness and accuracy. The results showed that the proposed system can reproduce constant forces with an average error of 0.75 N and standard deviation of 0.3 N with an average reaction time of 0.5 s and standard deviation of 0.4 s.