The strict development processes of commercial upper-limb prostheses and the complexity of research projects required for their development makes them expensive for end users, both in terms of acquisition and maintenance. Moreover, many of them possess complex ways to operate and interact with the subjects, influencing patients to not favor these devices and shed them from their activities of daily living. The advent of 3D printers allows for distributed open-source research projects that follow new design principles; these consider simplicity without neglecting performance in terms of grasping capabilities, power consumption and controllability. In this work, a simple, yet functional design based on 3D printing is proposed, with the aim to reduce costs and manufacturing time. The operation process consists in interpreting the user intent with electromyography electrodes, while providing visual feedback through a μLCD screen. Its modular, parametric and self-contained design is intended to aid people with different transradial amputation levels, despite of the socket's constitution. This approach allows for easy updates of the system and demands a low cognitive effort from the user, satisfying a trade-off between functionality and low cost. It also grants an easy customization of the amount and selection of available actions, as well as the sensors used for gathering the user intent, permitting alterations to fit the patients' unique needs. Furthermore, experimental results showed an apt mechanical performance when interacting with everyday life objects, in addition to a highly accurate and responsive controller; this also applies for the user-prosthesis interface.

People taken by upper limb disorders caused by neurological diseases suffer from grip weakening, which affects their quality of life. Researches on soft wearable robotics and advances in sensor technology emerge as promising alternatives to develop assistive and rehabilitative technologies. However, current systems rely on surface electromyography and complex machine learning classifiers to retrieve the user intentions. In addition, the grasp assistance through electromechanical or fluidic actuators is passive and does not contribute to the rehabilitation of upper-limb muscles. Therefore, this paper presents a robotic glove integrated with a force myography sensor. The glove-like orthosis features tendon-driven actuation through servo motors, working as an assistive device for people with hand disabilities. The detection of user intentions employs an optical fiber force myography sensor, simplifying the operation beyond the usual electromyography approach. Moreover, the proposed system applies functional electrical stimulation to activate the grasp collaboratively with the tendon mechanism, providing motion support and assisting rehabilitation.

This work presents the development of an underactuated glove-like orthosis to be used by people with disabilities of the hand and focused on those who can not perform firm grasps. This assistive device uses a tendon-driven system in order to perform the finger flexion/extension movements that are triggered by optical fiber force myography sensors placed on an opposites pair of muscles. A simpler but robust strategy of control is proposed in order to consciously interpret the user intent. Moreover, a force sensitive resistor is used to close the control loop with the aim to perform a proportional grip strength to the force performed by the flexor group of muscles. Experiments conducted indicates satisfactory results related to the type of grasp, providing grip strenght to perform activities of daily living. In addition, results confirm that optical FMG transducers can be used as control input for trigger the system.

The strict development processes of commercial upper-limb prosthesis and complexity of research projects makes them expensive for end users, both in terms of acquisition and maintenance. The advent of 3D printers and the internet, allows for distributed open-source research projects that follow new design principles; these take into account simplicity without neglecting performance in terms of grasping capabilities, power consumption and controllability. We propose a simple yet functional design based on 3D printing with the aim to reduce cost and save time in the manufacturing process. Its modular, parametric and self-contained design is intended to be fitted in a wide range of people with different transradial amputation levels. Moreover, the system brings an original user-friendly user-prosthesis interface (UPI), in order to trigger and increase the amount of customized hand postures that can be performed by the users. Surface electromyography (sEMG) control allows the user to consciously activate the prosthetic actuation mechanism, a graphical interface enables the possibility to select between different sets of predefined gestures. A five-fingered prosthetic hand integrating intuitive myoelectric control and a graphical UPI was tested, obtaining great mechanical performance, in addition to high accuracy and responsiveness of the sEMG controller.

The complexity of User-Prosthesis Interfaces (UPIs) to control and select different grip modes and gestures of active upper-limb prostheses, as well as the issues presented by the use of electromyography (EMG), along with the long periods of training and adaptation influence amputees on stopping using the device. Moreover, development cost and challenging research makes the final product too expensive for the vast majority of transradial amputees and often leaves the amputee with an interface that does not satisfy his needs. Usually, EMG controlled multi grasping prosthesis are mapping the challenging detection of a specific contraction of a group of muscle to one type of grasping, limiting the number of possible grasps to the number of distinguishable muscular contraction. To reduce costs and to facilitate the interaction between the user and the system in a customized way, we propose a hybrid UPI based on object classification from images and EMG, integrated with a 3D printed upper-limb prosthesis, controlled by a smartphone application developed in Android. This approach allows easy updates of the system and lower cognitive effort required from the user, satisfying a trade-off between functionality and low cost. Therefore, the user can achieve endless predefined types of grips, gestures, and sequence of actions by taking pictures of the object to interact with, only using four muscle contractions to validate and actuate a suggested type of interaction. Experimental results showed great mechanical performances of the prosthesis when interacting with everyday life objects, and high accuracy and responsiveness of the controller and classifier.