The application of an optical fiber sensor on the identification of hand postures by force myography technique is reported. The transducers are comprised of fiber microbending elements attached to the user forearm, allowing the assessment of muscular activities related to the hand movements. Next, the output light intensities are detected, and then correlated to the performed postures by means of artificial neural networks. The methodology was applied on the identification of 5 postures, yielding a 96.7% accuracy considering the average of 4 subjects. Finally, the sensor was utilized for controlling a virtual prosthetic hand implemented on V-REP environment, based on the event driven finite-state approach, providing the real-time execution of predefined grasp patterns.

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 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.

People who suffered a stroke, or spinal cord injury, still rely on the use of exhaustive physical therapy for recovery. When the aftereffects of these illness act on upper limbs as the hand, this causes a great impact on their quality of life. As alternative resources to the physical therapy, functional electrical stimulation and exoskeletons, or wearable robotic gloves, are consolidating as important resources to help to address this problem. Functional electrical stimulation has been proved to be an efficient rehabilitative technology, while exoskeletons are assistive technology helping people with a deficiency in their daily activities. In order to put these technologies working together, we propose a hybrid control strategy, making use of a tendon-driven robotic glove and functional electrical stimulation. Our aim is develop a glove-like orthosis for hand with the advantages of both technologies. This combined control strategy may have the potential to be used for hand rehabilitation.

Force myography (FMG) detects hand gestures based on muscular contractions, featuring as an alternative to surface electromyography. However, typical FMG systems rely on spatially-distributed arrays of force-sensing resistors to resolve ambiguities. The aim of this proof-of-concept study is to develop a method for identifying hand poses from the static and dynamic components of FMG waveforms based on a compact, single-channel optical fiber sensor. As the user performs a gesture, a micro-bending transducer positioned on the belly of the forearm muscles registers the dynamic optical signals resulting from the exerted forces. A Raspberry Pi 3 minicomputer performs data acquisition and processing. Then, convolutional neural networks correlate the FMG waveforms with the target postures, yielding a classification accuracy of (93.98 ± 1.54)% for eight postures, based on the interrogation of a single fiber transducer.