Biosignal‐based human–machine interfaces for assistance and rehabilitation : a survey

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal‐based HMIs for assistance and rehabilitation to outline state‐of‐the‐art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full‐text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever‐growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complex-ity, so their usefulness should be carefully evaluated for the specific application

A virtual reality input device for sports-related rehabilitation

Abstract. This work entails the hardware design, manufacturing and implementation of a VR controller device tailored for people with specific sports-related injuries. The target case of this thesis is the tennis elbow injury, where the designed controller helps them interface easily to the VR environment that is designed for their therapy. The sensors used are carefully selected in order to adequately capture the therapy exercise movements related to this kind of injury. For example, the use FSRs (Force Sensitive Resistors) that are put on the surface of a test object helps to detect a grasp during the exercise. The hardware design and manufacturing was done for a VR controller device that would give the desired performance, using Arduino IDE for its software development. In addition to this, the design of the VR environment allowed for an immersive VR experience for the rehabilitation. An experiment was carried out with eight participants, where they were asked to perform two exercises that involve grasping the test object. A series of questions were asked to them as part of the experimental evaluation. The results showed positive indications about the participants’ experience

Integrated optical fiber force myography sensor as pervasive predictor of hand postures

Force myography (FMG) is an appealing alternative to traditional electromyography in biomedical applications, mainly due to its simpler signal pattern and immunity to electrical interference. Most FMG sensors, however, send data to a computer for further processing, which reduces the user mobility and, thus, the chances for practical application. In this sense, this work proposes to remodel a typical optical fiber FMG sensor with smaller portable components. Moreover, all data acquisition and processing routines were migrated to a Raspberry Pi 3 Model B microprocessor, ensuring the comfort of use and portability. The sensor was successfully demonstrated for 2 input channels and 9 postures classification with an average precision and accuracy of ~99.5% and ~99.8%, respectively, using a feedforward artificial neural network of 2 hidden layers and a competitive output layer11CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPNão tem0012017/25666-

Feasibility of integrating multiple types of electroactive polymers to develop a biomimetic inspired muscle actuator

The focus of this project is to see if it is possible to integrate multiple EAP materials in an electro- mechanical system to produce a closer representation of a biological muscle with smooth varying motion. In this preliminary study, two common types of EAPs, ionic and dielectric, were investigated to determine their mechanical and electrical properties in order to assess their potential to be combined into a working artificial electromechanical muscle prototype at a later time. A conceptual design for an artificial electromechanical muscle was created with biomimetic relationships between EAP materials and the human bicep muscle. With the assistance of the Rochester General Hospital, a human arm model, isolating the bicep muscle, was created to calculate mechanical characteristics of the bicep brachii. From the human arm model, bicep muscle characteristics were compared to those of the dielectric EAP because of the ability for the EAP to output relatively high force and strain during actuation. It was found that the current state of the art of EAPs is a long way from making this a reality due to their limiting force output and voltage requirements. The feasibility of developing an artificial electromechanical muscle with EAP actuators is not possible with current technology