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

Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized

Assistente de navegação com apontador laser para conduzir cadeiras de rodas robotizadas

Orientador: Eric RohmerDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: As soluções de robótica assistida ajudam as pessoas a recuperar sua mobilidade e autonomia perdidas em suas vidas diárias. Este documento apresenta um assistente de navegação de baixo custo projetado para pessoas tetraplégicas para dirigir uma cadeira de rodas robotizada usando a combinação da orientação da cabeça e expressões faciais (sorriso e sobrancelhas para cima) para enviar comandos para a cadeira. O assistente fornece dois modos de navegação: manual e semi-autônomo. Na navegação manual, uma webcam normal com o algoritmo OpenFace detecta a orientação da cabeça do usuário e expressões faciais (sorriso, sobrancelhas para cima) para compor comandos e atuar diretamente nos movimentos da cadeira de rodas (parar, ir à frente, virar à direita, virar à esquerda). No modo semi-autônomo, o usuário controla um laser pan-tilt com a cabeça para apontar o destino desejado no solo e valida com o comando sobrancelhas para cima que faz com que a cadeira de rodas robotizada realize uma rotação seguida de um deslocamento linear para o alvo escolhido. Embora o assistente precise de melhorias, os resultados mostraram que essa solução pode ser uma tecnologia promissora para pessoas paralisadas do pescoço para controlar uma cadeira de rodas robotizadaAbstract: Assistive robotics solutions help people to recover their lost mobility and autonomy in their daily life. This document presents a low-cost navigation assistant designed for people paralyzed from down the neck to drive a robotized wheelchair using the combination of the head's posture and facial expressions (smile and eyebrows up) to send commands to the chair. The assistant provides two navigation modes: manual and semi-autonomous. In the manual navigation, a regular webcam with the OpenFace algorithm detects the user's head orientation and facial expressions (smile, eyebrows up) to compose commands and actuate directly on the wheelchair movements (stop, go front, turn right, turn left). In the semi-autonomous, the user controls a pan-tilt laser with his/her head to point the desired destination on the ground and validates with eyebrows up command which makes the robotized wheelchair performs a rotation followed by a linear displacement to the chosen target. Although the assistant need improvements, results have shown that this solution may be a promising technology for people paralyzed from down the neck to control a robotized wheelchairMestradoEngenharia de ComputaçãoMestre em Engenharia ElétricaCAPE

CES-513 Stages for Developing Control Systems using EMG and EEG Signals: A survey

Bio-signals such as EMG (Electromyography), EEG (Electroencephalography), EOG (Electrooculogram), ECG (Electrocardiogram) have been deployed recently to develop control systems for improving the quality of life of disabled and elderly people. This technical report aims to review the current deployment of these state of the art control systems and explain some challenge issues. In particular, the stages for developing EMG and EEG based control systems are categorized, namely data acquisition, data segmentation, feature extraction, classification, and controller. Some related Bio-control applications are outlined. Finally a brief conclusion is summarized.

Mechatronic Systems

Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools

Review of Machine Vision-Based Electronic Travel Aids

Visual impaired people have navigation and mobility problems on the road. Up to now, many approaches have been conducted to help them navigate around using different sensing techniques. This paper reviews several machine vision- based Electronic Travel Aids (ETAs) and compares them with those using other sensing techniques. The functionalities of machine vision-based ETAs are classified from low-level image processing such as detecting the road regions and obstacles to high-level functionalities such as recognizing the digital tags and texts. In addition, the characteristics of the ETA systems for blind people are particularly discussed

Development And Human Performance Evaluation Of Control Modes Of An Exo-Skeletal Assistive Robotic Arm (esara)

This research was conducted to assist with functional tasks for a targeted group of individuals with spinal cord injury (SCI); with C5 to C7 level of injury relating to upper extremity movement. The specific population was selected as the existing technology was either too expensive, too bulky or was unable to address their needs in regards to upper extremity mobility. In addition, no platforms allowed multimodal control options for customization or provided a methodology for this crucial evaluation. The motivation of this research was to provide a methodology for selecting the appropriate control of an assistive device based on the range of basic human movements that were possible by the population under consideration (button pushing, lever sliding, and speech). The main idea was to create an evaluation methodology based on a user platform with multiple modes of control. The controls were developed such that they would allow operation of the device with respect to the capabilities of SCI participants. Engineering advancements have taken assistive robotics to new dimensions. Technologies such as wheelchair robotics and myo-electronically controlled systems have opened up a wide range of new applications to assist people with physical disabilities. Similarly exo-skeletal limbs and body suits have provided new foundations from which technologies can aid function. Unfortunately, these devices have issues of usability, weight, and discomfort with donning. The Smart Assistive Reacher Arm (SARA) system, developed in this research, is a voice-activated, lightweight, mobile device that can be used when needed. SARA was built to help overcome daily reach challenges faced by individuals with limited arm and hand movement capability, such as people with cervical level 5-6 (C5-6) SCI. The functional reacher arm with voice control can be beneficial for this population. Comparison study with healthy participants and an SCI participant shows that, when using SARA, a person with SCI can perform simple reach and grasp tasks independently, without someone else\u27s help. This suggests that the interface is intuitive and can be easily used to a high-level of proficiency by a SCI individual. Using SARA, an Exo-Skeletal Assistive Robotic Arm (eSARA) was designed and built. eSARA platform had multiple modes of control namely, voice (ballistic mode with no extremity movement), button (ballistic mode with minor extremity movement) and slider (continuous mode with major extremity movement). eSARA was able to extend a total of 7 inches from its original position. The platform also provided lift assist for users that can potentially enable them to lift up to 20lbs.The purpose of eSARA was to build a platform that could help design a methodology to select the modality for a specific level of SCI injury or capability. The eSARA platform\u27s Human Machine Interface (HMI) was based on two experiments `Fine movement experiment\u27 and `Gross movement experiment\u27. These experiments tested the reaching, grasping and lifting ability of the platform. Two groups of healthy young adults were selected to perform the experiment. The first group, 12 healthy participants, had no movement restrictions. The second group, 6 Occupational Therapy students, that could mimic restrictions similar to those of a level 5-6 SCI individual. The experiment was also conducted by an SCI individual. The results of the 2 groups from both the experiments were compared with the results of the SCI participant. It was found that the SCI participant\u27s time performance to finish the tasks was comparable to the average of the healthy participants. It was concluded that the developed methodology and platforms could be used to evaluate the control modes needed in order to customize the system to the capabilities of SCI individual. . These platforms can be tested for a broader range of participants including participants with arthritis, recovering from paralysis and seniors with movement issues