The use of wearable equipment and sensing devices to monitor physical activities, whether for well-being, sports monitoring, or medical rehabilitation, has expanded rapidly due to the evolution of sensing techniques, cheaper integrated circuits, and the development of connectivity technologies. In this scenario, this paper presents a state-of-the-art review of sensors and systems for rehabilitation and health monitoring. Although we know the increasing importance of data processing techniques, our focus was on analyzing the implementation of sensors and biomedical applications. Although many themes overlap, we organized this review based on three groups: Sensors in Healthcare, Home Medical Assistance, and Continuous Health Monitoring; Systems and Sensors in Physical Rehabilitation; and Assistive Systems.

https://www.mdpi.com/1424-8220/20/15/4063/pdf

Sensors and Systems for Physical Rehabilitation and Health Monitoring-A Review.

Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.

Summary of over Fifty Years with Brain-Computer Interfaces-A Review.

Human–computer interface (HCI) systems are extending their boundaries in our daily life and becoming an important subject in biomedical engineering. Electrooculogram (EOG) signal as an input for such systems stems from the corneoretinal standing potential, which can be used for monitoring human eye rotation. Higher amplitude, better signal-to-noise ratio, and much easier recording conditions compared with electroencephalography, make it an important input modality for HCI systems. In this article, real-time processing and cost-effective (< 100$) HCI system was designed and developed based on the EOG signals. The required electrodes were embedded in updated eyeglasses for easy electrode placement over the subject’s face. EOG signals were acquired by the subject’s eye movement toward the four middle parts of the screen edges of a laptop placed in front of them. The system training for each subject ameliorated system withstanding against the blink and wrinkle artifact. Finally, the required commands for quadcopter navigation (up, down, left, and right) generated with 0.6-s total delay and 94.8% of system accuracy in detecting the correct eye movements. A real quadcopter navigation experiment based on the standard navigation interface and the developed HCI system represented the system accuracy, robustness, and usefulness.

EOG-Based HCI System for Quadcopter Navigation

Comparison of classification techniques for the control of EOG-based HCIs

In this study, a low cost single channel electrooculography (EOG) amplifier circuit and interface software have been created. This interface was created using the Python programming language. In this interface, EOG signals can be drawn and recorded in real time. These recorded signals can be redrawn later on offline interface. In addition, class information of blink, looking up and down eye movements was obtained through 3 warning lamps added to the interface. Thresholding method was used to classify these eye movements.Unlike the 2 channel EOG measurement circuits in the literature, a single channel EOG amplifier circuit was used in this study. Eye movements in vertical and horizontal planes with a small change in the electrode placement areas were visualized with this single channel amplifier circuit. The EOG signals received in response to these eye movements were compared with the signals received with 2 channel EOG amplifier circuits in the literature. There was little decrease in the amplitude values of the signals. However, there was no change in the general shape of the signals.

Single Channel EOG Measurement System and Interface Design

A Brain Computer Interface based Communication System using SSVEP and EOG

A high performance asynchronous EOG speller system

The brain computer interface (BCI) speller system can be classified into synchronous and asynchronous type. In synchronous type, one of the target characters is selected with specific interval periodically even if the user is making or not making attention on the target, whereas, in asynchronous case, the target character will not be selected until a confirmation signal is received from the user. In this proposed study a novel oddball stimulus paradigm is introduced on the hybrid steady state visual evoked potential (SSVEP) and electrooculogram (EOG) speller system to achieve an asynchronous control and high performance. The proposed system consists of forty characters grouped and indexed into five flickering unique frequency visual stimuli. Each visual stimulus is assigned with eight unique characters. The characters in each group are randomly highlighted by the oddball paradigm and the user performs blink eye movement in synchrony with the desired target character highlight. An asynchronous control is achieved by EOG signal and oddball paradigm because the target character will not be selected until the user performs blink eye movement. The stimulus paradigm helps the user to select the target group and desired target character, concurrently by SSVEP and EOG signal which increases the performance of the system. The proposed asynchronous speller system is tested and validated on ten subjects. The online classification accuracy and information transfer rate (ITR) of the proposed hybrid speller system are 99.38% and 116.58 bits/min respectively. Performance metrics of the proposed system are compared with the conventional speller systems and is found to be much superior to existing systems.

/pdf/stimulus-paradigm-for-an-asynchronous-concurrent-ssvep-and-1wm4knbhv1.pdf

Ramasubba Reddy M

Papers

A Brain Computer Interface based Communication System using SSVEP and EOG

A high performance asynchronous EOG speller system

Stimulus Paradigm for an Asynchronous Concurrent SSVEP and EOG Based BCI Speller System