Empowering and assisting natural human mobility: The simbiosis walker

This paper presents the complete development of the Simbiosis Smart Walker. The device is equipped with a set of sensor subsystems to acquire user-machine interaction forces and the temporal evolution of user's feet during gait. The authors present an adaptive filtering technique used for the identification and separation of different components found on the human-machine interaction forces. This technique allowed isolating the components related with the navigational commands and developing a Fuzzy logic controller to guide the device. The Smart Walker was clinically validated at the Spinal Cord Injury Hospital of Toledo - Spain, presenting great acceptability by spinal chord injury patients and clinical staf

Novel Approch of Man Machine Interaction using Brain Waves Electric Signals

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Sensory System for Implementing a Human—Computer Interface Based on Electrooculography

This paper describes a sensory system for implementing a human–computer interface based on electrooculography. An acquisition system captures electrooculograms and transmits them via the ZigBee protocol. The data acquired are analysed in real time using a microcontroller-based platform running the Linux operating system. The continuous wavelet transform and neural network are used to process and analyse the signals to obtain highly reliable results in real time. To enhance system usability, the graphical interface is projected onto special eyewear, which is also used to position the signal-capturing electrodes

Steering a Tractor by Means of an EMG-Based Human-Machine Interface

An electromiographic (EMG)-based human-machine interface (HMI) is a communication pathway between a human and a machine that operates by means of the acquisition and processing of EMG signals. This article explores the use of EMG-based HMIs in the steering of farm tractors. An EPOC, a low-cost human-computer interface (HCI) from the Emotiv Company, was employed. This device, by means of 14 saline sensors, measures and processes EMG and electroencephalographic (EEG) signals from the scalp of the driver. In our tests, the HMI took into account only the detection of four trained muscular events on the driver’s scalp: eyes looking to the right and jaw opened, eyes looking to the right and jaw closed, eyes looking to the left and jaw opened, and eyes looking to the left and jaw closed. The EMG-based HMI guidance was compared with manual guidance and with autonomous GPS guidance. A driver tested these three guidance systems along three different trajectories: a straight line, a step, and a circumference. The accuracy of the EMG-based HMI guidance was lower than the accuracy obtained by manual guidance, which was lower in turn than the accuracy obtained by the autonomous GPS guidance; the computed standard deviations of error to the desired trajectory in the straight line were 16 cm, 9 cm, and 4 cm, respectively. Since the standard deviation between the manual guidance and the EMG-based HMI guidance differed only 7 cm, and this difference is not relevant in agricultural steering, it can be concluded that it is possible to steer a tractor by an EMG-based HMI with almost the same accuracy as with manual steering

The Remote Controllable Electric Wheelchair System combined Human and Machine Intelligence for Caregivers and Care Receivers

Thesis (Master of Science in Informatics)--University of Tsukuba, no. 41280, 2019.3.2

A Study of recent classification algorithms and a novel approach for biosignal data classification

Analyzing and understanding human biosignals have been important research areas that have many practical applications in everyday life. For example, Brain Computer Interface is a research area that studies the connection between the human brain and external systems by processing and learning the brain signals called Electroencephalography (EEG) signals. Similarly, various assistive robotics applications are being developed to interpret eye or muscle signals in humans in order to provide control inputs for external devices. The efficiency for all of these applications depends heavily on being able to process and classify human biosignals. Therefore many techniques from Signal Processing and Machine Learning fields are applied in order to understand human biosignals better and increase the efficiency and success of these applications. This thesis proposes a new classifier for biosignal data classification utilizing Particle Swarm Optimization Clustering and Radial Basis Function Networks (RBFN). The performance of the proposed classifier together with several variations in the technique is analyzed by utilizing comparisons with the state of the art classifiers such as Fuzzy Functions Support Vector Machines (FFSVM), Improved Fuzzy Functions Support Vector Machines (IFFSVM). These classifiers are implemented on the classification of same biological signals in order to evaluate the proposed technique. Several clustering algorithms, which are used in these classifiers, such as K-means, Fuzzy c-means, and Particle Swarm Optimization (PSO), are studied and compared with each other based on clustering abilities. The effects of the analyzed clustering algorithms in the performance of Radial Basis Functions Networks classifier are investigated. Strengths and weaknesses are analyzed on various standard and EEG datasets. Results show that the proposed classifier that combines PSO clustering with RBFN classifier can reach or exceed the performance of these state of the art classifiers. Finally, the proposed classification technique is applied to a real-time system application where a mobile robot is controlled based on person\u27s EEG signal

Real-time estimation of horizontal gaze angle by saccade integration using in-ear electrooculography

The manuscript proposes and evaluates a real-time algorithm for estimating eye gaze angle based solely on single-channel electrooculography (EOG), which can be obtained directly from the ear canal using conductive ear moulds. In contrast to conventional high-pass filtering, we used an algorithm that calculates absolute eye gaze angle via statistical analysis of detected saccades. The estimated eye positions of the new algorithm were still noisy. However, the performance in terms of Pearson product-moment correlation coefficients was significantly better than the conventional approach in some instances. The results suggest that in-ear EOG signals captured with conductive ear moulds could serve as a basis for lightweight and portable horizontal eye gaze angle estimation suitable for a broad range of applications. For instance, for hearing aids to steer the directivity of microphones in the direction of the user’s eye gaze

Applications of the electric potential sensor for healthcare and assistive technologies

The work discussed in this thesis explores the possibility of employing the Electric Potential Sensor for use in healthcare and assistive technology applications with the same and in some cases better degrees of accuracy than those of conventional technologies. The Electric Potential Sensor is a generic and versatile sensing technology capable of working in both contact and non-contact (remote) modes. New versions of the active sensor were developed for specific surface electrophysiological signal measurements. The requirements in terms of frequency range, electrode size and gain varied with the type of signal measured for each application. Real-time applications based on electrooculography, electroretinography and electromyography are discussed, as well as an application based on human movement. A three sensor electrooculography eye tracking system was developed which is of interest to eye controlled assistive technologies. The system described achieved an accuracy at least as good as conventional wet gel electrodes for both horizontal and vertical eye movements. Surface recording of the electroretinogram, used to monitor eye health and diagnose degenerative diseases of the retina, was achieved and correlated with both corneal fibre and wet gel surface electrodes. The main signal components of electromyography lie in a higher bandwidth and surface signals of the deltoid muscle were recorded over the course of rehabilitation of a subject with an injured arm. Surface electromyography signals of the bicep were also recorded and correlated with the joint dynamics of the elbow. A related non-contact application of interest to assistive technologies was also developed. Hand movement within a defined area was mapped and used to control a mouse cursor and a predictive text interface