A headset-based minimized wearable computer

A headset with a sensory system and a short-range wireless radio transceiver can become a highly available, context-aware peripheral device. The author describes a headset that uses a combination of low-power motion sensors and microcontrollers to translate a user's behavior into corresponding symbolic codes.     

Enabling human–machine collaboration in infrastructure inspections through mixed reality

In this study, we propose a novel end-to-end system called Human–Machine Collaborative Inspection (HMCI) to enable collaboration between inspectors with Mixed Reality (MR) headsets and a robotic data collection platform (robot) for structural inspections. We utilize the MR headset’s holographic display and precise head tracking to allow inspectors to visualize and localize information (e.g., structural defect) on the real scenes, which are gathered by the robot and processed by an offsite computational server. The primary use case of HMCI is to enable the inspector to visualize, supervise, and improve results produced by automated defect detection algorithms in near real-time. The workflow in HMCI starts with collecting images and depth data to generate 3D maps of the site from the robot. A technique called single-shot localization is developed to create visual anchors for real-time spatial alignment between the robot and the MR headset. The 3D map and images are then sent to the computational server for analysis to detect defects and their locations. Then, the information is received by the MR headset and overlaid on the actual scenes to visualize it with spatial context. An experimental study is conducted in a lab environment to demonstrate HMCI using Microsoft HoloLens 2 (HL2) as the MR headset and Turtlebot2 as the robot. We start with the reconstruction of a 3D environment using a 3D depth sensor (Azure Kinect) on Turtlebot2 and visually detect fiducial markers as regions-of-interest (replicating structural damage) along a predefined inspection path. Then, regions-of-interest are successfully anchored to the real scene and visualized through HL2. To our knowledge, HMCI is one of the first human–machine collaborative systems that can integrate robots and inspectors with the MR headset, which has been developed, tested, and presented for structural inspection.     

Improving speech understanding in communication headsets: Simulation of adaptive subband processing for speech in noise

Speech communication headsets are necessary for many high-noise environments to maintain interaction between individuals and facilitate safe working conditions. However, current hearing protection devices intended to protect hearing health can impede speech communication or expose persons to sound pressure levels (SPLs) that could lead to excessive noise exposure if a communication signal is presented improperly. This paper explores an adaptive subband communication algorithm, based on a delayless subband active noise reduction architecture, intended to adjust the communication channel gain to provide an appropriate speech signal power in relation to the instantaneous environmental noise power. The method monitors SPLs underneath the ear cup of a communication headset to provide a target speech signal-to-noise ratio without exceeding safe noise exposure thresholds. A series of computer simulations derived from a real-world communication headset model are used to compare the method developed with a traditional passive attenuation headset and a commercial active noise reduction design. The simulations demonstrate the ability of the adaptive subband communication algorithm to adjust automatically the speech signal gain for improved intelligibility while maintaining healthy noise exposure levels.Relevance to industryThe electro-acoustic performance of an active speech communication headset is explored by simulation. The concept integrates a subband active noise control algorithm with an adaptive gain control structure to improve speech intelligibility in a noisy environment. The concept automatically selects appropriate communication channel gain levels without exceeding hearing damage thresholds or requiring user input, and is directly applicable to a practical device.     

Evaluation of commercial brain–computer interfaces in real and virtual world environment: A pilot study

This paper identifies the user’s adaptation on brain-controlled systems and the ability to control brain-generated events in a closed neuro-feedback loop. The user experience is quantified for the further understanding of brain–computer interfacing. A working system has been developed based on off-the-shelf components for controlling a robot in both the real and virtual world. Using commercial brain–computer interfaces (BCIs) the overall cost, set up time and complexity can be reduced. The system is divided in two prototypes based on the headset type used. The first prototype is based on the Neurosky headset and it has been tested with 54 participants in a field study. The second prototype is based on the Emotiv headset including more sensors and accuracy, tested with 31 participants in a lab environment. Evaluation results indicate that robot navigation through commercial BCIs can be effective and natural both in the real and the virtual environment.     

Measuring circular vection speed in a virtual reality headset

Understanding the relationship between vection speed and display speed is important to help ensure accurate self-motion experiences in a variety of applications. In the present research, a virtual optokinetic drum was used to induce circular vection at three distinct constant velocities and a method for measuring circular vection speed was developed and examined. In Experiment 1, vection strength was measured using a traditional magnitude estimation rating technique. An optokinetic drum presented on a virtual reality (VR) headset rotated in the yaw axis at slow, medium, and fast speeds. Magnitude estimation ratings were greatest during the fast condition in Experiment 1, replicating previous findings and validating the use of the virtual optokinetic drum in a VR headset. In Experiment 2, participants spun a circular knob in a clockwise or counter-clockwise direction to index their vection speed, direction and duration. The speed at which participants spun the knob increased with drum speed. Vection speed was much faster than the optokinetic drum’s speed, demonstrating that participants may perceive self-motion at faster speeds than intended. It is recommended that indices of vection speed such as the circular knob be used to better understand the relationship between graphic speed and vection speed.     

Are low cost Brain Computer Interface headsets ready for motor imagery applications?

Low cost electroencephalography (EEG) headset devices for brain data capturing are fast becoming a key instrument on Brain Computer Interface (BCI) applications. In spite of being a controller device initially developed for gaming, the research community has adopted them as a key element to gather EEG data. However, there have been little discussion about their performance when being compared with professional and research EEG headsets.This paper provides an assessment of one of these devices, the Emotiv EPOC, on a motor imagery problem. As a benchmark, the data and results presented for the Data Set V of the BCI Competition III have been used, which were recorded with a professional Biosemi Active 2 EEG headset. From the perspective of a final working application, it is shown that the performance of this headset is comparable to that found in professional devices when using the same number of sensors and sensor positions for a three status motor imagery cognitive process. This finding implies an increase on the number of EEG headsets the researchers and manufacturers of BCI systems applied to motor imagery problems can integrate and a reduction of their cost.As part of this paper the Emotiv EPOC recorded raw and pre-processed datasets are published to allow further improvements and comparisons.     

Exploring educational immersive videogames: an empirical study with a 3D multimodal interaction prototype

ABSTRACT

Gestural interaction devices emerged and originated various studies on multimodal human–computer interaction to improve user experience (UX). However, there is a knowledge gap regarding the use of these devices to enhance learning. We present an exploratory study which analysed the UX with a multimodal immersive videogame prototype, based on a Portuguese historical/cultural episode. Evaluation tests took place in high school environments and public videogaming events. Two users would be present simultaneously in the same virtual reality (VR) environment: one as the helmsman aboard Vasco da Gama’s fifteenth-century Portuguese ship and the other as the mythical Adamastor stone giant at the Cape of Good Hope. The helmsman player wore a VR headset to explore the environment, whereas the giant player used body motion to control the giant, and observed results on a screen, with no headset. This allowed a preliminary characterisation of UX, identifying challenges and potential use of these devices in multi-user virtual learning contexts. We also discuss the combined use of such devices, towards future development of similar systems, and its implications on learning improvement through multimodal human–computer interaction.     

Towards the Use of Brain–Computer Interface and Gestural Technologies as a Potential Alternative to PIN Authentication

This article describes an exploratory study which examined the use of Brain–Computer Interface (BCI) and gestural technologies generated from a BCI headset as a novel potential alternative to a 4-digit PIN code for authentication. Unlike other input modalities, many of these tokens (i.e., “push,” “lift,” “excitement”), can overcome some of the security vulnerabilities associated with PIN authentication (e.g., observations from third parties). Participants engaged in a controlled study where they performed five, 4-token authentication tasks on a simulated authentication screen. The percentage of completed BCI and gestural input tasks, as well as input time and accuracy, was compared to the 4-digit PIN task. The results showed that while authentication using a BCI headset is currently not as complete, fast, or accurate as that of a 4-digit PIN code, users felt that such a system would represent greater security over PIN-based authentication. In addition, mental commands, which might be perceived as the most secure from the standpoint of inconspicuous detection, were found to offer disappointing results both in terms of completion percentage and completion time.     

A New Wi-Fi for Peer-to-Peer Communications

Topics include a research chip that provides fast short-range wireless communications, a product that provides peer-to-peer Wi-Fi services, a technology that could great increase laptop-battery life, and a headset that would let users control computer games with neural activity associated with their thoughts and facial expressions.     

VALENCE: affective visualisation using EEG

VALENCE is an interactive visualisation controlled by live brainwave monitoring. We used a wireless EEG headset to monitor the player's alpha waves (an indicator of relaxation) and valence (an indicator of emotion or arousal). The game world is an emergent system of attractive and repulsive forces responding to EEG input.     

Towards an EEG-based brain-computer interface for online robot control

According to New York Times, 5.6 million people in the United States are paralyzed to some degree. Motivated by requirements of these paralyzed patients in controlling assisted-devices that support their mobility, we present a novel EEG-based BCI system, which is composed of an Emotive EPOC neuroheadset, a laptop and a Lego Mindstorms NXT robot in this paper. We provide online learning algorithms that consist of k-means clustering and principal component analysis to classify the signals from the headset into corresponding action commands. Moreover, we also discuss how to integrate the Emotiv EPOC headset into the system, and how to integrate the LEGO robot. Finally, we evaluate the proposed online learning algorithms of our BCI system in terms of precision, recall, and the F-measure, and our results show that the algorithms can accurately classify the subjects’ thoughts into corresponding action commands.     

一种用于有源降噪耳机系统的次级通道建模技术

实现了一种用于有源降噪耳机的次级通道建模方法,这种方法采用音乐片段作为激励信号,引入一个自适应误差预测器克服次级通道建模过程中遇到的干扰的影响,使得建模精度更高.对预测器中不同的延迟进行分析讨论,得出最佳延迟等于次级通道脉冲响应长度的结论.实验表明,这种方法能够较好地剔除窄带干扰对建模的影响,具有较高的实用价值.     

Classification of primitive shapes using brain–computer interfaces

Brain–computer interfaces (BCIs) are recent developments in alternative technologies of user interaction. The purpose of this paper is to explore the potential of BCIs as user interfaces for CAD systems. The paper describes experiments and algorithms that use the BCI to distinguish between primitive shapes that are imagined by a user. Users wear an electroencephalogram (EEG) headset and imagine the shape of a cube, sphere, cylinder, pyramid or a cone. The EEG headset collects brain activity from 14 locations on the scalp. The data is analyzed with independent component analysis (ICA) and the Hilbert–Huang Transform (HHT). The features of interest are the marginal spectra of different frequency bands (theta, alpha, beta and gamma bands) calculated from the Hilbert spectrum of each independent component. The Mann–Whitney U-test is then applied to rank the EEG electrode channels by relevance in five pair-wise classifications. The features from the highest ranking independent components form the final feature vector which is then used to train a linear discriminant classifier. Results show that this classifier can discriminate between the five basic primitive objects with an average accuracy of about 44.6% (compared to naïve classification rate of 20%) over ten subjects (accuracy range of 36%–54%). The accuracy classification changes to 39.9% when both visual and verbal cues are used. The repeatability of the feature extraction and classification was checked by conducting the experiment on 10 different days with the same participants. This shows that the BCI holds promise in creating geometric shapes in CAD systems and could be used as a novel means of user interaction.     

基于小波包变换的脑电波信号降噪及特征提取

针对原始脑电波信号存在非平稳性且非常容易受到各种信号干扰等特点,对基于小波变换和小波包变换的脑电波信号的滤波降噪方法,和基于小波包变换的脑电波信号特征提取方法进行了研究。首先利用MindSet采集到原始脑电波数据,然后分别应用小波变换和小波包变换对其进行降噪处理,比较了两种方法的性能,验证了基于小波包变换的降噪方法的优越性和特征提取方法的有效性。     

基于G PS 定位智能导盲耳机及导盲拐杖的设计

At present, the main auxiliary means of travel for blind people in China are guide sticks, guide dogs and related barrier-free facilities, but guide sticks and guide dogs have their own advantages and disadvantages, the level of barrier-free facilities construc- tion in various places is uneven, which has led to the travel difficulties of blind people. Starting from providing intelligent and safe way of travel for blind people, this paper researches and designs an intelligent guide headset based on GPS positioning and voice navigation, supplemented by intelligent guide crutches to detect obstacles, to provide blind people with a new travel experience, more secure and convenient service.     

虚拟现实前沿技术在青少年科普教育中的创新与实践

近年来随着计算机图形技术的发展,越来越多的虚拟现实技术被运用到科普领域,其技术核心就是使用计算机模拟虚拟的情景并通过VR设备来体验。其中效果最好、沉浸性最强的设备非VR头戴式头盔莫属。但是在青少年科普活动中,特别是6-12年龄段的青少年群体,传统的VR头戴式头盔存在着一些明显的安全隐患,就连头盔设备官网都会提示12岁以下儿童不适用于该设备。在此前提下,本文研究混合现实技术和全息投影技术在青少年科普教育中的创新和实践。     

Biosec baseline corpus: A multimodal biometric database

The baseline corpus of a new multimodal database, acquired in the framework of the FP6 EU BioSec Integrated Project, is presented. The corpus consists of fingerprint images acquired with three different sensors, frontal face images from a webcam, iris images from an iris sensor, and voice utterances acquired both with a close-talk headset and a distant webcam microphone. The BioSec baseline corpus includes real multimodal data from 200 individuals in two acquisition sessions. In this contribution, the acquisition setup and protocol are outlined, and the contents of the corpus—including data and population statistics—are described. The database will be publicly available for research purposes by mid-2006.     

基于USB总线的听力障碍筛查设备设计

在一个基于计算机的听力障碍评价系统中,为了有效驱动听力计专用耳机并提高测量的准确度,设计了基于USB总线的听力障碍筛查设备。硬件方面,来自上位机的音频数据通过USB芯片和FPGA中设计的接口逻辑送入音频DAC中,并经过三级变换电路将其转换为模拟音频功率信号。软件方面,设计了驱动程序和DSP程序,实现了音频数据的发送和接受。该设备已成功应用在听力障碍评价系统中。     

A typewriting system operated by head movements, based on home-computer equipment

For persons who cannot move their hands and legs we have designed a relatively inexpensive typewriting system which can be operated by movements of the head. The typewriter is made up of commercially available home computer equipment - i e, a computer including monitor and printer and a headset replacing the mouse. A user-friendly software program has been designed to replace the mouse button and to make this equipment act as a typewriter with simple text-editing features. Some ergonomics aspects of the typewriter set-up and the results of an evaluation of the typewriter by two patients suffering from amyotrophic lateral sclerosis (ALS) are given. Several factors relevant to the design, construction, evaluation and application of the typewriter are discussed.