EyePhone: A mobile EOG-based Human-Computer Interface for assistive healthcare

English, Erik; Hung, Alfredo; Kesten, Evan; Latulipe, David; Jin, Zhanpeng

doi:10.1109/ner.2013.6695882

Cited by 13 publications

(11 citation statements)

References 8 publications

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“…Particularly, because the eyes and related facial muscles are rarely affected by neuromuscular mobility impairments, many HCI systems are developed by translating electrooculography (EOG) signals generated by intended actions of these intact organs to control commands. A majority of existing EOG-HCI systems [4][5][6][7] rely on multi wet electrodes, because they can achieve a high signal-to-noise ratio (SNR) of EOG and can provide more discriminative information for recognizing more types of eye activities. As a result, characteristic structure of EOG in temporal or spatial domain can be more accurately extracted by multiple wet electrodes, and hence the systems are more capable of classifying different types of eye-movements such as looking at different directions, resulting in a higher system performance score.…”

Section: Introductionmentioning

confidence: 99%

A user-friendly wearable single-channel EOG-based human-computer interface for cursor control

Ang

Zhang

Hung

et al. 2015

2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)

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This paper presents a novel wearable single-channel electrooculography (EOG) based human-computer interface (HCI) with a simple system design and robust performance. In the proposed system, EOG signals for control are generated from double eye blinks, collected by a commercial wearable device (the NeuroSky MindWave headset), and then converted into a sequence of commands that can control cursor navigations and actions. The EOG-based cursor control system was tested on 8 subjects in indoor or outdoor environment, and the average accuracy is 84.42% for indoor uses and 71.50% for outdoor uses. Compared with other existing EOG-based HCI systems, this system is highly user-friendly and does not require any training. Therefore, this system has the potential to provide an easy-to-use and cheap assistive technique for locked-in patients who have lost their main body muscular abilities but with proper eye-condition.

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Section: Introductionmentioning

confidence: 99%

A user-friendly wearable single-channel EOG-based human-computer interface for cursor control

Ang

Zhang

Hung

et al. 2015

2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)

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“…Cellular phone based patient monitoring system (C-SMART) [32], mobile phone based fall detection system on Android platform [33], personal heart monitoring and rehabilitation system using smart phones [34], a mobile health monitoring system for elderly people (iCare) [35], MobiHealth-a vital telemonitoring and tele-treatment system based on a body area network (BAN) and mobile healthcare service platform using next generation public wireless networks [36], smartphone based body area network system (SBBANS) [37], Android based fall detection system with physiological data monitoring [38], mobile based multi-function health monitoring system [39], human activity recognition system combining accelerometer data and GPS of android-based smartphone [40], daily mood assessment using mobile phone sensing [41], a mobile EOG-based human computer interface for assistive healthcare (EyePhone) [42], smartphone-based sleep quantity measuring and modelling algorithm [43], an android-based mobile healthcare system incorporating PSoC and cloud storage [44], smartphone-based continuous monitoring system for home-bound elders and patients [45] have been found in literature which have extensively used cellular phones and smartphones for efficient, wearable and portable healthcare systems.…”

Section: Cellular and Smartphone Based E-healthcare Systemsmentioning

confidence: 99%

Mobile and E-Healthcare: Recent Trends and Future Directions

Kher¹

2016

J Health Med Econ

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In the past decade, wearable sensors and devices have evolved as key technological objects which have dramatically revolutionized the next generation healthcare solutions. This is the era of cut-throat competition filled with immense stress which leads to the detection of various diseases even in the people of early age. Moreover, people are becoming more health conscious in developed as well as developing countries. The miniaturization of sensors and devices and tendency of people to be physician-independent have lead the researchers across the globe to come up with numerous healthcare solutions which are not only ubiquitous but are affordable also. Wearable medical devices (WMDs) capable of monitoring some of the most common physiological parameters-pulse, blood oxygen saturation, 2 lead ECG, heart rhythm, skin temperature etc. have already been popular. Ambulatory and long-term health monitoring for elderly people is another aspect, particularly for countries like China and Japan where population aging is increasing. With the development of networks, collected health information can be sent to the cloud server of the nearest clinic or hospital. Doctors can then provide patients with medical advice. Thus, WMDs or WMD-based applications can help both patients and doctors in daily health monitoring. The paradigm has gradually shifted to the mobile health (mHealth) -an integration of ever advancing wireless communication, ubiquitous computing, and wearable device technologies. The mHealth can be regarded as the most advanced version of healthcare monitoring.

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“…Recently, electrooculography (EOG)-based HMI, which infers users' intention from eye movements, such as blinking and looking up/down/left/right, has emerged as a promising technology [2][3][4][5][6][7]. The advantages of EOG as a control signal for HMI include (1) EOG can be easily observed from most people who can move their eyes and the signal patterns are highly consistent and (2) eye movements are intentional and natural movements that do not introduce uncomfortable experience to users.…”

mentioning

confidence: 99%

“…Therefore, EOG-HMI has been considered as a practical alternative to some conventional EEG-based HMIs such as motor-imagery EEG-based HMI systems that cannot work for specific cohorts [8] and visual evoked potentials-based HMIs that can easily fatigue users [9]. However, most existing EOG-HMI systems acquire EOG signals from multiple (3~8) wet and often pre-wired electrodes placed around the eyes and the setup is not convenient and user-friendly [2][3][4][5]. More importantly, although signal processing algorithms, such as wavelet filtering and support vector machines (SVM), are useful for removing noise and improving recognition accuracy of EOG [6,7], they are usually hardware-and power-intensive in practice.…”

mentioning

confidence: 99%

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Efficient Implementation and Design of a New Single-Channel Electrooculography-Based Human–Machine Interface System

Ang

Tsui

et al. 2015

IEEE Trans. Circuits Syst. II

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This brief introduces a new and practical human-machine interface (HMI) system based on single-channel electrooculography (EOG) signals. The proposed system uses a consumer wireless recording device to collect EOG and employs new encoding/decoding paradigms to covey users' intentions with EOG from eye movements including blinking and looking up. The simplicity and mobility of the system provides a comfortable and practical solution to HMI. Furthermore, to reduce the hardware complexity and power consumption of the signal processing modules of the EOG-based HMI system, a novel multiplier-less implementation is developed, where all the algorithms involved, such as bandpass filtering, wavelet filtering, and support vector machine, can be realized using limited number of adders and shifters only. Experimental results show that the proposed system offers a simple, practical and yet reliable EOG-based HMI with low complexity and power consumption.

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EyePhone: A mobile EOG-based Human-Computer Interface for assistive healthcare

Cited by 13 publications

References 8 publications

A user-friendly wearable single-channel EOG-based human-computer interface for cursor control

A user-friendly wearable single-channel EOG-based human-computer interface for cursor control

Mobile and E-Healthcare: Recent Trends and Future Directions

Efficient Implementation and Design of a New Single-Channel Electrooculography-Based Human–Machine Interface System

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