Smarter eyewear

Ishimaru, Shoya; Kunze, Kai; Uema, Yuji; Kise, Koichi; İnami, Masahiko; Tanaka, Katsuma

doi:10.1145/2638728.2638795

Cited by 54 publications

(15 citation statements)

References 9 publications

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“…(2) For long-term sleep monitoring, the convenience of signal acquisition is a crucial factor, highly influencing usability and acceptance. Besides PSG and EOG signals can also be acquired by eye masks (Liang et al, 2015), glasses (Ishimaru et al, 2014), and even unobtrusive devices. Adjusting our method on EOG signals acquired from these devices is of great significance.…”

Section: Discussionmentioning

confidence: 99%

EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal

et al. 2021

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In recent years, automatic sleep staging methods have achieved competitive performance using electroencephalography (EEG) signals. However, the acquisition of EEG signals is cumbersome and inconvenient. Therefore, we propose a novel sleep staging approach using electrooculogram (EOG) signals, which are more convenient to acquire than the EEG. A two-scale convolutional neural network first extracts epoch-wise temporary-equivalent features from raw EOG signals. A recurrent neural network then captures the long-term sequential information. The proposed method was validated on 101 full-night sleep data from two open-access databases, the montreal archive of sleep studies and Sleep-EDF, achieving an overall accuracy of 81.2 and 76.3%, respectively. The results are comparable to those models trained with EEG signals. In addition, comparisons with six state-of-the-art methods further demonstrate the effectiveness of the proposed approach. Overall, this study provides a new avenue for sleep monitoring.

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Section: Discussionmentioning

confidence: 99%

EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal

et al. 2021

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“…3) to evaluate the feasibility of large scale sensing in the theater. One technology we evaluated was eye-tracking using smart glass-based electrooculography (EEG) [17,43,91]. We initially planned to have 60-100 participants wearing these, however test participants reported feeling distracted by wearing something on their head and also did not like to look through the frame of the devices.…”

Section: Sensingmentioning

confidence: 99%

Linking Audience Physiology to Choreography

Han

Chernyshov

Sugawa

et al. 2023

ACM Trans. Comput.-Hum. Interact.

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The use of wearable sensor technology opens up exciting avenues for both art and HCI research, providing new ways to explore the invisible link between audience and performer. To be effective, such work requires close collaboration between performers and researchers. In this paper, we report on the co-design process and research insights from our work integrating physiological sensing and live performance. We explore the connection between the audience’s physiological data and their experience during the performance, analyzing a multi-modal dataset collected from 98 audience members. We identify notable moments based on HRV and EDA, and show how the audience’s physiological responses can be linked to the choreography. The longitudinal changes in HRV features suggest a strong connection to the choreographer’s intended narrative arc, while EDA features appear to correspond with short-term audience responses to dramatic moments. We discuss the physiological phenomena and implications for designing feedback systems and interdisciplinary collaborations.

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“…In [3], the authors propose an activity recognition system that uses EOG sensors to detect eye movement and use eye movement patterns to infer the user's activities. Ishimaru et al [19] uses a commercial EOG eyeglass to detect user's activities. The methodology of this work is similar to that of [3].…”

Section: Facial Activity Detection Systemsmentioning

confidence: 99%

“…The difference is that the EOG sensor used in this work can sense activities unobtrusively. W!INCE [41] uses the same commercial EOG glasses as in [19] to sense the user's UFAs. W!INCE is able to recognize six UFAs including one neutral state by identifying the distinct patterns of the EOG readings.…”

Section: Facial Activity Detection Systemsmentioning

confidence: 99%

Acoustic-based Upper Facial Action Recognition for Smart Eyewear

Xie

Zhang

2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Smart eyewear (e.g., AR glasses) is considered to be the next big breakthrough for wearable devices. The interaction of state-of-the-art smart eyewear mostly relies on the touchpad which is obtrusive and not user-friendly. In this work, we propose a novel acoustic-based upper facial action (UFA) recognition system that serves as a hands-free interaction mechanism for smart eyewear. The proposed system is a glass-mounted acoustic sensing system with several pairs of commercial speakers and microphones to sense UFAs. There are two main challenges in designing the system. The first challenge is that the system is in a severe multipath environment and the received signal could have large attenuation due to the frequency-selective fading which will degrade the system's performance. To overcome this challenge, we design an Orthogonal Frequency Division Multiplexing (OFDM)-based channel state information (CSI) estimation scheme that is able to measure the phase changes caused by a facial action while mitigating the frequency-selective fading. The second challenge is that because the skin deformation caused by a facial action is tiny, the received signal has very small variations. Thus, it is hard to derive useful information directly from the received signal. To resolve this challenge, we apply a time-frequency analysis to derive the time-frequency domain signal from the CSI. We show that the derived time-frequency domain signal contains distinct patterns for different UFAs. Furthermore, we design a Convolutional Neural Network (CNN) to extract high-level features from the time-frequency patterns and classify the features into six UFAs, namely, cheek-raiser, brow-raiser, brow-lower, wink, blink and neutral. We evaluate the performance of our system through experiments on data collected from 26 subjects. The experimental result shows that our system can recognize the six UFAs with an average F1-score of 0.92.

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Smarter eyewear

Cited by 54 publications

References 9 publications

EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal

EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal

Linking Audience Physiology to Choreography

Acoustic-based Upper Facial Action Recognition for Smart Eyewear

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