A Wearable Device for Fast and Subtle Spontaneous Smile Recognition

Perusquía-Hernández, Monica; Hirokawa, Masakazu; Suzuki, Kenji

doi:10.1109/taffc.2017.2755040

Cited by 37 publications

(28 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…); while our proposed method only uses the forehead region and is unobtrusive. EMG placed at the temples has shown to detect a smile with around 90% accuracy in [60]. EEG has also been studied for emotion detection with above 90% accuracy among four emotions in [61].…”

Section: Conclusion Discussion and Future Outlookmentioning

confidence: 99%

Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure Mechanomyography

Zhou

Ghose

Lukowicz

2020

Sensors

View full text Add to dashboard Cite

We investigate how pressure-sensitive smart textiles, in the form of a headband, can detect changes in facial expressions that are indicative of emotions and cognitive activities. Specifically, we present the Expressure system that performs surface pressure mechanomyography on the forehead using an array of textile pressure sensors that is not dependent on specific placement or attachment to the skin. Our approach is evaluated in systematic psychological experiments. First, through a mimicking expression experiment with 20 participants, we demonstrate the system’s ability to detect well-defined facial expressions. We achieved accuracies of 0.824 to classify among three eyebrow movements (0.333 chance-level) and 0.381 among seven full-face expressions (0.143 chance-level). A second experiment was conducted with 20 participants to induce cognitive loads with N-back tasks. Statistical analysis has shown significant correlations between the Expressure features on a fine time granularity and the cognitive activity. The results have also shown significant correlations between the Expressure features and the N-back score. From the 10 most facially expressive participants, our approach can predict whether the N-back score is above or below the average with 0.767 accuracy.

show abstract

Section: Conclusion Discussion and Future Outlookmentioning

confidence: 99%

Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure Mechanomyography

Zhou

Ghose

Lukowicz

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…An alternative is a surface-EMG-based detection method, which in comparison, is advantageous over a CV-based detection method due to better temporal resolution and robustness against occlusion and light changes [13]. Electrodes might be placed in specific locations, and amplitude analysis is then applied to detect activation of specific facial muscles that correspond to microexpressions of interest [19,20].…”

Section: Facial Microexpressions and Their Automatic Detectionmentioning

confidence: 99%

“…Electrodes might be placed in specific locations, and amplitude analysis is then applied to detect activation of specific facial muscles that correspond to microexpressions of interest [19,20]. Recently in [13,21], Artificial Neural Network (ANN) was adopted to classify microexpressions directly from preprocessed EMG signals.…”

Section: Facial Microexpressions and Their Automatic Detectionmentioning

confidence: 99%

“…In this paper, in order to investigate potential of shadowers' facial microexpressions for comprehensibility prediction, four native Japanese were asked to shadow Japanese utterances spoken by Vietnamese learners. Here, native shadowers' microexpressions were detected via two methods: from videos of shadowers' face, and from surface electromyography (EMG) signals recorded by a wearable device [13]. In the former, Facial Action Coding System (FACS) is used to analyze recorded face videos, and in the latter, amplitude analysis is adopted, where RMS values of the EMG envelope are calculated.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Native Listeners’ Facial Microexpressions While Shadowing Non-Native Speech — Potential of Shadowers’ Facial Expressions for Comprehensibility Prediction

Trisitichoke

Ando²,

Saito

et al. 2019

Interspeech 2019

View full text Add to dashboard Cite

Recently, researchers' attention has been paid to pronunciation assessment not based on comparison between L2 speech and native models, but based on comprehensibility of L2 speech [1, 2, 3]. In our previous studies [4, 5, 6], native listeners' shadowing of L2 speech was examined and it was shown that delay of shadowing and accuracy of articulation in shadowing utterances, both of which were acoustically calculated, are strongly influenced by the amount of cognitive load imposed for understanding L2 speech, especially when it is with strong accents. In this paper, aside from acoustic analysis of shadowings, we focus on shadowers' facial microexpressions and examine how they are correlated with perceived comprehensibility. To extract facial expression features, two methods are tested. One is a computer-vision-based method and recorded videos of shadowers' facial expressions are analyzed. The other is a method using a physiological sensor that can detect subtle movements of facial muscles. In experiments, four shadowers' facial expressions are analyzed, each of whom shadowed approximately 800 L2 utterances. Results show that some of shadowers' facial expressions are highly correlated with perceived comprehensibility, and that those facial expressions are strongly shadowerdependent. These results indicate a high potential of shadowers' facial expressions for comprehensibility prediction.

show abstract

“…With the continuous development of Internet of Things (IoT), various scientific and technological elements are quietly entering every corner of our lives. The mature development of wearable device technology will be a great opportunity and breakthrough for the IoT. Recently, wearable technology has attracted much attention.…”

Section: Introductionmentioning

confidence: 99%

SEWMS: An edge‐based smart wearable maintenance system in communication network

Rui

Qin

et al. 2019

Softw Pract Exp

View full text Add to dashboard Cite

Summary The development of the Internet of Things (IoT) and wearable technology provides an opportunity for the development of maintenance of communication. The use of wearable technology and instant messaging technology of IoT can improve the support capabilities and data interaction ability of on‐site maintenance of the communication network. Existing communication maintenance systems lack real‐time operation and maintenance of data interaction. In the field operation decision‐making and execution process, there are problems of lack of field links and inconvenient information interaction. On‐site maintenance mainly relies on maintenance personnel to actively search for information, and the retrieval results are lacking of personalization, which makes it difficult to meet the needs of on‐site maintenance of the communication network. Edge computing and information push technology can solve these problems to some extent. In this paper, we focus on the current low level in information, complicated scenes, and various information of on‐site maintenance and propose a dynamic context‐aware information push algorithm. The simulation results demonstrate that the algorithm delivers good performance in terms of precision, recall, and F1. Besides, we present a smart wearable maintenance system, an edge computing–assisted IoT platform for the real‐time guidance of technical experts and systems for on‐site maintenance personnel, aiming to improve the efficiency and quality of on‐site maintenance.

show abstract

A Wearable Device for Fast and Subtle Spontaneous Smile Recognition

Cited by 37 publications

References 48 publications

Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure Mechanomyography

Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure Mechanomyography

Analysis of Native Listeners’ Facial Microexpressions While Shadowing Non-Native Speech — Potential of Shadowers’ Facial Expressions for Comprehensibility Prediction

SEWMS: An edge‐based smart wearable maintenance system in communication network

Contact Info

Product

Resources

About