Emotion recognition using wireless signals

Zhao, M.; Adib, Fadel; Katabi, Dina

doi:10.1145/3236621

Cited by 100 publications

(68 citation statements)

References 38 publications

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“…Developing better methods to combine several BCG axes could be a topic of future study. Also, increasing the total number of axes beyond three – for example, with a gyroscope to measure angular motion – or even analyzing the limb vibrations based on non-contact measurements [25], [26] could also be a topic for future study.…”

Section: Resultsmentioning

confidence: 99%

Wearable Sensing of Cardiac Timing Intervals From Cardiogenic Limb Vibration Signals

2017

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In this paper we describe a new method to measure aortic valve opening (AVO) and closing (AVC) from cardiogenic limb vibrations (i.e., wearable ballistocardiogram [BCG] signals). AVO and AVC were detected for each heartbeat with accelerometers on the upper arm (A), wrist (W), and knee (K) of 22 subjects following isometric exercise. Exercise-induced changes were recorded with impedance cardiography. The method, Filter BCG, detects peaks in distal vibrations after filtering with individually-tuned bandpass filters. In agreement with recent studies, we did not find peaks at AVO and AVC in limb vibrations directly. Interestingly, distal vibrations filtered with FilterBCG yielded reliable peaks at AVO (r2 = 0.95 A, 0.94 W, 0.77 K) and AVC (r2= 0.92 A, 0.89 W, 0.68 K). FilterBCG measures AVO and AVC accurately from arm, wrist, and knee vibrations, and it outperforms the standard R-J interval method.

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Section: Resultsmentioning

confidence: 99%

Wearable Sensing of Cardiac Timing Intervals From Cardiogenic Limb Vibration Signals

2017

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“…Advertisers can learn of customers’ reaction immediately. We believe that this will be a trend in the future [38]. In addition, owing to the range resolution and low-frequency penetration of UWB, we can also use it to extend our senses, enabling us to detect vital signs through walls or behind the closed doors.…”

Section: Resultsmentioning

confidence: 99%

Short-Range Vital Signs Sensing Based on EEMD and CWT Using IR-UWB Radar

Jin

2016

Sensors

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The radar sensor described realizes healthcare monitoring capable of detecting subject chest-wall movement caused by cardiopulmonary activities and wirelessly estimating the respiration and heartbeat rates of the subject without attaching any devices to the body. Conventional single-tone Doppler radar can only capture Doppler signatures because of a lack of bandwidth information with noncontact sensors. In contrast, we take full advantage of impulse radio ultra-wideband (IR-UWB) radar to achieve low power consumption and convenient portability, with a flexible detection range and desirable accuracy. A noise reduction method based on improved ensemble empirical mode decomposition (EEMD) and a vital sign separation method based on the continuous-wavelet transform (CWT) are proposed jointly to improve the signal-to-noise ratio (SNR) in order to acquire accurate respiration and heartbeat rates. Experimental results illustrate that respiration and heartbeat signals can be extracted accurately under different conditions. This noncontact healthcare sensor system proves the commercial feasibility and considerable accessibility of using compact IR-UWB radar for emerging biomedical applications.

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“…According to the five levels of the database, if the percentage reaches 80-100%, five points are given. This study used Microsoft's Project Oxford tool to establish expression recognition as the sub-criteria (Zhao et al 2016).…”

Section: Methodsmentioning

confidence: 99%

Development and evaluation of an emotional lexicon system for young children

Wei

2019

Microsyst Technol

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Traditionally, children's emotion has been assessed by teachers according to observation. We should be able to detect children's emotions using algorithmic techniques. To achieve this goal, it is necessary to develop and evaluate an emotional lexicon based on the standardized test entitled Emotional Competencies Scale for Young Children (ECSYC). The purpose of this study was to establish the criterion-related validity. The methodology of this study was to firstly develop 40 scenarios based on ECSYC. Secondly, we developed the five-level criteria. Thirdly, this study implemented observer training and calculated inter-rater consistency reliability. Fourthly, observers categorized 200 children's replies into five levels. Fifthly, this study ranked the sequence of frequency of each level and completed the emotional lexicon. The findings showed that the Spearman's rho coefficient reached up to .406*. (p = .026), which is significant, indicating that Young Children Emotional Lexicon (YCEL) and ECSYC were significantly correlated. The accuracies of the emotion detection recognizer using a bimodal emotion recognition approach achieved 46.7%, 60.85% and 78.73% for facial expression recognition, speech recognition, and a bimodal emotion recognition, respectively. Findings confirmed that the YCEL is feasible for speech recognition. The bimodal emotion recognition accuracies increased 32.03% and 17.88% compared with using a single modal of facial expression recognition and speech recognition, respectively. It is feasible to automatically detect children's emotional development and bring the norm up to date.

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Emotion recognition using wireless signals

Cited by 100 publications

References 38 publications

Wearable Sensing of Cardiac Timing Intervals From Cardiogenic Limb Vibration Signals

Wearable Sensing of Cardiac Timing Intervals From Cardiogenic Limb Vibration Signals

Short-Range Vital Signs Sensing Based on EEMD and CWT Using IR-UWB Radar

Development and evaluation of an emotional lexicon system for young children

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