Inside-out: Reflecting on your inner state

Hernández, Javier; McDuff, Daniel; Fletcher, Roger; Picard, Rosalind W.

doi:10.1109/percomw.2013.6529507

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…The visual context in these cases, therefore, plays a critical role to interpret the physiological signals. Furthermore, linking physiological information with visual imagery can be useful in a wide variety of applications such as catalyzing introspection [15], augmenting human memory [17], and improving social communication [23].…”

Section: Discussionmentioning

confidence: 99%

Cardiac and Respiratory Parameter Estimation Using Head-mounted Motion-sensitive Sensors

Hernández¹,

Li²,

Rehg

et al. 2015

EAI Endorsed Transactions on Pervasive Health and Technology

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This work explores the feasibility of using motion-sensitive sensors embedded in Google Glass, a head-mounted wearable device, to robustly measure physiological signals of the wearer. In particular, we develop new methods to use Glass's accelerometer, gyroscope, and camera to extract pulse and respiratory waves of 12 participants during a controlled experiment. We show it is possible to achieve a mean absolute error of 0.82 beats per minute (STD: 1.98) for heart rate and 0.6 breaths per minute (STD: 1.19) for respiration rate when considering different observation windows and combinations of sensors. Moreover, we show that a head-mounted gyroscope sensor shows improved performance versus more commonly explored sensors such as accelerometers and demonstrate that a head-mounted camera is a novel and promising method to capture the physiological responses of the wearer. These findings included testing across sitting, supine, and standing postures before and after physical exercise.

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Section: Discussionmentioning

confidence: 99%

Cardiac and Respiratory Parameter Estimation Using Head-mounted Motion-sensitive Sensors

Hernández¹,

Li²,

Rehg

et al. 2015

EAI Endorsed Transactions on Pervasive Health and Technology

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“…Similar to VER, researchers over the years have also used physiological features such as HR, RP, EEG, blood oxygen saturation (BOS), GSR, and SKT for emotion recognition [38,[74][75][76].…”

Section: Historical Evolution and Current Trendsmentioning

confidence: 99%

A Survey on Databases for Multimodal Emotion Recognition and an Introduction to the VIRI (Visible and InfraRed Image) Database

Siddiqui

Dhakal

Yang

et al. 2022

MTI

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Multimodal human–computer interaction (HCI) systems pledge a more human–human-like interaction between machines and humans. Their prowess in emanating an unambiguous information exchange between the two makes these systems more reliable, efficient, less error prone, and capable of solving complex tasks. Emotion recognition is a realm of HCI that follows multimodality to achieve accurate and natural results. The prodigious use of affective identification in e-learning, marketing, security, health sciences, etc., has increased demand for high-precision emotion recognition systems. Machine learning (ML) is getting its feet wet to ameliorate the process by tweaking the architectures or wielding high-quality databases (DB). This paper presents a survey of such DBs that are being used to develop multimodal emotion recognition (MER) systems. The survey illustrates the DBs that contain multi-channel data, such as facial expressions, speech, physiological signals, body movements, gestures, and lexical features. Few unimodal DBs are also discussed that work in conjunction with other DBs for affect recognition. Further, VIRI, a new DB of visible and infrared (IR) images of subjects expressing five emotions in an uncontrolled, real-world environment, is presented. A rationale for the superiority of the presented corpus over the existing ones is instituted.

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“…The device is designed to be worn like a pair of glasses, enabling gathering personal information of the wearer during daily activity. In a recent exploration [3], we have shown that motion sensitive sensors such as the accelerometer embedded in Glass can accurately capture subtle head motions due to the beating of the heart and respiration of the person, enabling comfortable measurement during sedentary activities in an office.…”

Section: Affect Sensingmentioning

confidence: 99%

SenseGlass

Hernández

Picard

2014

Proceedings of the Adjunct Publication of the 27th Annual ACM Symposium on User Interface Software and Technology - UIST'14 Adj

Self Cite

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For over a century, scientists have studied human emotions in laboratory settings. However, these emotions have been largely contrived -elicited by movies or fake "lab" stimuli, which tend not to matter to the participants in the studies, at least not compared with events in their real life. This work explores the utility of Google Glass, a head-mounted wearable device, to enable fundamental advances in the creation of affect-based user interfaces in natural settings.

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Inside-out: Reflecting on your inner state

Cited by 10 publications

References 15 publications

Cardiac and Respiratory Parameter Estimation Using Head-mounted Motion-sensitive Sensors

Cardiac and Respiratory Parameter Estimation Using Head-mounted Motion-sensitive Sensors

A Survey on Databases for Multimodal Emotion Recognition and an Introduction to the VIRI (Visible and InfraRed Image) Database

SenseGlass

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