Investigation of Material Properties for Thermal Imaging-Based Interaction

Abdelrahman, Yomna; Shirazi, Alireza Sahami; Henze, Niels; Schmidt, Albrecht

doi:10.1145/2702123.2702290

Cited by 29 publications

(19 citation statements)

References 13 publications

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“…However, the evolution of commercial, relatively cheap thermal cameras operating in the FIR band allowed the evolution of a diverse new set of applications. In a recent survey paper, Gade and Moeslund provided an overview of current applications of thermal cameras, highlighting the potential of using thermal cameras in the HCI domain [15] for instance, for physiological motoring [24], vision extension [3,4], gestural interaction [1,5,33,50] and touch points detection [2].…”

Section: Thermal Imagingmentioning

confidence: 99%

Cognitive Heat

Abdelrahman

Velloso

Dingler

et al. 2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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127

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Current digital systems are largely blind to users' cognitive states. Systems that adapt to users' states show great potential for augmenting cognition and for creating novel user experiences. However, most approaches for sensing cognitive states, and cognitive load specifically, involve obtrusive technologies, such as physiological sensors attached to users' bodies. This paper present an unobtrusive indicator of the users' cognitive load based on thermal imaging that is applicable in real-world. We use a commercial thermal camera to monitor a person's forehead and nose temperature changes to estimate their cognitive load. To assess the effect of different levels of cognitive load on facial temperature we conducted a user study with 12 participants. The study showed that different levels of the Stroop test and the complexity of reading texts affect facial temperature patterns, thereby giving a measure of cognitive load. To validate the feasibility for real-time assessments of cognitive load, we conducted a second study with 24 participants, we analyzed the temporal latency of temperature changes. Our system detected temperature changes with an average latency of 0.7 seconds after users were exposed to a stimulus, outperforming latency in related work that used other thermal imaging techniques. We provide empirical evidence showing how to unobtrusively detect changes in cognitive load in real-time. Our exploration of exposing users to different content types gives rise to thermal-based activity tracking, which facilitates new applications in the field of cognition-aware computing. CCS Concepts: • Human-centered computing → Human computer interaction (HCI); • Computing methodologies → Cognitive science; • Hardware → Displays and imagers;

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Section: Thermal Imagingmentioning

confidence: 99%

Cognitive Heat

Abdelrahman

Velloso

Dingler

et al. 2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

Self Cite

127

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“…For example, Benko et al described a simplified strategy for how touches to a spherical display could be detected; normalizing was conducted to compensate for different intensities at different heights along the sphere, followed by thresholding and finding contours (Benko et al, 2008). Furthermore, some properties of heat traces on different materials-glass, tile, MDF, aluminum-were investigated by Abdelrahman et al (2015). The paper most similar to the current article could be argued to be one by Larson and colleagues, which proposed various mechanisms and explored various properties of thermal sensing, also for a user interface application (Larson et al, 2011).…”

Section: Related Workmentioning

confidence: 85%

PastVision+: Thermovisual Inference of Recent Medicine Intake by Detecting Heated Objects and Cooled Lips

Cooney

Bigün

2017

Front. Robot. AI

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This article addresses the problem of how a robot can infer what a person has done recently, with a focus on checking oral medicine intake in dementia patients. We present PastVision+, an approach showing how thermovisual cues in objects and humans can be leveraged to infer recent unobserved human-object interactions. Our expectation is that this approach can provide enhanced speed and robustness compared to existing methods, because our approach can draw inferences from single images without needing to wait to observe ongoing actions and can deal with short-lasting occlusions; when combined, we expect a potential improvement in accuracy due to the extra information from knowing what a person has recently done. To evaluate our approach, we obtained some data in which an experimenter touched medicine packages and a glass of water to simulate intake of oral medicine, for a challenging scenario in which some touches were conducted in front of a warm background. Results were promising, with a detection accuracy of touched objects of 50% at the 15 s mark and 0% at the 60 s mark, and a detection accuracy of cooled lips of about 100 and 60% at the 15 s mark for cold and tepid water, respectively. Furthermore, we conducted a follow-up check for another challenging scenario in which some participants pretended to take medicine or otherwise touched a medicine package: accuracies of inferring object touches, mouth touches, and actions were 72.2, 80.3, and 58.3% initially, and 50.0, 81.7, and 50.0% at the 15 s mark, with a rate of 89.0% for person identification. The results suggested some areas in which further improvements would be possible, toward facilitating robot inference of human actions, in the context of medicine intake monitoring.

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“…Hence, enabling an extension of how we perceive, visualize and interact with the surroundings. As well sa novel HCI research [1,3,5,18] Thermal imaging provides a heat map for the scene to the user in a contact-less and in real time, as it operates in a robust manner (e.g., light independent). Moreover, infrared imaging (including both near-and far-Infrared) enhances and extends the perception of our visual sensing.…”

Section: Introductionmentioning

confidence: 99%