Cardiopulmonary resuscitation (CPR) is alongside electrical defibrillation the most crucial countermeasure for sudden cardiac arrest, which affects thousands of individuals every year. In this paper, we present a novel approach including sinusoid models that use skeletal motion data from an RGB-D (Kinect) sensor and the Differential Evolution (DE) optimization algorithm to dynamically fit sinusoidal curves to derive frequency and depth parameters for cardiopulmonary resuscitation training. It is intended to be part of a robust and easy-to-use feedback system for CPR training, allowing its use for unsupervised training. The accuracy of this DE-based approach is evaluated in comparison with data of 28 participants recorded by a state-of-the-art training mannequin. We optimized the DE algorithm hyperparameters and showed that with these optimized parameters the frequency of the CPR is recognized with a median error of ±2.9 compressions per minute compared to the reference training mannequin.
Augmented Reality (AR) overlays computer-generated visual, auditory or other sensory information onto the real world. Due to recent technological advancement in the field, it can become increasingly difficult for the user to differentiate between sensory information coming from real and virtual objects, leading to interesting perceptual phenomena. For example, an AR experience in which users can experience their own hands in flames has been shown to elicit heat illusions on the affected hands. In this study, we investigate the potential that AR has for top-down modulation of pain and thermal perception. We assessed thermal pain and detection thresholds on the participant’s right hand while covering it with realistic virtual flames. We compared this experience to a baseline condition with no additional stimuli. We also report on a condition in which the hand is covered by a blue fluid not instantly associated with fire. We found that experiencing a virtual burning hand induces analgesic as well hyperalgesic effects as participants begin to feel heat related pain at lower temperatures and cold related pain at higher temperatures. The experience also impacts significantly on the lowest temperature at which participants starts perceiving warmth. The blue fluid do not affect the thresholds corresponding to the baseline condition. Our research thus confirms previous experiments showing that pain and thermal perception can be manipulated by by AR, while providing quantitative results on the magnitude of this effect.
Augmented Reality (AR) overlays computer-generated visual, auditory or other sensory information on the real world. Due to recent advancements in AR it can be difficult for the user to differentiate between sensory information coming from real and virtual objects, leading to interesting phenomena. For example, an AR experience in which users can experience their own hands in flames has been shown to elicit heat illusions on the affected hands. In this study, we investigate the potential that AR has for top-down modulation of pain and thermal perception. We assessed thermal pain and detection thresholds on the participant's right hand when they experienced that hand covered in virtual flames in AR. We compared that experience to a baseline condition with no additional stimuli as well to a control condition that sees the hand covered by unrealistic blue flames to compensate the distraction factor. We found that experiencing a burning hand in AR induced analgesic and hyperalgesic effects as participants began to feel heat related pain on lower temperatures and cold related pain on higher temperatures. That experience also significantly changed the lowest temperature at which participants started perceiving warmth. These results demonstrate that pain and thermal perception can be manipulated by altering the perception of our body in AR.
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