Using wireless signals to monitor human vital signs, especially heartbeat information, has been intensively studied in the past decade. This non-contact sensing modality can drive various applications from cardiac health, sleep, and emotion management. Under the circumstance of the COVID-19 pandemic, non-contact heart monitoring receives increasingly market demands. However, existing wireless heart monitoring schemes can only detect limited heart activities, such as heart rate, fiducial points, and Seismocardiography (SCG)-like information. In this paper, we present CardiacWave to enable a non-contact high-definition heart monitoring. CardiacWave can provide a full spectrum of Electrocardiogram (ECG)-like heart activities, including the details of P-wave, T-wave, and QRS complex. Specifically, CardiacWave is built upon the Cardiac-mmWave scattering effect (CaSE), which is a variable frequency response of the cardiac electromagnetic field under the mmWave interrogation. The CardiacWave design consists of a noise-resistant sensing scheme to interrogate CaSE and a cardiac activity profiling module for extracting cardiac electrical activities from the interrogation response. Our experiments show that the CardiacWave-induced ECG measures have a high positive correlation with the heart activity ground truth (i.e., measurements from a medical-grade instrument). The timing difference of P-waves, T-waves, and QRS complex is 0.67%, 0.71%, and 0.49%, respectively, and a mean cardiac event difference is within a delay of 5.3 milliseconds. These results indicate that CaridacWave offers high-fidelity and integral heart clinical characteristics. Furthermore, we evaluate the CardiacWave system with participants under various conditions, including heart and breath rates, ages, and heart habits (e.g., tobacco use).
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Effects of Environmental Noise Levels on Patient Handoff Communication in a Mixed Reality Simulation
When medical caregivers transfer patients to another person’s care (a patient handoff), it is essential they effectively communicate the patient’s condition to ensure the best possible health outcomes. Emergency situations caused by mass casualty events (e.g., natural disasters) introduce additional difficulties to handoff procedures such as environmental noise. We created a projected mixed reality simulation of a handoff scenario involving a medical evacuation by air and tested how low, medium, and high levels of helicopter noise affected participants’ handoff experience, handoff performance, and behaviors. Through a human-subjects experimental design study (N = 21), we found that the addition of noise increased participants’ subjective stress and task load, decreased their self-assessed and actual performance, and caused participants to speak louder. Participants also stood closer to the virtual human sending the handoff information when listening to the handoff than they stood to the receiver when relaying the handoff information. We discuss implications for the design of handoff training simulations and avenues for future handoff communication research.
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- Award ID(s):
- 1800961
- NSF-PAR ID:
- 10442469
- Date Published:
- Journal Name:
- 28th ACM Symposium on Virtual Reality Software and Technology
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3562939.3565627
