It was also recently presented at the highly selective international conference, ACM UbiComp 2022, by Aakriti Adhikari. The findings are reported recently in the ACM Journal on Interactive, Mobile, Wearable and Ubiquitous Technologies ( IMWUT) in a paper co-authored by UofSC graduate students, Aakriti Adhikari and Hem Regmi, and UofSC faculties of computer science and engineering department, Dr. By processing mmWave signals and combining them with custom-designed conditional Generative Adversarial Networks (GAN) model, they demonstrated that MiShape generates high-resolution silhouettes and accurate poses of human body on par with existing vision-based systems. This system provides an advantage over camera-based solutions since it works even under no light conditions and preserves users’ privacy.
Now a team of researchers from the Systems Research on X laboratory at the University of South Carolina has designed a monitoring system, called MiShape, based on millimeter-wave (mmWave) wireless technology in 5G-and-beyond devices to track humans beyond-line-of-sight, see through obstructions, and monitor gait, posture, and sedentary behaviors. More importantly, cameras impose a major privacy concern and are often undesirable for users to install inside their homes. So, they do not perform well in occlusion, low light, and dark conditions.
Traditionally, optical cameras, IRs, LiDARs, etc., have been used to build such applications, but they depend on light or thermal energy radiating from the human body. The need for understanding and perceiving at-home human activities and biomarkers is critical in numerous applications, such as monitoring the behavior of elderly patients in assisted living conditions, detecting falls, tracking the progression of degenerative diseases, such as Parkinson’s, or monitoring recovery of patients’ during post-surgery or post-stroke.
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