Silviu Borac
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SimPer: Simple Self-Supervised Learning of Periodic Targets
Yuzhe Yang
Dina Katabi
Daniel McDuff
Ming-Zher Poh
Jiang Wu
International Conference on Learning Representations (ICLR) (2023)
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From human physiology to environmental evolution, important processes in nature often exhibit meaningful and strong periodic or quasi-periodic changes. Due to their inherent label scarcity, learning useful representations for periodic tasks with limited or no supervision is of great benefit. Yet, existing self-supervised learning (SSL) methods overlook the intrinsic periodicity in data, and fail to learn representations that capture periodic or frequency attributes. In this paper, we present SimPer, a simple contrastive SSL regime for learning periodic information in data. To exploit the periodic inductive bias, SimPer introduces customized augmentations, feature similarity measures, and a generalized contrastive loss for learning efficient and robust periodic representations. Extensive experiments on common real-world tasks in human behavior analysis, environmental sensing, and healthcare domains verify the superior performance of SimPer compared to state-of-the-art SSL methods, highlighting its intriguing properties including better data efficiency, robustness to spurious correlations, and generalization to distribution shifts.
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Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms
Jim Taylor
Sean K Bae
Melissa Moran
Neal Wadhwa
Mike Krainin
Julie Cannon
Jiening Zhan
Alejandra Maciel
Shwetak Patel
Ming Po
Mehr Kashyap
Eric Steven Teasley
Mike McConnell
Allen Chai
Si-Hyuck Kang
Jonathan Wang
Liwen Chen
Jiang Wu
Yunus Emre
Nature Communications Medicine (2022)
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Background: Measuring vital signs plays a key role in both patient care and wellness, but can be challenging outside of medical settings due to the lack of specialized equipment.
Methods: In this study, we prospectively evaluated smartphone camera-based techniques for measuring heart rate (HR) and respiratory rate (RR) for consumer wellness use. HR was measured by placing the finger over the rear-facing camera, while RR was measured via a video of the participants sitting still in front of the front-facing camera.
Results: In the HR study of 95 participants (with a protocol that included both measurements at rest and post exercise), the mean absolute percent error (MAPE) ± standard deviation of the measurement was 1.6% ± 4.3%, which was significantly lower than the pre-specified goal of 5%. No significant differences in the MAPE were present across colorimeter-measured skin-tone subgroups: 1.8% ± 4.5% for very light to intermediate, 1.3% ± 3.3% for tan and brown, and 1.8% ± 4.9% for dark. In the RR study of 50 participants, the mean absolute error (MAE) was 0.78 ± 0.61 breaths/min, which was significantly lower than the pre-specified goal of 3 breaths/min. The MAE was low in both healthy participants (0.70 ± 0.67 breaths/min), and participants with chronic respiratory conditions (0.80 ± 0.60 breaths/min).
Conclusions: These results validate the accuracy of our smartphone camera-based techniques to measure HR and RR across a range of pre-defined subgroups.
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We present an algorithm for image based simplification of computer generated scenes for virtual reality (VR) applications. Our system transforms geometrically-complex environments into a layered quad tile representation renderable on low power mobile-class VR devices. A novel constrained optimization formulation ensures that the resulting tiles are renderable within a predetermined compute budget, limiting both primitive-count and fill rate. Furthermore, a new method for texturing from RGBD images generates high-quality silhouettes without the drawbacks of traditional point splatting. We demonstrate the effectiveness of this layered tile representation at rendering complex scenes with visually compelling, anti-aliased results.
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