Silviu Borac
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SimPer: Simple Self-Supervised Learning of Periodic Targets
Yuzhe Yang
Jiang Wu
Dina Katabi
Ming-Zher Poh
Daniel McDuff
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
Sean K Bae
Yunus Emre
Jonathan Wang
Jiang Wu
Mehr Kashyap
Si-Hyuck Kang
Liwen Chen
Melissa Moran
Julie Cannon
Eric Steven Teasley
Allen Chai
Neal Wadhwa
Mike Krainin
Alejandra Maciel
Mike McConnell
Shwetak Patel
Jim Taylor
Jiening Zhan
Ming Po
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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