Browsing by Author "Maity, Akash Kumar"

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    A Robust Algorithm for Identification of Motion Artifacts in Photoplethysmography Signals
    (2018-12-03) Maity, Akash Kumar; Sabharwal, Ashutosh; Veeraraghavan, Ashok; Heckel, Reinhard
    Photoplethysmography(PPG) is commonly used as a means of continuous health monitoring. Many clinically relevant parameters like heart rate (HR), blood oxygenaton level (SPO2) are derived from the sensor measurements using PPG. Presence of motion artifacts in the signal decreases the accuracy of estimating the parameters and therefore reduces the reliabilty of these sensor devices. Motion artifacts can be both periodic or aperiodic. Existing state-of-the-art methods for motion detection rely on the semi-periodic structure of PPG to distinguish from aperiodic motion artifacts. Periodic motion artifacts that can be introduced by perioidic movements like hand tapping, jogging, cannot be detected by current methods reliably. In this thesis, we propose a novel technique, PPGMotion, for identifying all motion artifacts in PPG signals. PPGMotion relies on the morphological structure of artifact-free PPG signal, which has a fast systolic phase and a slowly decaying diastolic phase. We note that in the presence of motion artifacts, the recorded PPG signals do not exhibit the characteristic PPG shape. Our approach uses this prior information about the PPG morphology to reliable detect periodic motion artifacts, without the need of any additional hardware components like an accelerometer. To evaluate the proposed method, we adopt both a simulation and real data collection. For simulation-based iii analysis, we use a generative model for motion artifacts to simulate different cases of motion artifacts. For real data, we have compared our approach against recent works on motion identification using 3 datasets, where we record the PPG from a pulse-oximeter attached to a finger with subjects making (1) random finger movements, (2) periodic movements like periodic finger tapping and (3) PPG recordings from Maxim smartwatch with subjects running on a treadmill. Dataset (2) and (3) are expected to introduce periodic motion artifacts in the measured PPG signals. We demonstrate that while our approach is similar in performance to previous methods when random motion artifacts are introduced, the performance is significantly better in the presence of periodic motion artifacts. We show that for simulated dataset, the performance of PPGMotion is significantly better than existing work as the contaminated PPG tends to become periodic, with an increase in sensitivity of atleast 10% over state-of-the-art method. For real data, PPGMotion is successful in identifying the periodic motion artifacts, with mean sensitivity of 95% and accuracy of 95.8%, compared to the state-of-the-art method with mean sensitivity of 66% and accuracy of 89% for dataset (2). For dataset (1), PPGMotion achieves an accuracy of 96.35% with sensitivity of 95.29%, and for dataset (3), PPGMotion achieves an accuracy of 91.89% and sensitivity of 93.03%, compared to the second best method with accuracy 81.23% and sensitivity 74.99%.
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    Camera-based Tissue Hemodynamics Imaging
    (2023-04-21) Maity, Akash Kumar; Sabharwal, Ashutosh; Veeraraghavan, Ashok
    Blood flow changes within the human tissue have two main characteristics- i) the temporal pulsatile variation caused by the regular heart beat, and ii) the spatial variation that captures the presence of blood vessels beneath the skin surface. Hemodynamics, which describes blood flow throughout the body, is important to monitor for many medical conditions. The use of visible and near-infrared light for deep tissue hemodynamics imaging is emerging as a low-cost and safe alternative to some of the existing state-of-the-art technologies. However, the best camera-based systems suffer from a poor signal-to-noise ratio and low signal contrast regime due to light interaction in the tissue. Hence, accurate estimation using light-based imaging remains an open problem. In this thesis, we develop two camera-based systems to estimate two dimensions of tissue hemodynamics: i) heart rate as a measure of temporal variation, and ii) deep tissue perfusion to map spatial variation across the tissue. In the first part, we present RobustPPG, a camera-based, motion-robust imaging technique for estimating heart rate accurately from human face videos under normal ambient illumination. We explicitly model and generate motion distortions due to the movements of the person's face using inverse rendering. The generated motion distortion is then used to filter the motion-induced measurements. We demonstrate that our approach performs better than the state-of-the-art methods in extracting a clean blood volume signal with over $2$ dB signal quality improvement and $30\%$ improvement in RMSE of estimated heart rate in intense motion scenarios. In the second part of our thesis, we present SpeckleCam, a camera-based system to recover deep tissue blood perfusion in high resolution. We use a line scanning system and a fast algorithm for recovering high-resolution blood flow deep inside the tissue. Our approach replaces the traditional matrix-multiplication form with a convolution-based forward model that enables us to develop an efficient and fast blood flow reconstruction algorithm, with $10\times$ speedup in runtime compared to the prior methods. We show that our proposed approach can recover complex structures $6$ mm deep inside a tissue-like scattering medium in the reflection geometry. We also demonstrate the sensitivity of our approach to real tissue to detect reduced flow in major blood vessels during arm occlusion.
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    RobustPPG: camera-based robust heart rate estimation using motion cancellation
    (Optica Publishing Group, 2022) Maity, Akash Kumar; Maity, Akash Kumar; Wang, Jian; Wang, Jian; Wang, Jian; Sabharwal, Ashutosh; Sabharwal, Ashutosh; Nayar, Shree K.; Nayar, Shree K.
    Camera-based heart rate measurement is becoming an attractive option as a non-contact modality for continuous remote health and engagement monitoring. However, reliable heart rate extraction from camera-based measurement is challenging in realistic scenarios, especially when the subject is moving. In this work, we develop a motion-robust algorithm, labeled RobustPPG, for extracting photoplethysmography signals (PPG) from face video and estimating the heart rate. Our key innovation is to explicitly model and generate motion distortions due to the movements of the person’s face. We use inverse rendering to obtain the 3D shape and albedo of the face and environment lighting from video frames and then render the human face for each frame. The rendered face is similar to the original face but does not contain the heart rate signal; facial movements alone cause pixel intensity variation in the generated video frames. Finally, we use the generated motion distortion to filter the motion-induced measurements. We demonstrate that our approach performs better than the state-of-the-art methods in extracting a clean blood volume signal with over 2 dB signal quality improvement and 30% improvement in RMSE of estimated heart rate in intense motion scenarios.