Heart Rate (HR) is a fundamental vital sign, monitoring which provides essential information for automated healthcare systems. The emerging technology of Photoplethysmograph (PPG) is shown as a feasible candidate for such applications; however, Motion Artifacts (MA) hinder efficient HR estimation using PPG, especially in situations involving physical activities. It is previously shown that even in the presence of sever MA, HR is still traceable with the help of simultaneous acceleration data although at high computational expenses. In this paper, we propose a novel framework, that not only improves the accuracy in HR estimation, but also achieves realtime performance by significantly reducing the complexity of system; mainly due to alleviation of the need for computationally demanding MA cancellation methods. Utilizing an spectrum estimation model (autoregressive) that suits well to the inherent PPG generation process, and benefiting from further intrinsic properties of the environment (e.g., the venous pulsation phenomenon); our framework achieves realtime and delayed (post-processed) Average Absolute Errors (AAE) of 1.19 and 0.99 Beats Per Minute (BPM) respectively, on the 12 benchmark recordings in which subjects run at speeds of up to 15 km/h maximum. Moreover, the system makes standalone implementation feasible by processing input frames (2 channel PPG and 3D ACC) in < 0.004 times of the frame duration, operating on a 3.2 GHz processor. This study provides wearable healthcare technologies with a robust framework for accurate HR monitoring; at considerably low computational costs.

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PPG based heart rate (HR) monitoring has recently attracted much attention with the advent of wearable devices such as smart watches and smart bands. However, due to severe motion artifacts (MA) caused by wristband stumbles, PPG based HR monitoring is a challenging problem in scenarios where the subject performs intensive physical exercises. This work proposes a novel approach to the problem based on supervised learning by Neural Network (NN). By simulations on the benchmark datasets [1], we achieve acceptable estimation accuracy and improved run time in comparison with the literature. A major contribution of this work is that it alleviates the need to use simultaneous acceleration signals. The simulation results show that although the proposed method does not process the simultaneous acceleration signals, it still achieves the acceptable Mean Absolute Error (MAE) of 1.39 Beats Per Minute (BPM) on the benchmark data set.

<p>Dental disease is largely preventable and closely linked to poor toothbrushing behaviors. Motion-sensors, such as accelerometers, gyroscopes, and magnetometers, allow for mon- itoring of toothbrushing behaviors. Researchers have attempted to infer tooth surface coverage using sensors attached to the toothbrush handle or embedded in smartwatches. However, the inferences may be deficient because the datasets were collected under structured toothbrushing assumptions performed in con- trolled laboratory settings and not the free-form and irregular brushing patterns observed in real-world settings. To address the aforementioned problem, we collected a dataset of 187 brush- ing sessions, including free-form brushing. We present, to our knowledge, the first motion-sensor dataset obtained during free- form brushing. Using our experiences, we discuss the challenges of studying toothbrushing behaviors in naturalistic settings. We also propose a three-stage method (i.e. pre-processing, brush transition time detection, and time-series classification) to detect the teeth surfaces brushed during a session. Our findings are two-fold: (a) the classification of teeth surfaces during free- form toothbrushing is more challenging than during brushing in controlled settings; (b) high classification accuracy can be achieved using random train-test split of the data (i.e. k-fold cross-validation); however, generalization beyond the participants in the training set poses difficulties. Beyond publishing the first dataset of free-form toothbrushing, we validate our findings by applying our proposed method to our provided dataset, as well as the datasets of toothbrushing in controlled settings. </p>