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(searched for: doi:10.1109/bibm47256.2019.8983009)
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Amtul Haq Ayesha, Donghao Qiao, Farhana Zulkernine
Abstract:
Methods of estimating heart rate without the use of sensor devices provides essential benefits in both the medical field as well as the other computing applications. Smartphones are the handiest devices available to everyone today. By using videos of fingertip captured with smartphone camera, heart rate (HR) can be estimated using the photoplethysmography (PPG) technique. It is based on tracking subtle color changes on the skin owing to cardiovascular activities. These color changes are invisible to the human eye but can be detected by digital cameras. The method is divided into three main steps: first, reading the video frames and processing them to obtain the PPG data, next, extracting the Blood Volume Pulse (BVP) signal, and finally, estimating the HR from the signal. In this project, the color intensity of the skin pixels is used, and filters are applied to eliminate the noise and retain only the pulses of interest. The extracted signal is fed into a convolutional regression neural network which outputs the estimated HR. The results obtained are compared with the ground truth HR obtained by using a contact PPG sensor. We obtained a Mean Absolute Error (MAE) of 7.01 beats per minute (bpm) and an error percentage of 8.3% on test data.
Sudarshan S. Chawathe
Abstract:
This paper presents methods that use data from wearable sensors, such as those found in low-cost commodity hardware, to infer the human activity (such as reading or walking) corresponding to the sensor readings. A related task is the identification of individuals based on the same data. The classification accuracy of the methods used in this work is higher than earlier work using the same dataset. Further, a significant reduction in the number of sensor data streams produces only a very small impact on this accuracy, which is a feature of practical significance due to implications for network bandwidth and energy budgets in such systems.
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