• Optical-based sensor prototype for continuous monitoring of the blood pressure

      Cohen, Zachary Joel Valentino; Haxha, Shyqyri; University of Bedfordshire (IEEE Sensors Journal, 2017-07-01)
      In this paper, we report a prototype ring sensor device for continuous measurement of blood pressure with the use of our, previously developed, heart rate monitoring ring device. An experiment is described where the heart rate device provides the voltage output of the heart using the transmission photoplethysmography (PPG) method and predicts the blood pressure’s value to ±5% of its true value. We report a novel potential non-invasive, low cost, continuous heart rate and blood pressure monitoring device that uses transmission PPG instead of the traditional cuff method to observe the changes in volume of the pressure through the arteries of the finger. The continuous samples are averaged out constantly. We employed the PPG technique to optically determine the blood volume changes in the arteries of the finger. A Pearson’s product moment correlation coefficient proved an r value of 0.86 showing strong linear correlation between the average voltage of the heart rate and the corresponding blood pressure. The proposed blood pressure ring sensor device was tested and benchmarked (against Nonin 2120 benchmark blood pressure device) four participants for a continuous period of four hours, where the average Mean Arterial Pressure (MAP) (using Nonin 2120) for four hours was at 98.92mmHg and the average predicted MAP was at 92.8mmHg, which demonstrates an accuracy of 93.8%.The average real systolic pressure (using Nonin 2120) was at 144.25mmHg and the predicted average systolic pressure was at 132.77mmHg which shows an accuracy of 92%. The average real diastolic pressure (using Nonin 2120) was at 76.25mmHg and the predicted diastolic pressure was 72.7mmHg, showing an accuracy of 95.5%. 
    • Passive localization through light flicker fingerprinting

      Munir, Bilal; Dyo, Vladimir (IEEE, 2019-08-22)
      In this paper, we show that the flicker waveforms of various CFL and LED lamp models exhibit distinctive waveform patterns due to harmonic distortions of rectifiers and voltage regulators, the key components of modern lamp drivers. We then propose a passive localization technique based on fingerprinting these distortions that occur naturally in indoor environments and thus requires no infrastructure or additional equipment. The novel technique uses principal component analysis (PCA) to extract the most important signal features from the flicker frequency spectra followed by kNN clustering and neural net- work classifiers to identify a light source based on its flicker signature. The evaluation on 39 flicker patterns collected from 8 residential locations demonstrates that the technique can identify a location within a house with up to 90% accuracy and identify an individual house from a set of houses with an average accuracy of 86.3%.