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- 저자 : Alexander J. Casson (검색결과 2 건)
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Alexander J. Casson 대한의용생체공학회 2019 Biomedical Engineering Letters (BMEL) Vol.9 No.1
The electroencephalogram (EEG) is a widely used non-invasive method for monitoring the brain. It is based upon placingconductive electrodes on the scalp which measure the small electrical potentials that arise outside of the head due to neuronalaction within the brain. Historically this has been a large and bulky technology, restricted to the monitoring of subjects in alab or clinic while they are stationary. Over the last decade much research eff ort has been put into the creation of “wearableEEG” which overcomes these limitations and allows the long term non-invasive recording of brain signals while peopleare out of the lab and moving about. This paper reviews the recent progress in this fi eld, with particular emphasis on theelectrodes used to make connections to the head and the physical EEG hardware. The emergence of conformal “tattoo” typeEEG electrodes is highlighted as a key next step for giving very small and socially discrete units. In addition, new recommendationsfor the performance validation of novel electrode technologies are given, with standards in this area seen as thecurrent main bottleneck to the wider take up of wearable EEG. The paper concludes by considering the next steps in thecreation of next generation wearable EEG units, showing that a wide range of research avenues are present.
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Gyroscope vs. accelerometer measurements of motion from wrist PPG during physical exercise
Alexander J. Casson,Arturo Vazquez Galvez,Delaram Jarchi 한국통신학회 2016 ICT Express Vol.2 No.4
Many wearable devices include PPG (photoplethysmography) sensors for non-invasive heart rate monitoring. However, PPG signals are heavily corrupted by motion interference, and rely on simultaneous motion measurements to remove the interference. Accelerometers are used commonly, but cannot differentiate between acceleration due to movement and acceleration due to gravity. This paper compares measurements of motion using accelerometers and gyroscopes to give a more complete estimate of wrist motion. Results show the two sensor signals are very different, with low correlations present. When used in a wrist PPG heart rate algorithm gyroscope motion estimates obtain better performance in half of the cases.
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