Detection of Cardioseismographic Signals throughout the Body

2013 39th Annual Northeast Bioengineering Conference

Roscoe

Alamgir

et al. 2013

Self Cite

Section: Experimental Datamentioning

confidence: 99%

Manifestation of Periodic Vibrations in Postural Control Experiments

2013 39th Annual Northeast Bioengineering Conference

Roscoe

Alamgir

et al. 2013

Self Cite

“…Throughout the course of many experiments conducted using SLIP-FALLS, our research group has noticed that unexplained periodic noise with a central frequency slightly higher than 1 Hz is often picked up by the tri-axial accelerometer attached to a subject's head. Interestingly enough, further experiments demonstrated that the added periodic signal is only observed when the accelerometer is attached to the test subject, indicating that the source of the signal must be physiological [2]. Seated, blindfolded subjects exhibit near-constant acceleration thresholds regardless of the velocity or displacement of the move [3].…”

Section: Introductionmentioning

confidence: 99%

Periodic Entrainment in Head Accelerometer Signals from Seated Human Subjects

Roscoe

2013 39th Annual Northeast Bioengineering Conference

Sani

et al. 2013

Self Cite

Possible cardiac linked contributions were found in signals taken from an accelerometer located on a person's head. This could provide insight into perceptual thresholds of translational whole body moves of a seated subject. The signal might create a noise floor that could explain why near-constant acceleration perceptual thresholds are seen in blindfolded and seated subjects (where only vestibular input is available) regardless of displacement or velocity of the move.

Using multiple placements of accelerometers to measure cardiovascular pulse transit times

Canino

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Robinson

2016

To measure pulse transit times (PTT) and calculate pulse wave velocities (PWV), tri-axial and uniaxial accelerometers were placed in groups of 2 to 4 over the manubrium, xiphoid process, forehead, wrist and ankle, and/or over the carotid, femoral, and posterior auricular arteries in 11 consented supine subjects. Signals were sampled at 1 kHz and filtered. Radial vectors were calculated from the tri-axial measurements. A 3-lead ECG was simultaneously collected over the same 180 s window, as well as respiratory rate. Ensemble averages (with ±S.D.) and raster plots were generated for each filtered time series from 200 ms before to 800 ms after the peak of each ECG R-wave. Lag times between the R-wave peak (taken as t=0) and one or more prominent peaks (or valleys if appropriate) of the various accelerometer signals were calculated, by using the signal from the axis (or the radial vector) with the best signal to noise ratio. PWV was calculated from the regression of the distance measured versus the PTT between pairs, especially of the clinically-relevant carotid-femoral PTT. A spectral analysis of each ensemble was taken, with the hypothesis that in young adults the measures at the periphery would have less energy at higher frequencies than those of an older adult because of age-related changes in arterial distensibility.