Recent Past Members
Philip A. Shaltis, Ph.D. in Mechanical Engineering, MIT
Eric R. Wade,
Ph.D. in Mechanical Engineering, MIT
Levi B. Wood, Ph.D. Candidate in Mechanical Engineering, MIT
Recent Projects
Adaptive
Motion Artifact Reduction for Wearable Health Monitoring Sensors Using
MEMS Accelerometers
Wearable medical sensors are expected to revolutionize home health
monitoring and sports medicine. The photoplethysmogram (PPG) sensor, in
particular, contains rich information about heart pulsation and blood
oxygen saturation as well as breath rate. More recently, the PPG has
even been used for estimating arterial blood pressure.
However, the physiologic information found in a PPG signal may be so significantly corrupted by non-physiologic artifacts that the measured signal can no longer be used for any medical assessment, as shown in the top figure to the right.
It is extremely difficult, or even impossible, to eliminate motion artifact induction into the PPG sensor signal. It is very difficult to eliminate contact disturbances/movements between the sensor and the skin. Even if it were possible to eliminate sensor contact artifacts, there is additional motion artifact due to the acceleration induced blood flow in the peripheral arteries.
Since it is not possible to eliminate motion artifact induction, we have instead explored a framework for removing motion artifact from a corrupted sensor signal.
As shown in the middle figure on the right, we equip a PPG sensor, in particular a PPG finger-ring senor, with a MEMS accelerometer, to measure sensor and arm motions. We then use the acceleration information with the corrupted PPG signal measurement in an adaptive noise cancellation framework to remove the portion of the PPG signal that is correlated with the motion. This yields a recovered signal that should be free of motion artifact.
We have found that, by using Widrow’s Active Noise Cancellation framework and a Laguerre series expansion to model the motion-to-artifact dynamics, we are able to consistently obtain results that are able to capture wave form timing and amplitude, as shown in the bottom figure on the right.
Related Publications
L.B. Wood, H.H Asada, “Low
Variance Adaptive Filter for Cancelling Motion Artifact in Wearable
Photoplethysmogram Sensor Signals,” Proceedings
of IEEE Engineering in Medicine and Biology Conference, Sept. 2007. (Poster Presented at Conference)
L.B.
Wood, H.H Asada, “Active Motion Artifact Reduction for Wearable Sensors Using Laguerre Expansion and Signal Separation,” Proceedings of IEEE Engineering in Medicine and
Biology Conference, pp
3571-3574, Sept. 2005.
P.T. Gibbs, L.B.
Wood, H.H Asada, ““Active Motion Artifact Cancellation for Wearable Health Monitoring Sensors using MEMS Accelerometers,” SPIE
Smart Structures and Materials 2005, Vol. 5765 pp. 811-819, May 2005.
Revised: September 23, 2007.