Title
Cardiac and Respiratory Parameter Estimation Using Head-mounted Motion-sensitive Sensors
Abstract
This work explores the feasibility of using motion-sensitive sensors embedded in Google Glass, a head-mounted wearable device, to robustly measure physiological signals of the wearer. In particular, we develop new methods to use Glass’s accelerometer, gyroscope, and camera to extract pulse and respiratory waves of 12 participants during a controlled experiment. We show it is possible to achieve a mean absolute error of 0.82 beats per minute (STD: 1.98) for heart rate and 0.6 breaths per minute (STD: 1.19) for respiration rate when considering different observation windows and combinations of sensors. Moreover, we show that a head-mounted gyroscope sensor shows improved performance versus more commonly explored sensors such as accelerometers and demonstrate that a head-mounted camera is a novel and promising method to capture the physiological responses of the wearer. These findings included testing across sitting, supine, and standing postures before and after physical exercise.
Year
Venue
Field
2015
EAI Endorsed Transactions on Pervasive Health and Technology
Computer vision,Gyroscope,Accelerometer,Wearable computer,Pulse (signal processing),Artificial intelligence,Controlled experiment,Estimation theory,Sitting,Medicine,Supine position
DocType
Volume
Issue
Journal
1
1
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Javier Hernandez11017.58
Yin Li279735.85
James M. Rehg35259474.66
R. W. Picard487241493.12