Motion-Adaptive Image Capture in a Body-Worn Wearable Sensor

Any body-mounted wearable sensor that takes periodic pictures is susceptible to motion blur in the captured images. Motion blur may render images useless, and capture of these images consumes battery power. In this paper, an attempt is made to decrease the number of blurred images captured by an eyeglass-mounted camera and increase battery life of the device. The camera motion was detected using an onboard accelerometer and motion derived metrics were used to control image capture. A total of 825 images with corresponding accelerometer data were collected during 2.3 hours in different lighting conditions to train several capture models. Further 1564 images (4.3 hours) were used to test and compare the capture models. The performance of the best model was assessed in an independent experiment, where two devices, one taking pictures at a fixed frequency and one using the motion-adaptive capture were used to collect 650 images (1.8 hours). The motion-adaptive algorithm captured the same number of blur-free images, but reduced power consumption by 12%. The algorithm was found to perform better in the conditions with higher chances of motion blur e.g. in low lighting conditions.