With the flourishing development of body sensor networks, a variety of head-worn sensor-based devices have emerged in many domains, to facilitate applications involving head movements. This paper explores the potential of using head-mounted sensors coupled with computational algorithms, to assess visual field defects through analyzing head motion in reading activities. Visual field defects, such as homonymous hemianopia, is a common disorder that occurs after stroke, injury, or vascular brain damage. A customized reading experiment is conducted on 17 participants, while Google Glass is used for head motion monitoring and visual field defect simulation. The results show a 6%-10% drop in reading performance with the simulated condition. Several machine learnig algorithms demonstrate the distinguishability of head motion in reading activities for visual field defect, with an average accuracy of 91%. Furthermore, experiment results suggest that the difference in head motion between normal and impaired visual field is less significant under extreme reading conditions.