TY - GEN
T1 - Toward visual field assessment using head-worn sensing devices
AU - Ma, Yuchao
AU - Aminikhanghahi, Samaneh
AU - Wilhelm, Shane
AU - Thorsen, Wesley Daniel
AU - Coleman, Evan Conley
AU - Ghasemzadeh, Hassan
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported in part by the National Science Fountion under the grant CNS-1566359. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/4/2
Y1 - 2018/4/2
N2 - 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.
AB - 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.
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U2 - 10.1109/BSN.2018.8329673
DO - 10.1109/BSN.2018.8329673
M3 - Conference contribution
AN - SCOPUS:85049668968
T3 - 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2018
SP - 118
EP - 121
BT - 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2018
Y2 - 4 March 2018 through 7 March 2018
ER -