An analysis of eye-movements during reading for the detection of mild cognitive impairment
Kathleen C. Fraser, Kristina Lundholm Fors, Dimitrios Kokkinakis, Arto Nordlund
Abstract
We present a machine learning analysis of eye-tracking data for the detection of mild cognitive impairment, a decline in cognitive abilities that is associated with an increased risk of developing dementia. We compare two experimental configurations (reading aloud versus reading silently), as well as two methods of combining information from the two trials (concatenation and merging). Additionally, we annotate the words being read with information about their frequency and syntactic category, and use these annotations to generate new features. Ultimately, we are able to distinguish between participants with and without cognitive impairment with up to 86% accuracy.- Anthology ID:
- D17-1107
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1016–1026
- Language:
- URL:
- https://aclanthology.org/D17-1107
- DOI:
- 10.18653/v1/D17-1107
- Cite (ACL):
- Kathleen C. Fraser, Kristina Lundholm Fors, Dimitrios Kokkinakis, and Arto Nordlund. 2017. An analysis of eye-movements during reading for the detection of mild cognitive impairment. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1016–1026, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- An analysis of eye-movements during reading for the detection of mild cognitive impairment (Fraser et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/D17-1107.pdf