Paper Info
Title
Multimodal Behavior Analysis Towards Detecting Mild Cognitive Impairment: Preliminary Results on Gait and Speech.
Abstract
Behavioral analysis for identifying changes in cognitive and physical functioning is expected to help detect dementia such as mild cognitive impairment (MCI) at an early stage. Speech and gait features have been especially recognized as behavioral biomarkers for dementia that possibly occur early in its course, including MCI. However, there are no studies investigating whether exploiting the combination of multimodal behavioral data could improve detection accuracy. In this study, we collected speech and gait behavioral data from Japanese seniors consisting of cognitively healthy adults and patients with MCI Comparing the models using single modality behavioral data, we showed that the model using multimodal behavioral data could improve detection by up to 5.9%, achieving 82.4% accuracy (chance 55.9%). Our results suggest that the combination of multimodal behavioral features capturing different functional changes resulting from dementia might improve accuracy and help timely diagnosis at an early stage.
Year
DOI
Venue
2019
10.3233/SHTI190240
Studies in Health Technology and Informatics
Keywords
Field
DocType
Alzheimer's disease,linguistic features,motion capture
Data mining,Gait,Physical medicine and rehabilitation,Medicine,Cognitive impairment
Conference
Volume
ISSN
Citations 
264
0926-9630
0
PageRank 
References 
Authors
0.34
0
13
Name
Order
Citations
PageRank
Kaoru Shinkawa1122.62
Akihiro Kosugi272.80
Masafumi Nishimura300.34
Miyuki Nemoto421.13
Kiyotaka Nemoto5213.58
Tomoko Takeuchi610.73
Yuriko Numata710.73
Ryohei Watanabe810.73
Eriko Tsukada910.73
Miho Ota10212.93
Shinji Higashi1110.73
Tetsuaki Arai1221.13
Yasunori Yamada1363.98