Abstract: This paper investigates the integration of different approaches to automatically predict high/low-score on the dementia scale.
Abstract—This paper investigates the integration of different approaches to automatically predict high/low-score on the de- mentia scale.
The results show that the best classification accuracy (0.875) is achieved when interaction and activity features are fused using a random forest classifier.
Going back to Table 2, 11 papers (64.7%) focus on works that use speech and vocal features, and/or video-based ones, in order to perform detection.
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This paper developed a feature extraction method related to daily activity patterns based on a clustering algorithm and analyzed its effectiveness and ...
Oct 22, 2024 · This paper focuses on dementia scale classification from daily activity data collected using sensors that can be deployed in actual residential ...
Apr 15, 2022 · In this paper, we refer to the extracted behavioral features based on daily activity patterns as bag-of-activity patterns (BoAPs) and verify ...
Akira Minamisawa, Shogo Okada , Ken Inoue, Mami Noguchi: Dementia Scale Score Classification Based on Daily Activities Using Multiple Sensors.
This paper proposes a new approach to detecting very early stage of dementia automatically. We develop a computer avatar with spoken dialog functionalities ...
Dementia Scale Classification Based on Ubiquitous Daily Activity and Interaction Sensing ... Interaction (ACII). 2019 8th International Conference on ...