Deep Convolution Network Based Emotion Analysis for Automatic Detection of Mild Cognitive Impairment in the Elderly

TL;DR

This paper introduces a deep convolutional neural network system that analyzes facial emotion patterns to automatically detect mild cognitive impairment in the elderly, offering a potentially faster and less costly alternative to traditional methods.

Contribution

It develops a novel facial expression recognition algorithm using MobileNet and SVM, and applies it to detect cognitive impairment with promising accuracy.

Findings

Achieved 73.3% detection accuracy on elderly dataset.

Developed a facial expression recognition algorithm with MobileNet and SVM.

Validated system effectiveness with 61 elderly participants.

Abstract

A significant number of people are suffering from cognitive impairment all over the world. Early detection of cognitive impairment is of great importance to both patients and caregivers. However, existing approaches have their shortages, such as time consumption and financial expenses involved in clinics and the neuroimaging stage. It has been found that patients with cognitive impairment show abnormal emotion patterns. In this paper, we present a novel deep convolution network-based system to detect the cognitive impairment through the analysis of the evolution of facial emotions while participants are watching designed video stimuli. In our proposed system, a novel facial expression recognition algorithm is developed using layers from MobileNet and Support Vector Machine (SVM), which showed satisfactory performance in 3 datasets. To verify the proposed system in detecting cognitive impairment, 61 elderly people including patients with cognitive impairment and healthy people as a control group have been invited to participate in the experiments and a dataset was built accordingly. With this dataset, the proposed system has successfully achieved the detection accuracy of 73.3%.