Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with Natural Language Processing capabilities

TL;DR

This paper presents an entertainment chatbot that uses Natural Language Processing and Machine Learning to automatically detect cognitive impairment in elderly people through engaging conversations about news, reducing the need for manual testing.

Contribution

It introduces an automated, engaging system that assesses cognitive skills and detects impairment using natural language generation and similarity metrics, validated with field tests.

Findings

Participants without impairment scored significantly higher on similarity metrics.

The system achieved over 80% accuracy, F-measure, and recall in detecting cognitive impairment.

Stress and concentration levels affected user performance and similarity scores.

Abstract

Previous researchers have proposed intelligent systems for therapeutic monitoring of cognitive impairments. However, most existing practical approaches for this purpose are based on manual tests. This raises issues such as excessive caretaking effort and the white-coat effect. To avoid these issues, we present an intelligent conversational system for entertaining elderly people with news of their interest that monitors cognitive impairment transparently. Automatic chatbot dialogue stages allow assessing content description skills and detecting cognitive impairment with Machine Learning algorithms. We create these dialogue flows automatically from updated news items using Natural Language Generation techniques. The system also infers the gold standard of the answers to the questions, so it can assess cognitive capabilities automatically by comparing these answers with the user responses. It employs a similarity metric with values in [0, 1], in increasing level of similarity. To evaluate the performance and usability of our approach, we have conducted field tests with a test group of 30 elderly people in the earliest stages of dementia, under the supervision of gerontologists. In the experiments, we have analysed the effect of stress and concentration in these users. Those without cognitive impairment performed up to five times better. In particular, the similarity metric varied between 0.03, for stressed and unfocused participants, and 0.36, for relaxed and focused users. Finally, we developed a Machine Learning algorithm based on textual analysis features for automatic cognitive impairment detection, which attained accuracy, F-measure and recall levels above 80%. We have thus validated the automatic approach to detect cognitive impairment in elderly people based on entertainment content.