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

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. The results suggest that the solution has strong potential for long-term user-friendly therapeutic monitoring of elderly people.

Explainable Automatic Industrial Carbon Footprint Estimation From Bank Transaction Classification Using Natural Language Processing

“…Jaccard 34.85 % Linguistic proximity metric [67] 46.89 % As shown in Table 5, Jaccard similarity results in a lower percentage of directly validated explanations. Therefore, in the rest of the experiments, our linguistic metric [67] was used.…”

“…Jaccard 34.85 % Linguistic proximity metric [67] 46.89 % As shown in Table 5, Jaccard similarity results in a lower percentage of directly validated explanations. Therefore, in the rest of the experiments, our linguistic metric [67] was used. This metric is well-suited to our goal, as it requires fewer terms per explanation than the Jaccard distance to detect similarity, and unlike the cosine distance, it does not rely on term ordering.…”

“…The configuration used for the LSTM model included For similarity metrics between groups of terms, we con-414 sidered two different approaches: (i) Jaccard similarity as 415 a baseline [65], [66], and (ii) our own sophisticated metric 416 based on lexical and semantic proximity [67]. The cosine 417 distance was discarded provided that the terms in the de-418 scriptions had no logical ordering.…”

Supporting dementia caregivers in Peru through chatbots: generative AI vs structured conversations.

“…Using conversational agents to support PWD and their caregivers is an emerging field of research. Nevertheless, recent papers detail the use of chatbots to support PWD, including in the detection of cognitive impairment (de Arriba-Pérez, et al, 2022).…”

EduChat: An AI-Based Chatbot for University-Related Information Using a Hybrid Approach

“…IBM's Project Debater [12] is an artificial intelligence system specialized in debating with humans, proficient at presenting comprehensive and well-structured arguments using natural language. AI-based chatbots have been applied in various fields, ranging from healthcare [13,14], entertainment [15,16], to education [17][18][19][20].…”