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Found 13 papers, 4 papers with code
Everyday Living Artificial Intelligence Hub
We present the Everyday Living Artificial Intelligence (AI) Hub, a novel proof-of-concept framework for enhancing human health and wellbeing via a combination of tailored wear-able and Conversational Agent (CA) solutions for non-invasive monitoring of physiological signals, assessment of behaviors through unobtrusive wearable devices, and the provision of personalized interventions to reduce stress and anxiety.
Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?
\textbf{Results}: Imperfect ASR-generated transcripts surprisingly outperformed manual transcription for distinguishing between individuals with AD and those without in the ``Cookie Theft'' task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Backdoor Adjustment of Confounding by Provenance for Robust Text Classification of Multi-institutional Clinical Notes
Machine learning and deep learning approaches have been used to improve the performance of clinical NLP.
A Dialogue System for Assessing Activities of Daily Living: Improving Consistency with Grounded Knowledge
A lack of functioning may lead to poor living conditions requiring personal care and assistance.
TRESTLE: Toolkit for Reproducible Execution of Speech, Text and Language Experiments
The evidence is growing that machine and deep learning methods can learn the subtle differences between the language produced by people with various forms of cognitive impairment such as dementia and cognitively healthy individuals.
The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia Classification
Linguistic anomalies detectable in spontaneous speech have shown promise for various clinical applications including screening for dementia and other forms of cognitive impairment.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models
Deep learning (DL) techniques involving fine-tuning large numbers of model parameters have delivered impressive performance on the task of discriminating between language produced by cognitively healthy individuals, and those with Alzheimer's disease (AD).
A Conversational Agent System for Dietary Supplements Use
Dietary supplements (DS) have been widely used by consumers, but the information around the efficacy and safety of DS is disparate or incomplete, thus creating barriers for consumers to find information effectively.
Conversational Agent for Daily Living Assessment Coaching Demo
Conversational Agent for Daily Living Assessment Coaching (CADLAC) is a multi-modal conversational agent system designed to impersonate {``}individuals{''} with various levels of ability in activities of daily living (ADLs: e. g., dressing, bathing, mobility, etc.)
A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the Alzheimer's Type
In recent years there has been a burgeoning interest in the use of computational methods to distinguish between elicited speech samples produced by patients with dementia, and those from healthy controls.
What Analogies Reveal about Word Vectors and their Compositionality
Analogy completion via vector arithmetic has become a common means of demonstrating the compositionality of word embeddings.
