Information from different bio-signals such as speech, handwriting, and gait have been used to monitor the state of Parkinson's disease (PD) patients, however, all the multimodal bio-signals may not always be available. We propose a method based on multi-view representation learning via generalized canonical correlation analysis (GCCA) for learning a representation of features extracted from handwriting and gait that can be used as a complement to speech-based features. Three different problems are addressed: classification of PD patients vs. healthy controls, prediction of the neurological state of PD patients according to the UP-DRS score, and the prediction of a modified version of the Frenchay dysarthria assessment (m-FDA). According to the results, the proposed approach is suitable to improve the results in the addressed problems, specially in the prediction of the UPDRS, and m-FDA scores.
Alzheimer's disease (AD) is an increasingly prevalent cognitive disorder in which memory, language, and executive function deteriorate, usually in that order. There is a growing need to support individuals with AD and other forms of dementia in their daily lives, and our goal is to do so through speech-based interaction. Given that 33% of conversations with people with middle-stage AD involve a breakdown in communication, it is vital that automated dialogue systems be able to identify those breakdowns and, if possible, avoid them. In this article, we discuss several linguistic features that are verbal indicators of confusion in AD (including vocabulary richness, parse tree structures, and acoustic cues) and apply several machine learning algorithms to identify dialogue-relevant confusion from speech with up to 82% accuracy. We also learn dialogue strategies to avoid confusion in the first place, which is accomplished using a partially observable Markov decision process and which obtains accuracies (up to 96.1%) that are significantly higher than several baselines. This work represents a major step towards automated dialogue systems for individuals with dementia.
The partially observable Markov decision process (POMDP) framework has been applied in dialogue systems as a formal framework to represent uncertainty explicitly while being robust to noise. In this context, estimating the dialogue POMDP model components (states, observations, and reward) is a significant challenge as they have a direct impact on the optimized dialogue POMDP policy. Learning states and observations sustaining a POMDP have been both covered in the first part (Part I), whereas this part (Part II) covers learning the reward function, that is required by the POMDP. To this end, we propose two specific algorithms based on inverse reinforcement learning (IRL). The first is called POMDP-IRL-BT (BT for belief transition) and it approximates a belief transition model, similar to the Markov decision process transition models. The second is a pointbased POMDP-IRL algorithm, denoted by PB-POMDP-IRL (PB for point-based), that approximates the value of the new beliefs, which occurs in the computation of the policy values, using a linear approximation of expert beliefs. Ultimately, we apply the two algorithms on healthcare dialogue management in order to learn a dialogue POMDP from dialogues collected by SmartWheeler (an intelligent wheelchair).
Description logics (DLs) are formalisms for representing knowledge bases of application domains. The Web Ontology Language (OWL) is a syntactic variant of a very expressive DL. OWL reasoners can infer implied information from OWL ontologies. The performance of OWL reasoners can be severely affected by situations that require decision-making over many alternatives. Such a nondeterministic behavior is often controlled by heuristics that are based on insufficient information. This article proposes a novel OWL reasoning approach that applies machine learning (ML) to implement pragmatic and optimal decision-making strategies in such situations. Disjunctions occurring in ontologies are one source of nondeterministic actions in reasoners. We propose two ML-based approaches to reduce the nondeterminism caused by dealing with disjunctions. The first approach is restricted to propositional DL while the second one can deal with standard DL. Both approaches speed up our ML-based reasoner by up to two orders of magnitude in comparison to the non-ML reasoner. Another source of nondeterministic actions is the order in which tableau rules should be applied. On average, our ML-based approach achieves a speedup of two
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.