4,405 research outputs found
A Method for Analysis of Patient Speech in Dialogue for Dementia Detection
We present an approach to automatic detection of Alzheimer's type dementia
based on characteristics of spontaneous spoken language dialogue consisting of
interviews recorded in natural settings. The proposed method employs additive
logistic regression (a machine learning boosting method) on content-free
features extracted from dialogical interaction to build a predictive model. The
model training data consisted of 21 dialogues between patients with Alzheimer's
and interviewers, and 17 dialogues between patients with other health
conditions and interviewers. Features analysed included speech rate,
turn-taking patterns and other speech parameters. Despite relying solely on
content-free features, our method obtains overall accuracy of 86.5\%, a result
comparable to those of state-of-the-art methods that employ more complex
lexical, syntactic and semantic features. While further investigation is
needed, the fact that we were able to obtain promising results using only
features that can be easily extracted from spontaneous dialogues suggests the
possibility of designing non-invasive and low-cost mental health monitoring
tools for use at scale.Comment: 8 pages, Resources and ProcessIng of linguistic, paralinguistic and
extra-linguistic Data from people with various forms of cognitive impairment,
LREC 201
A Mimetic Strategy to Engage Voluntary Physical Activity In Interactive Entertainment
We describe the design and implementation of a vision based interactive
entertainment system that makes use of both involuntary and voluntary control
paradigms. Unintentional input to the system from a potential viewer is used to
drive attention-getting output and encourage the transition to voluntary
interactive behaviour. The iMime system consists of a character animation
engine based on the interaction metaphor of a mime performer that simulates
non-verbal communication strategies, without spoken dialogue, to capture and
hold the attention of a viewer. The system was developed in the context of a
project studying care of dementia sufferers. Care for a dementia sufferer can
place unreasonable demands on the time and attentional resources of their
caregivers or family members. Our study contributes to the eventual development
of a system aimed at providing relief to dementia caregivers, while at the same
time serving as a source of pleasant interactive entertainment for viewers. The
work reported here is also aimed at a more general study of the design of
interactive entertainment systems involving a mixture of voluntary and
involuntary control.Comment: 6 pages, 7 figures, ECAG08 worksho
Detecting Alzheimer's Disease Using Interactional and Acoustic Features from Spontaneous Speech
Alzheimer’s Disease (AD) is a form of Dementia that manifests in cognitive decline including memory, language, and changes in behavior. Speech data has proven valuable for inferring cognitive status, used in many health assessment tasks, and can be easily elicited in natural settings. Much work focuses on analysis using linguistic features; here, we focus on non-linguistic features and their use in distinguishing AD patients from similar-age Non-AD patients with other health conditions in the Carolinas Conversation Collection (CCC) dataset. We used two types of features: patterns of interaction including pausing behaviour and floor control, and acoustic features including pitch, amplitude, energy, and cepstral coefficients. Fusion of the two kinds of features, combined with feature selection, obtains very promising classification results: classification accuracy of 90% using standard models such as support vector machines and logistic regression. We also obtain promising results using interactional features alone (87% accuracy), which can be easily extracted from natural conversations in daily life and thus have the potential for future implementation as a noninvasive method for AD diagnosis and monitoring
Conversational affective social robots for ageing and dementia support
Socially assistive robots (SAR) hold significant potential to assist older adults and people with dementia in human engagement and clinical contexts by supporting mental health and independence at home. While SAR research has recently experienced prolific growth, long-term trust, clinical translation and patient benefit remain immature. Affective human-robot interactions are unresolved and the deployment of robots with conversational abilities is fundamental for robustness and humanrobot engagement. In this paper, we review the state of the art within the past two decades, design trends, and current applications of conversational affective SAR for ageing and dementia support. A horizon scanning of AI voice technology for healthcare, including ubiquitous smart speakers, is further introduced to address current gaps inhibiting home use. We discuss the role of user-centred approaches in the design of voice systems, including the capacity to handle communication breakdowns for effective use by target populations. We summarise the state of development in interactions using speech and natural language processing, which forms a baseline for longitudinal health monitoring and cognitive assessment. Drawing from this foundation, we identify open challenges and propose future directions to advance conversational affective social robots for: 1) user engagement, 2) deployment in real-world settings, and 3) clinical translation
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