Predicting Future Accident Risks of Older Drivers by Speech Data from a Voice-Based Dialogue System: A Preliminary Result

Yamada, Yasunori; Shinkawa, Kaoru; Kosugi, Akihiro; Kobayashi, Masatomo; Takagi, Hironobu; Nemoto, Miyuki; Nemoto, Kiyotaka; Arai, Tetsuaki

doi:10.1007/978-3-030-51057-2_19

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…With these shifts, NLP can potentially be applied to speech signals in real-time or asynchronously in clinical contexts. Recent studies have used NLP to generate COVID-19 phenotypes ( 76 ), track emotional distress in online cancer support groups ( 77 ), diagnose PD ( 78 , 79 ), predict driving risk in older adults ( 80 ), and predict binge-eating behaviors ( 81 ).…”

Section: Complementary and Novel Strategies To Measure Depressionmentioning

confidence: 99%

Natural Language Processing as an Emerging Tool to Detect Late-Life Depression

DeSouza¹,

Robin²,

Gumus³

et al. 2021

Front. Psychiatry

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Late-life depression (LLD) is a major public health concern. Despite the availability of effective treatments for depression, barriers to screening and diagnosis still exist. The use of current standardized depression assessments can lead to underdiagnosis or misdiagnosis due to subjective symptom reporting and the distinct cognitive, psychomotor, and somatic features of LLD. To overcome these limitations, there has been a growing interest in the development of objective measures of depression using artificial intelligence (AI) technologies such as natural language processing (NLP). NLP approaches focus on the analysis of acoustic and linguistic aspects of human language derived from text and speech and can be integrated with machine learning approaches to classify depression and its severity. In this review, we will provide rationale for the use of NLP methods to study depression using speech, summarize previous research using NLP in LLD, compare findings to younger adults with depression and older adults with other clinical conditions, and discuss future directions including the use of complementary AI strategies to fully capture the spectrum of LLD.

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Section: Complementary and Novel Strategies To Measure Depressionmentioning

confidence: 99%

Natural Language Processing as an Emerging Tool to Detect Late-Life Depression

DeSouza¹,

Robin²,

Gumus³

et al. 2021

Front. Psychiatry

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“…With work in this area ongoing, preliminary results have shown promise: researchers found statistically significant correlations between MMSE scores and features measuring pauses, hesitations, and error-handling when older adults interacted with virtual assistants [25]; achieved 79% accuracy in predicting future car accidents (within 1.5 years) based on pauses, filled pauses, and pronoun use in conversations with virtual assistants [26]; and found that people with MCI pause more often and for longer, and produce fewer words and shorter speech chunks when interacting with virtual assistants than do cognitively healthy users [27].…”

Section: Smartphone Conversation Recordingmentioning

confidence: 99%

Measuring Cognitive Status from Speech in a Smart Home Environment

Fraser,

Komeili

2021

Preprint

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The population is aging, and becoming more tech-savvy. The United Nations predicts that by 2050, one in six people in the world will be over age 65 (up from one in 11 in 2019), and this increases to one in four in Europe and Northern America. Meanwhile, the proportion of American adults over 65 who own a smart phone has risen 24 percentage points from 2013-2017, and the majority have Internet in their homes [1]. Smart devices and smart home technology have profound potential to transform how people age, their ability to live independently in later years, and their interactions with their circle of care.Cognitive health is a key component to independence and well-being in old age, and smart homes present many opportunities to measure cognitive status in a continuous, unobtrusive manner.In this article, we focus on speech as a measurement instrument for cognitive health. Existing methods of cognitive assessment suffer from a number of limitations that could be addressed through smart home speech sensing technologies. We begin with a brief tutorial on measuring cognitive status from speech, including some pointers to useful open-source software toolboxes for the interested reader. We then present an overview of the preliminary results from pilot studies on active and passive smart home speech sensing for the measurement of cognitive health, and conclude with some recommendations and challenge statements for the next wave of work in this area, to help overcome both technical and ethical barriers to success. Measuring Cognitive Decline from SpeechDeclines in cognitive ability can occur as a result of a number of underlying physical factors: traumatic brain injury, stroke, and neurodegenerative diseases such as Alzheimer's disease (AD), among others. The set of cognitive symptoms associated with neurodegenerative diseases is known as dementia. Since dementia develops slowly over time, there is increasing interest in methods to detect it at the earliest possible stages, when interventions aimed to slow down the decline process are most effective. It is estimated that approximately half of dementia cases go undiagnosed, which can lead to poorer outcomes for both the individual and their caregivers [2].

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Measuring Cognitive Status from Speech in a Smart Home Environment

Fraser

Komeili²

2021

IEEE Instrum. Meas. Mag.

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Predicting Future Accident Risks of Older Drivers by Speech Data from a Voice-Based Dialogue System: A Preliminary Result

Cited by 4 publications

References 13 publications

Natural Language Processing as an Emerging Tool to Detect Late-Life Depression

Natural Language Processing as an Emerging Tool to Detect Late-Life Depression

Measuring Cognitive Status from Speech in a Smart Home Environment

Measuring Cognitive Status from Speech in a Smart Home Environment

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