Author Archives: Xiaohui Liang

The increasing number of patients living with dementia requires effective support measures. Easier access to mobile devices can provide the follow-up and monitoring of patients with dementia. The literature presents several studies on the use of mobile applications to assist patients, health professionals, and caregivers, considering the different knowledge areas, and scientific and technological contributions.Continue reading “(Related) A comprehensive systematic review on mobile applications to support dementia patients”

Eli Kurtz, Youxiang Zhu, Tiffany Driesse, Bang Tran, John A. Batsis, Robert M. Roth, and Xiaohui Liang Early detection of Alzheimer’s Disease and Related Dementias (ADRD) is critical in treating the progression of the disease. Previous studies have shown that ADRD can be detected and classified using machine learning models trained on samples of spontaneousContinue reading “Early Detection of Cognitive Decline using Voice Assistant Commands (ICASSP 2023)”

Bang Tran, Youxiang Zhu, James W. Schwoebel, and Xiaohui Liang Excessive sleepiness in critical tasks and jobs can lead to adverse outcomes, such as work accidents and car crashes. Detecting and monitoring sleepiness levels can prevent these adverse events from happening. In this paper, we propose an attention-based sleepiness detection method using HuBERT embeddings andContinue reading “Exploiting Relevance of Speech to Sleepiness Detection via Attention Mechanism (ICC 2023)”

Youxiang Zhu and Xiaohui Liang Speech-based dementia detection faces small data problems and weak data-label correlation problems. Existing works still used cross-validation or fixed training testing split as evaluation protocols on the small data, which may overestimate the performance. We propose a new evaluation protocol under the few-shot learning setting. Based on our evaluation results,Continue reading “More Data, Better Accuracy? An Empirical Study on Few-shot Speech-based Dementia Detection (SoCal 2022)”

Dr. Liang delivered a keynote speech on “Exploiting Human Speech for Early Detection of Cognitive Decline” at IEEE International Conference on Universal Village on Oct. 24, 2022.

Voice-based AI-powered digital assistants, such as Alexa, Siri, and Google Assistant, present an exciting opportunity to translate healthcare from the hospital to the home. But building a digital, medical panopticon can raise many legal and ethical challenges if not designed and implemented thoughtfully. This paper highlights the benefits and explores some of the challenges ofContinue reading “(Related) Should Alexa diagnose Alzheimer’s?: Legal and ethical issues with at-home consumer devices”

Youxiang Zhu, Xiaohui Liang, John A. Batsis (University of North Carolina), Robert M. Roth (Dartmouth) Abstract: Detecting dementia using human speech is promising but faces a limited data challenge. While recent research has shown general pretrained models (e.g., BERT) can be applied to improve dementia detection, the pretrained model can hardly be fine-tuned with theContinue reading “Towards Interpretability of Speech Pause in Dementia Detection using Adversarial Learning (INTERSPEECH 2022)”

Our poster “Older Adults experience and response to Voice Assistant Systems” was chosen as the best poster in the category of Geriatric Syndromes (descriptive research on the mechanisms, natural history or management of major geriatric syndromes) at the American Geriatrics Society (AGS) 2022.

We have successfully recruited 32 older adults for our Phase II evaluation. 13 participants (5 healthy, 8 mild cognitive impairment) chose Pathway 1, i.e., performing seven sessions once every three months + home data tracking 19 participants (10 healthy, 9 mild cognitive impairment) chose Pathway 2, i.e., performing seven sessions only once every three monthsContinue reading “Phase II update”

Tiffany M. Driesse, Xiaohui Liang, Michael Fowler, Jing Yuan, and John A. Batsis Background: Poor diet among older adults is a risk factor for developing multiple chronic diseases. Dietary recall comprises an important component in intervention research and clinical care. Commonly used tools include the web-based automated self-administered 24-hour assessment (ASA-24). Yet voice assistant (VAS)Continue reading “Patient Perceptions of Using Voice-Based Dietary Assessment Tools Among Older Adults”