Towards Visualization of Time-Series Ecological Momentary Assessment (EMA) Data on Standalone Voice-First Virtual Assistants

• URL: http://arxiv.org/abs/2208.00301v1
• Date: Sat, 30 Jul 2022 20:03:15 GMT
• Title: Towards Visualization of Time-Series Ecological Momentary Assessment (EMA) Data on Standalone Voice-First Virtual Assistants
• Authors: Yichen Han, Christopher Bo Han, Chen Chen, Peng Wei Lee, Michael Hogarth, Alison A. Moore, Nadir Weibel, Emilia Farcas
• Abstract summary: We designed a prototype system, where older adults are able to query and examine the time-series EMA data on Amazon Echo Show. We conducted a preliminary semi-structured interview with a geriatrician and an older adult, and identified three findings that should be carefully considered when designing such visualizations.
• Score: 12.563166620724248
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Population aging is an increasingly important consideration for health care in the 21th century, and continuing to have access and interact with digital health information is a key challenge for aging populations. Voice-based Intelligent Virtual Assistants (IVAs) are promising to improve the Quality of Life (QoL) of older adults, and coupled with Ecological Momentary Assessments (EMA) they can be effective to collect important health information from older adults, especially when it comes to repeated time-based events. However, this same EMA data is hard to access for the older adult: although the newest IVAs are equipped with a display, the effectiveness of visualizing time-series based EMA data on standalone IVAs has not been explored. To investigate the potential opportunities for visualizing time-series based EMA data on standalone IVAs, we designed a prototype system, where older adults are able to query and examine the time-series EMA data on Amazon Echo Show - a widely used commercially available standalone screen-based IVA. We conducted a preliminary semi-structured interview with a geriatrician and an older adult, and identified three findings that should be carefully considered when designing such visualizations.

Related papers

• STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical [58.79671189792399]
  STLLaVA-Med is designed to train a policy model capable of auto-generating medical visual instruction data. We validate the efficacy and data efficiency of STLLaVA-Med across three major medical Visual Question Answering (VQA) benchmarks.
  arXiv  Detail & Related papers  (2024-06-28T15:01:23Z)
• Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence [1.2330982742485441]
  The experience of older adults' interaction with the Ehealth interface is collected through interviews and transformed into operatable databases. The study identifies important design factors, such as intuitive visualization and straightforward explanations, that are critical for creating efficient Human Computer Interaction (HCI) tools among older users.
  arXiv  Detail & Related papers  (2024-02-01T16:39:02Z)
• Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review [0.6906005491572401]
  Many studies are being published on Ambient Assisted Living (AAL) systems, which help to reduce the preoccupations raised by the independent living of the elderly. In this study, a systematic review of the literature is presented on fall detection and Human Activity Recognition (HAR) for the elderly. To address the current tendency to perform these two tasks, the review focuses on the use of Deep Learning (DL) based approaches on computer vision data.
  arXiv  Detail & Related papers  (2024-01-22T09:40:52Z)
• Clairvoyance: A Pipeline Toolkit for Medical Time Series [95.22483029602921]
  Time-series learning is the bread and butter of data-driven *clinical decision support* Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a software toolkit. Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
  arXiv  Detail & Related papers  (2023-10-28T12:08:03Z)
• LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day [85.19963303642427]
  We propose a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images. The model first learns to align biomedical vocabulary using the figure-caption pairs as is, then learns to master open-ended conversational semantics. This enables us to train a Large Language and Vision Assistant for BioMedicine in less than 15 hours (with eight A100s)
  arXiv  Detail & Related papers  (2023-06-01T16:50:07Z)
• Evaluating the Possibility of Integrating Augmented Reality and Internet of Things Technologies to Help Patients with Alzheimer's Disease [0.0]
  This study reports preliminary results on an Ambient Assisted Living (AAL) real-time system, achieved through the Internet of Things (IoT) and Augmented Reality (AR) concepts. The system has two main sections: the smartphone or windows application allows caregivers to monitor patients' status at home and be notified if patients are at risk.
  arXiv  Detail & Related papers  (2023-01-20T20:39:32Z)
• In-vehicle alertness monitoring for older adults [63.359033532099204]
  We present a system for in-vehicle alertness monitoring for older adults. We implemented a prototype traveler monitoring system and evaluated the alertness detection algorithm on ten older adults (70 years and older. This study is the first of its kind for a hitherto under-studied population and has implications for future work on algorithm development and system design through participatory methods.
  arXiv  Detail & Related papers  (2022-08-17T06:07:37Z)
• Understanding Barriers and Design Opportunities to Improve Healthcare and QOL for Older Adults through Voice Assistants [14.851433929566237]
  We consider the processes of care delivery and QOL enhancements for older adults as a collaborative task between patients and providers. We identified 12 barriers that older adults might encounter during daily routine and while managing health. Our work will benefit practitioners who study and attempt to create full fledged IVA powered smart devices to deliver better care and support an increased QOL for aging populations.
  arXiv  Detail & Related papers  (2021-11-05T23:22:05Z)
• LAE : Long-tailed Age Estimation [52.5745217752147]
  We first formulate a simple standard baseline and build a much strong one by collecting the tricks in pre-training, data augmentation, model architecture, and so on. Compared with the standard baseline, the proposed one significantly decreases the estimation errors. We propose a two-stage training method named Long-tailed Age Estimation (LAE), which decouples the learning procedure into representation learning and classification.
  arXiv  Detail & Related papers  (2021-10-25T09:05:44Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)
• Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement [9.825675909430611]
  We present a video-based and on-device optical cardiopulmonary vital sign measurement approach. It enables real-time cardiovascular and respiratory measurements on mobile platforms. We evaluate our system on an Advanced RISC Machine (ARM) CPU and achieve state-of-the-art accuracy while running at over 150 frames per second.
  arXiv  Detail & Related papers  (2020-06-06T06:31:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.