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CHS: Medium: Collaborative Research: Teachable Activity Trackers for Older Adults

CHS：媒介：协作研究：针对老年人的可教学活动追踪器

基本信息

批准号：
1955568
负责人：
Eun Kyoung Choe
金额：
$ 108万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955568&HistoricalAwards=false
关键词：
CHS Medium Collaborative Research Teachable

项目摘要

Self-tracking of physical activities can support people of all ages in understanding their lifestyle behaviors and making healthy choices, reducing chronic disease risks. For older adults, movement behaviors are especially critical. They help people maintain functional abilities and live independently. Smart watches and other activity tracking technologies have become available, making self-tracking easier than before, but older adults have adopted them less. One barrier is that current physical activity trackers do not effectively identify and track older adults’ activities. This project aims to understand (1) what kind of data are needed from older adults to make activity tracking work for them; and (2) how to engage older adults to collect the needed data. This project will develop a new approach to personalizing older adults’ activity tracking. It will open up new research avenues on personalized and multimodal self-tracking that affect healthcare, quality of life, and privacy. This project is expected to make broader impacts for older adults in enhancing their motivation to engage in physical activities, as well as societal impacts in nurturing a culture of diversity and inclusion that benefits the lives of older adults and people with and without disabilities or health conditions.This project uses “teachable interfaces” to facilitate personalized, self-tracking for older adults’ physical activities, while considering their changes in mobility and diverse physical characteristics. The teachable interfaces are intended to help people provide personalized activity labels, which will be used to recognize their unique movements. They will also enable self-tracking of meaningful and modifiable movement and non-movement activities, supporting older adults to displace inactivity with physical activity, which can provide significant health benefits. The research team will investigate: (1) older adults’ movement and non-movement activities that they wish to change; (2) new personalized, multimodal activity trackers that provide opportunities for self-reflection through teachable interfaces; and (3) commonalities and differences in efficacies for subgroups of older adults (e.g., people with mild dementia) and what adjustments are needed to accommodate them. Combining expertise from human-computer interaction, interactive machine learning, accessibility, aging, and kinesiology, the project will employ a mixed-methods research approach: co-design with older adults, technology design and development, and evaluations both in the lab and in people’s natural environments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

身体活动的自我跟踪可以帮助所有年龄段的人了解他们的生活方式行为并做出健康的选择，降低慢性病风险。对于老年人来说，运动行为尤其重要。它们帮助人们保持功能性能力并独立生活。智能手表和其他活动跟踪技术已经出现，这使得自我跟踪比以前更容易，但老年人采用它们的人较少。一个障碍是，目前的身体活动跟踪器不能有效地识别和跟踪老年人的活动。该项目旨在了解（1）老年人需要什么样的数据才能使活动跟踪为他们工作;以及（2）如何让老年人收集所需的数据。该项目将开发一种新的方法来个性化老年人的活动跟踪。它将为个性化和多模式自我跟踪开辟新的研究途径，影响医疗保健，生活质量和隐私。预计该项目将对老年人产生更广泛的影响，增强他们参与体育活动的动力，并对培养多样性和包容性文化产生社会影响，使老年人和有残疾或健康状况的人的生活受益。该项目使用“可教界面”，以促进老年人体育活动的个性化和自我跟踪，同时考虑到它们在移动性和不同的物理特性方面的变化。可教界面旨在帮助人们提供个性化的活动标签，这些标签将用于识别他们独特的动作。它们还将使人们能够自我跟踪有意义的和可修改的运动和非运动活动，支持老年人以身体活动取代不活动，这可以提供显着的健康益处。研究小组将调查：（1）老年人希望改变的运动和非运动活动;（2）新的个性化，多模式活动跟踪器，通过可教界面提供自我反思的机会;（3）老年人亚组（例如，患有轻度痴呆症的人）以及需要进行哪些调整来适应他们。结合人机交互，交互式机器学习，可访问性，衰老和运动学的专业知识，该项目将采用混合方法研究方法：与老年人共同设计，技术设计和开发，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech

MyMove：帮助老年人在带有语音的智能手表上收集现场活动标签

DOI：
10.1145/3491102.3517457
发表时间：
2022
期刊：
2022 CHI Conference on Human Factors in Computing Systems
影响因子：
0
作者：
Kim, Young-Ho;Chou, Diana;Lee, Bongshin;Danilovich, Margaret;Lazar, Amanda;Conroy, David E.;Kacorri, Hernisa;Choe, Eun Kyoung
通讯作者：
Choe, Eun Kyoung

Activity Recognition in Older Adults with Training Data from Younger Adults: Preliminary Results on in Vivo Smartwatch Sensor Data

利用年轻人的训练数据识别老年人的活动：体内智能手表传感器数据的初步结果