ASSETS '21: The 23rd International ACM SIGACCESS Conference on Computers and Accessibility
ASSETS '21: The 23rd International ACM SIGACCESS Conference on Computers and Accessibility
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ASSETS 21：第 23 届国际 ACM SIGACCESS 计算机和辅助功能会议
DOI：
10.1145/3441852.3476557
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Rector, Kyle
中科院分区：
文献类型：
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作者：
Malik, Jeehan;Majure, Mitchell;Rubio Bidon, Hana Gabrielle;Lamoureux, Regan;Rector, Kyle
有视觉障碍(PVI)的人比有视力的同龄人更不可能参加体育活动。一个障碍是缺乏可访问的基于团体的有氧锻炼课程，通常是由于教练没有提供可访问的口头指导。虽然在运动跟踪方面有研究，但这些工具通常需要视力或对运动的熟悉。有一些可行的解决方案，可以在慢节奏的练习中提供个性化的语言反馈，而不是一概而论到有氧运动。作为回应，我们开发了一种算法，利用计算机视觉和CNN检测传感器垫上的鞋印。我们可以推断一个人是否正在进行一次阶梯式有氧锻炼，并正在设计反应性的语言反馈来引导他重新加入这个课程。未来的工作将包括完成开发和进行用户研究，以评估反应性口头反馈的有效性。
People with visual impairments (PVIs) are less likely to participate in physical activity than their sighted peers. One barrier is the lack of accessible group-based aerobic exercise classes, often due to instructors not giving accessible verbal instructions. While there is research in exercise tracking, these tools often require vision or familiarity with the exercise. There are accessible solutions that give personalized verbal feedback in slower-paced exercises, not generalizing to aerobics. In response, we have developed an algorithm that detects shoeprints on a sensor mat using computer vision and a CNN. We can infer whether a person is following along with a step aerobics workout and are designing reactive verbal feedback to guide the person to rejoin the class. Future work will include finishing development and conducting a user study to assess the effectiveness of the reactive verbal feedback.