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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. 

Physical activity is an important part of quality life, however people with visual impairments (PVIs) are less likely to participate in physical activity than their sighted peers. One barrier is that exercise instructors may not give accessible verbal instructions. There is a potential for text analysis to determine these phrases, and in response provide more accessible instructions. First, a taxonomy of accessible phrases needs to be developed. To address this problem, we conducted user studies with 10 PVIs exercising along with audio and video aerobic workouts. We analyzed video footage of their exercise along with interviews to determine a preliminary set of phrases that are helpful or confusing. We then conducted an iterative qualitative analysis of six other exercise videos and sought expert feedback to derive our taxonomy. We hope these findings inform systems that analyze instructional phrases for accessibility to PVIs.