Fine-grained, unobtrusive monitoring of gym exercises can help users track their own exercise routines and also provide corrective feedback. We propose W8-Scope, a system that uses a simple magnetic-cum-accelerometer sensor, mounted on the weight stack of gym exercise machines, to infer various attributes of gym exercise behavior. More specifically, using multiple machine learning models, W8-Scope helps identify who is exercising, what exercise she is doing, how much weight she is lifting, and whether she is committing any common mistakes. Real world studies, conducted with 50 subjects performing 14 different exercises over 103 distinct sessions in two gyms, show that W8-Scope can, at the granularity of individual exercise sets, achieve high accuracy—e.g., identify the weight used with an accuracy of 97.5%, detect commonplace mistakes with 96.7% accuracy and identify the user with 98.7% accuracy. By incorporating an additional, simple IR sensor on the weight stack, the exercise classification accuracy (across the 14 exercises) further increases from 96.93% to 97.51%. Moreover, by adopting incremental learning techniques, W8-Scope can also accurately track these various facets of exercise over longitudinal periods, in spite of the inherent natural changes in a user’s exercising behavior. Our comprehensive analysis also reveals open challenges, such as adapting to the expertise level of individuals or providing in-situ, early feedback, that remain to be addressed.

对健身房锻炼进行细粒度、不显眼的监控可以帮助用户跟踪自己的锻炼习惯，并提供纠正反馈。我们提出了W8-Scope系统，它使用一个简单的磁性和加速度计传感器，安装在健身房锻炼机器的重量堆栈上，以推断健身房锻炼行为的各种属性。更具体地说，W8-Scope使用多种机器学习模型，帮助识别谁在锻炼、她在做什么运动、她举了多少重量，以及她是否犯了任何常见错误。对 50 名受试者在两个体育馆的 103 次不同课程中进行 14 种不同锻炼进行的真实世界研究表明，W8-Scope可以在单个练习集的粒度上实现高精度——例如，以 97.5% 的准确度识别使用的重量，以 96.7% 的准确度检测常见错误，以 98.7% 的准确度识别用户。通过在配重块上加入一个额外的简单 IR 传感器，运动分类准确度（跨越 14 个运动）进一步从 96.93% 提高到 97.51%。此外，通过采用渐进式学习技术，W8-Scope还可以在纵向周期内准确跟踪锻炼的这些不同方面，尽管用户的锻炼行为存在固有的自然变化。我们的综合分析还揭示了一些尚待解决的开放性挑战，例如适应个人的专业知识水平或提供现场的早期反馈。