Loh Darrell, Lee Tien J, Zihajehzadeh Shaghayegh, Hoskinson Reynald, Park Edward J
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:502-5. doi: 10.1109/EMBC.2015.7318409.
Fitness activity classification on wearable devices can provide activity-specific information and generate more accurate performance metrics. Recently, optical head-mounted displays (OHMD) like Google Glass, Sony SmartEyeglass and Recon Jet have emerged. This paper presents a novel method to classify fitness activities using head-worn accelerometer, barometric pressure sensor and GPS, with comparisons to other common mounting locations on the body. Using multiclass SVM on head-worn sensors, we obtained an average F-score of 96.66% for classifying standing, walking, running, ascending/descending stairs and cycling. The best sensor location combinations were found to be on the ankle plus another upper body location. Using three or more sensors did not show a notable improvement over the best two-sensor combinations.
可穿戴设备上的健身活动分类能够提供特定活动信息，并生成更准确的性能指标。最近，像谷歌眼镜、索尼智能眼镜和Recon Jet这样的光学头戴式显示器（OHMD）已经出现。本文提出了一种使用头戴式加速度计、气压传感器和全球定位系统（GPS）对健身活动进行分类的新方法，并与身体上其他常见的安装位置进行了比较。通过在头戴式传感器上使用多类支持向量机（SVM），我们在对站立、行走、跑步、上下楼梯和骑自行车进行分类时获得了96.66%的平均F值。发现最佳的传感器位置组合是在脚踝加上另一个上身位置。使用三个或更多传感器并没有比最佳的双传感器组合显示出显著的改进。
