论文标题
用于使用腕上戴的可穿戴传感器步行和跑步的基于CNN的速度检测算法
CNN-based Speed Detection Algorithm for Walking and Running using Wrist-worn Wearable Sensors
论文作者
论文摘要
近年来，无处不在的技术（例如智能手表和健身追踪器）激增，可以毫不费力地跟踪人类的身体活动。这些设备使普通公民能够跟踪自己的身体健康，并鼓励他们过着健康的生活方式。在各种练习中，步行和跑步是人们在日常生活中最常见的人，无论是通勤，运动还是做家务。如果以正确的强度完成，步行和跑步足以帮助个人达到健身和减肥目标。因此，重要的是要测量步行/跑步速度，以估算燃烧的卡路里，并防止它们遭受酸痛，伤害和倦怠的风险。现有的可穿戴技术使用GPS传感器来测量高效率高且在室内效果不佳的速度。在本文中，我们设计，实施和评估了基于卷积神经网络的算法，该算法利用腕部损坏的设备利用加速度计和陀螺仪的感觉数据以高精度检测速度。在跑步机上以不同速度行走/跑步时，收集了$ 15 $的参与者的数据。我们的速度检测算法通过$ 70-15-15 $ 70-15-15 $ traint-test-test-teast-apes-test评估分配和一对一的交叉验证评估策略，实现了$ 4.2 \％$和$ 9.8 \％$ $ MAPE（平均绝对错误百分比）值。
In recent years, there have been a surge in ubiquitous technologies such as smartwatches and fitness trackers that can track the human physical activities effortlessly. These devices have enabled common citizens to track their physical fitness and encourage them to lead a healthy lifestyle. Among various exercises, walking and running are the most common ones people do in everyday life, either through commute, exercise, or doing household chores. If done at the right intensity, walking and running are sufficient enough to help individual reach the fitness and weight-loss goals. Therefore, it is important to measure walking/ running speed to estimate the burned calories along with preventing them from the risk of soreness, injury, and burnout. Existing wearable technologies use GPS sensor to measure the speed which is highly energy inefficient and does not work well indoors. In this paper, we design, implement and evaluate a convolutional neural network based algorithm that leverages accelerometer and gyroscope sensory data from the wrist-worn device to detect the speed with high precision. Data from $15$ participants were collected while they were walking/running at different speeds on a treadmill. Our speed detection algorithm achieved $4.2\%$ and $9.8\%$ MAPE (Mean Absolute Error Percentage) value using $70-15-15$ train-test-evaluation split and leave-one-out cross-validation evaluation strategy respectively.
