文章基本信息

标题：Fitness Activity Recognition on Smartphones Using Doppler Measurements
本地全文：下载
作者：Biying Fu ; Florian Kirchbuchner ; Arjan Kuijper 等
期刊名称：Informatics
电子版ISSN：2227-9709
出版年度：2018
卷号：5
期号：2
页码：24
DOI：10.3390/informatics5020024
语种：English
出版社：MDPI Publishing
摘要：Quantified Self has seen an increased interest in recent years, with devices including smartwatches, smartphones, or other wearables that allow you to monitor your fitness level. This is often combined with mobile apps that use gamification aspects to motivate the user to perform fitness activities, or increase the amount of sports exercise. Thus far, most applications rely on accelerometers or gyroscopes that are integrated into the devices. They have to be worn on the body to track activities. In this work, we investigated the use of a speaker and a microphone that are integrated into a smartphone to track exercises performed close to it. We combined active sonar and Doppler signal analysis in the ultrasound spectrum that is not perceivable by humans. We wanted to measure the body weight exercises bicycles, toe touches, and squats, as these consist of challenging radial movements towards the measuring device. We have tested several classification methods, ranging from support vector machines to convolutional neural networks. We achieved an accuracy of 88% for bicycles, 97% for toe-touches and 91% for squats on our test set.
关键词：human activity recognition; exercise recognition; mobile sensing; ultrasound sensing; Doppler effect human activity recognition ; exercise recognition ; mobile sensing ; ultrasound sensing ; Doppler effect