A Natural Walking Monitor for Pulmonary Patients Using Mobile Phones
A Natural Walking Monitor for Pulmonary Patients Using Mobile Phones
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DOI：
10.1109/jbhi.2015.2427511
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发表时间：
2015-07-01
影响因子：
7.7
通讯作者：
Schatz, Bruce
中科院分区：
文献类型：
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作者：
Juen, Joshua;Cheng, Qian;Schatz, Bruce
移动的设备具有通过收集包括自然行走期间的行走速度的运动数据来连续监测健康的潜力。自然行走是在跑步机和护士辅助行走中没有人工速度限制的行走。健身追踪器已经变得流行，其通常使用固定的步幅来记录所采取的步骤和距离。虽然对于日常目的有用，但医疗监测需要精确的准确性，并且需要用科学有效的措施对真实的患者进行测试。步行速度与患者的发病率密切相关，并广泛用于通过测量步行进行医学评估。6分钟步行试验（6 MWT）是慢性阻塞性肺疾病和充血性心力衰竭的标准评估。当前一代智能手机硬件包含与医疗设备和流行健身设备类似的传感器芯片。我们开发了一个中间件软件MoveSense，它可以在独立的智能手机上运行，同时提供与医疗加速度计相当的读数。我们评估了六种机器学习方法，以获得自然行走训练模型期间的步态速度，从而预测28名肺部患者和10名无肺部疾病的受试者在6 MWT期间的自然行走速度和距离。我们还将我们的模型的准确性与流行的健身设备进行了比较。我们普遍训练的支持向量机模型在受控的6 MWT期间产生6 MWT距离，误差为3.23%，在自然自由行走期间为11.2%。此外，我们的模型在对五个受试者进行距离估计测试时获得了7.9%的误差，而在自然行走期间在健身设备中看到的误差为50-400%。
Mobile devices have the potential to continuously monitor health by collecting movement data including walking speed during natural walking. Natural walking is walking without artificial speed constraints present in both treadmill and nurse-assisted walking. Fitness trackers have become popular which record steps taken and distance, typically using a fixed stride length. While useful for everyday purposes, medical monitoring requires precise accuracy and testing on real patients with a scientifically valid measure. Walking speed is closely linked to morbidity in patients and widely used for medical assessment via measured walking. The 6-min walk test (6MWT) is a standard assessment for chronic obstructive pulmonary disease and congestive heart failure. Current generation smartphone hardware contains similar sensor chips as in medical devices and popular fitness devices. We developed a middleware software, MoveSense, which runs on standalone smartphones while providing comparable readings to medical accelerometers. We evaluate six machine learning methods to obtain gait speed during natural walking training models to predict natural walking speed and distance during a 6MWT with 28 pulmonary patients and ten subjects without pulmonary condition. We also compare our model's accuracy to popular fitness devices. Our universally trained support vector machine models produce 6MWT distance with 3.23% error during a controlled 6MWT and 11.2% during natural free walking. Furthermore, our model attains 7.9% error when tested on five subjects for distance estimation compared to the 50-400% error seen in fitness devices during natural walking.