Combining different wearable devices to assess gait speed in real-world setting
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This dataset was created in the context of the EU project Tolife, which aims at validating an artificial intelligence solution to process daily life patient data captured by non-obtrusive sensors to enable personalized treatment, assessment of health outcomes and improved quality of life in chronic obstructive pulonary disease patients. In this study, we utilized the Tolife sensor platform, comprising a Samsung Galaxy A14 smartphone, a Samsung Galaxy Watch 5, and custom smart shoes. These devices gathered inertial sensor data, including accelerometers, gyroscopes, and pressure sensors, for gait analysis. The smart shoes feature integrated electronics, including inertial measurement units and Bluetooth connectivity, along with pressure sensors in the insole. Reference measurements for gait speed were obtained using the Xsens Awinda inertial motion tracker with MVN Analyze software. Twenty participants underwent a modified Six Minute-Walking-Test (6MWT) at three different paces: slow, medium, and fast, with consistent device placement. The dataset is organized into folders storing wearable device data and reference measurements, with individual .csv files for each sensor and test pace. The dataset is structured in two folders: the first stores data acquired from the wearable devices, while the second one contains the reference data. Within the folders, the organization follows the structure outlined in the "readme" file. The distances covered by subjects during the tests are stored in the “smwd.csv” file. Here, each rows represent a subject, and the columns the test’s paces: slow, medium and fast. When using this dataset, please cite "Zanoletti, M.; Bufano, P.; Bossi, F.; Di Rienzo, F.; Marinai, C.; Rho, G.; Vallati, C.; Carbonaro, N.; Greco, A.; Laurino, M.; et al. Combining Different Wearable Devices to Assess Gait Speed in Real-World Settings. Sensors 2024, 24, 3205. https://doi.org/10.3390/s24103205"
本数据集依托欧盟Tolife项目构建，该项目旨在验证一套人工智能解决方案，用于处理非侵入式传感器采集的患者日常数据，以实现慢性阻塞性肺疾病（chronic obstructive pulmonary disease）患者的个性化治疗、健康结局评估，并提升其生活质量。本研究采用了Tolife传感器平台，该平台包含三星Galaxy A14智能手机、三星Galaxy Watch 5智能手表以及定制智能鞋。上述设备可采集惯性传感器数据，涵盖加速度计、陀螺仪与压力传感器，用于步态分析。该定制智能鞋集成了电子组件，包括惯性测量单元与蓝牙模块，并在鞋垫中搭载了压力传感器。步态速度的参考测量值通过搭载MVN Analyze软件的Xsens Awinda惯性运动追踪系统获取。20名受试者按照慢、中、快三种不同步速完成了改良版六分钟步行试验（6MWT），且设备佩戴位置保持一致。本数据集按文件夹分类存储可穿戴设备数据与参考测量数据，每个传感器与每种试验步速对应单独的.csv格式文件。本数据集分为两个文件夹：第一个文件夹存储可穿戴设备采集的数据，第二个文件夹则存放参考测量数据。文件夹内的组织格式遵循"readme"文件中说明的结构。受试者在试验中行走的距离存储于"smwd.csv"文件中。该文件中，每一行对应一名受试者，每一列对应一种试验步速：慢、中、快。使用本数据集时，请引用以下文献：Zanoletti, M.; Bufano, P.; Bossi, F.; Di Rienzo, F.; Marinai, C.; Rho, G.; Vallati, C.; Carbonaro, N.; Greco, A.; Laurino, M.; et al. Combining Different Wearable Devices to Assess Gait Speed in Real-World Settings. Sensors 2024, 24, 3205. https://doi.org/10.3390/s24103205
