基于皮肤佩戴式传感器的机器学习步速估计：从健康对照到多发性硬化患者

数据集获取（Figshare）

• 源站标题：A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis
• DOI：10.1371/journal.pone.0178366
• 发布日期：2017-06-01
• 文件数：4 个
• 文件总体积：5.17 MB
• 源站页面：https://plos.figshare.com/articles/dataset/A_machine_learning_approach_for_gait_speed_estimation_using_skin-mounted_wearable_sensors_From_healthy_controls_to_individuals_with_multiple_sclerosis/5059843

命令行下载

curl -L -o "S1 Appendix.docx" "https://ndownloader.figshare.com/files/8574931"
curl -L -o "S1 File.zip" "https://ndownloader.figshare.com/files/8574955"
curl -L -o "S2 File.zip" "https://ndownloader.figshare.com/files/8574964"
curl -L -o "S3 File.zip" "https://ndownloader.figshare.com/files/8574970"

文件清单

• S1 Appendix.docx（55.49 KB）
• S1 File.zip（3.93 MB）
• S2 File.zip（1.15 MB）
• S3 File.zip（37.22 KB）

文件总体积 5.17 MB（源站 API 实测）。

数据说明

Gait speed is a powerful clinical marker for mobility impairment in patients suffering from neurological disorders. However, assessment of gait speed in coordination with delivery of comprehensive care is usually constrained to clinical environments and is often limited due to mounting demands on the availability of trained clinical staff. These limitations in assessment design could give rise to poor ecological validity and limited ability to tailor interventions to individual patients. Recent advances in wearable sensor technologies have fostered the development of new methods for monitoring parameters that characterize mobility impairment, such as gait speed, outside the clinic, and therefore address many of the limitations associated with clinical assessments. However, these methods are often validated using normal gait patterns; and extending their utility to subjects with gait impairments continues to be a challenge. In this paper, we present a machine learning method for estimating gait speed using a configurable array of skin-mounted, conformal accelerometers. We establish the accuracy of this technique on treadmill walking data from subjects with normal gait patterns and subjects with multiple sclerosis-induced gait impairments. For subjects with normal gait, the best performing model systematically overestimates speed by only 0.01 m/s, detects changes in speed to within less than 1%, and achieves a root-mean-square-error of 0.12 m/s. Extending these models trained on normal gait to subjects with gait impairments yields only minor changes in model performance. For example, for subjects with gait impairments, the best performing model systematically overestimates speed by 0.01 m/s, quantifies changes in speed to within 1%, and achieves a root-mean-square-error of 0.14 m/s. Additional analyses demonstrate that there is no correlation between gait speed estimation error and impairment severity, and that the estimated speeds maintain the clinical significance of ground truth speed in this population. These results support the use of wearable accelerometer arrays for estimating walking speed in normal subjects and their extension to MS patient cohorts with g

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0178366（source: https://plos.figshare.com/articles/dataset/A_machine_learning_approach_for_gait_speed_estimation_using_skin-mounted_wearable_sensors_From_healthy_controls_to_individuals_with_multiple_sclerosis/5059843）。