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使用可穿戴传感器在功能活动中测量的加速度和方向的有效性和可靠性。

Validity and reliability of accelerations and orientations measured using wearable sensors during functional activities.

机构信息

School of Healthcare Sciences, College of Biomedical and Life Sciences, Cardiff University, Cardiff, CF14 4EP, UK.

出版信息

Sci Rep. 2022 Aug 26;12(1):14619. doi: 10.1038/s41598-022-18845-x.


DOI:10.1038/s41598-022-18845-x
PMID:36028523
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9417076/
Abstract

Wearable sensors may enable the assessment of movement in a real-world setting, but they are not yet a standard practice in the analysis of movement due to the unknown accuracy and reliability with respect to different functional activities. Here, we established the concurrent validity and test-retest reliability of accelerations and orientations measured using affordable novel sensors during squats, jumps, walking and stair ambulation. In this observational study, participants underwent three data collection sessions during one day. Accelerations and orientations from sacrum, thigh and shank were collected using these sensors and already validated gold-standard sensors as the criterion method. We assessed validity by comparing the similarity of signal waveforms with the Linear Fit Method and by comparing mean differences in range values with the Bland-Altman plots. Reliability was assessed by calculating interclass correlation coefficient and standard error of measurements of the range values. Concurrent validity was from fair to excellent in 91% of the cases for accelerations and in 84.4% for orientations. Test-retest reliability of accelerations was from fair to excellent in 97% of cases when the sensors were attached by a researcher, and in 84.4% of cases when the sensors were attached by participants. Test-retest reliability of orientations was from fair to excellent in 88.9% of cases when the sensors were attached by a researcher, and in 68.9% of cases when the sensors were attached by participants. In conclusion, the new affordable sensors provide accurate measures of accelerations and orientations during multiple functional activities in healthy adults. Reliability of the orientations may depend on the ability to replicate the same position of the sensor under test-retest conditions.

摘要

可穿戴传感器可以实现在真实环境下对运动的评估,但由于其在不同功能活动下的准确性和可靠性未知,它们尚未成为运动分析的标准实践。在这里,我们确定了在深蹲、跳跃、行走和上下楼梯时使用经济实惠的新型传感器测量的加速度和方向的同时效度和重测信度。在这项观察性研究中,参与者在一天内进行了三次数据采集。使用这些传感器和已经验证的黄金标准传感器作为标准方法,从骶骨、大腿和小腿收集加速度和方向。我们通过比较线性拟合方法的信号波形相似性以及比较范围值的平均差异的 Bland-Altman 图来评估有效性。通过计算范围值的组内相关系数和测量标准误差来评估可靠性。在 91%的情况下,加速度的同时效度为良到优,在 84.4%的情况下,方向的同时效度为良到优。当传感器由研究人员附着时,加速度的重测信度在 97%的情况下为良到优,当传感器由参与者附着时,加速度的重测信度在 84.4%的情况下为良到优。当传感器由研究人员附着时,方向的重测信度在 88.9%的情况下为良到优,当传感器由参与者附着时,方向的重测信度在 68.9%的情况下为良到优。总之,新的经济实惠的传感器可在健康成年人进行多项功能活动时提供准确的加速度和方向测量。方向的可靠性可能取决于在重测条件下复制传感器相同位置的能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/81e6e39d25b1/41598_2022_18845_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/25b5661ddfa7/41598_2022_18845_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/0efded111572/41598_2022_18845_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/360ab9b2634e/41598_2022_18845_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/efa480b65f7b/41598_2022_18845_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/97010e902288/41598_2022_18845_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/69c1918efe85/41598_2022_18845_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/12075df8ace9/41598_2022_18845_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/9e45154eda31/41598_2022_18845_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/81e6e39d25b1/41598_2022_18845_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/25b5661ddfa7/41598_2022_18845_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/0efded111572/41598_2022_18845_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/360ab9b2634e/41598_2022_18845_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/efa480b65f7b/41598_2022_18845_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/97010e902288/41598_2022_18845_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/69c1918efe85/41598_2022_18845_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/12075df8ace9/41598_2022_18845_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/9e45154eda31/41598_2022_18845_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4956/9418264/81e6e39d25b1/41598_2022_18845_Fig9_HTML.jpg

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[1]
Predicting Knee Joint Kinematics from Wearable Sensor Data in People with Knee Osteoarthritis and Clinical Considerations for Future Machine Learning Models.

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