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利用健身追踪器数据克服远程肌肉骨骼监测中压力鞋垫磨损时间的挑战。

Using Fitness Tracker Data to Overcome Pressure Insole Wear Time Challenges for Remote Musculoskeletal Monitoring.

作者信息

Nurse Cameron A, Rodzak Katherine M, Volgyesi Peter, Noehren Brian, Zelik Karl E

机构信息

Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37212, USA.

Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN 37212, USA.

出版信息

Sensors (Basel). 2024 Dec 3;24(23):7717. doi: 10.3390/s24237717.

Abstract

Tibia shaft fractures are common lower extremity fractures that can require surgery and rehabilitation. However, patient recovery is often poor, partly due to clinicians' inability to monitor bone loading, which is critical to stimulating healing. We envision a future of patient care that includes at-home monitoring of tibia loading using pressure-sensing insoles. However, one issue is missing portions of daily loading due to limited insole wear time (e.g., not wearing shoes all day). Here, we introduce a method for overcoming this issue with a wrist-worn fitness tracker that can be worn all day. We developed a model to estimate tibia loading from fitness tracker data and evaluated its accuracy during 10-h remote data collections ( = 8). We found that a fitness tracker, with trained and calibrated models, could effectively supplement insole-based estimates of bone loading. Fitness tracker-based estimates of loading stimulus-the minute-by-minute weighted impulse of tibia loading-showed a strong fit relative to insole-based estimates (R = 0.74). However, insoles needed to be worn for a minimum amount of time for accurate estimates. We found daily loading stimulus errors less than 5% when insoles were worn at least 25% of the day. These findings suggest that a multi-sensor approach-where insoles are worn intermittently and a fitness tracker is worn continuously throughout the day-could be a viable strategy for long-term, remote monitoring of tibia loading in daily life.

摘要

胫骨干骨折是常见的下肢骨折,可能需要手术和康复治疗。然而,患者的恢复情况往往不佳,部分原因是临床医生无法监测对促进愈合至关重要的骨负荷。我们设想了一个患者护理的未来,其中包括使用压力感应鞋垫在家中监测胫骨负荷。然而,一个问题是由于鞋垫佩戴时间有限(例如,并非整天都穿鞋),会遗漏部分日常负荷。在此,我们介绍一种使用可全天佩戴的腕部健身追踪器来克服这个问题的方法。我们开发了一个模型,用于从健身追踪器数据中估计胫骨负荷,并在10小时的远程数据收集期间( = 8)评估其准确性。我们发现,配备经过训练和校准的模型的健身追踪器可以有效地补充基于鞋垫的骨负荷估计。基于健身追踪器的负荷刺激估计——胫骨负荷的逐分钟加权冲量——与基于鞋垫的估计显示出很强的拟合度(R = 0.74)。然而,鞋垫需要佩戴最少的时间才能进行准确估计。我们发现,当鞋垫每天佩戴至少25%的时间时,每日负荷刺激误差小于5%。这些发现表明,一种多传感器方法——即间歇性佩戴鞋垫并全天持续佩戴健身追踪器——可能是在日常生活中对胫骨负荷进行长期远程监测的可行策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd9a/11644998/7f8a38aa1ab9/sensors-24-07717-g001.jpg

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