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用于跑步步态分析的智能手表和鞋带式惯性测量单元的有限互换性

Limited Interchangeability of Smartwatches and Lace-Mounted IMUs for Running Gait Analysis.

作者信息

Meingast Theodor, Carrier Bryson, Melvin Amanda, Kozloff Kenneth M, DeJong Lempke Alexandra F, Lepley Adam S

机构信息

School of Kinesiology, University of Michigan, Ann Arbor, MI 48109, USA.

Department of Kinesiology and Nutrition Sciences, University of Nevada-Las Vegas, Las Vegas, NV 89154, USA.

出版信息

Sensors (Basel). 2025 Sep 5;25(17):5553. doi: 10.3390/s25175553.

Abstract

Spatiotemporal running metrics such as cadence, stride length (SL), and ground contact time (GCT) are important for assessing performance and injury risk. However, such metrics are traditionally assessed using laboratory-based tools that are often inaccessible in applied settings. Wearable devices including smartwatches and lace-mounted inertial measurement units (IMUs) offer promising alternatives, yet cross-device agreement in reporting spatiotemporal variables remains unclear. This study evaluated agreement between a commercial smartwatch and lace-mounted IMUs across varied distances and environments in 65 physically active adults (33 female/32 male, height: 171.0 ± 8.9 cm; weight: 70.9 ± 15.2 kg). Participants completed indoor and outdoor runs (2.5 km, 5 km, 10 km, 20 km) wearing both devices simultaneously. Average cadence demonstrated acceptable agreement (MAPE = 4.1%, CCC = 0.66) and supported equivalence, particularly among males, during outdoor conditions, and longer run distances. In contrast, peak cadence showed weak correlation (MAPE = 5.3%, CCC = 0.29), and SL and GCT demonstrated poor agreement (MAPE = 14.9-19.0%, CCC = 0.30-0.39) across all conditions. While average cadence may serve as a metric for cross-device comparisons, especially for males, and longer-distance outdoor runs, other spatiotemporal metrics demonstrated poor agreement, limiting interchangeability. Understanding device-specific capabilities is essential when interpreting wearable-derived gait data. Further validation using gold-standard tools is needed to support accurate and applied use of wearable technologies.

摘要

诸如步频、步幅(SL)和地面接触时间(GCT)等时空跑步指标对于评估运动表现和受伤风险很重要。然而,传统上这些指标是使用基于实验室的工具来评估的,而这些工具在实际应用场景中往往难以获取。包括智能手表和鞋带式惯性测量单元(IMU)在内的可穿戴设备提供了有前景的替代方案,但在报告时空变量方面跨设备的一致性仍不明确。本研究评估了一款商用智能手表和鞋带式IMU在65名身体活跃的成年人(33名女性/32名男性,身高:171.0±8.9厘米;体重:70.9±15.2千克)不同距离和环境下的一致性。参与者同时佩戴这两种设备完成室内和室外跑步(2.5千米、5千米、10千米、20千米)。平均步频显示出可接受的一致性(平均绝对百分比误差 = 4.1%,组内相关系数 = 0.66),并支持等效性,特别是在男性中、户外条件下以及较长跑步距离时。相比之下,峰值步频显示出弱相关性(平均绝对百分比误差 = 5.3%,组内相关系数 = 0.29),并且步幅和地面接触时间在所有条件下都显示出较差的一致性(平均绝对百分比误差 = 14.9 - 19.0%,组内相关系数 = 0.30 - 0.39)。虽然平均步频可以作为跨设备比较的一个指标,特别是对于男性和较长距离的户外跑步,但其他时空指标显示出较差的一致性,限制了互换性。在解释可穿戴设备得出的步态数据时,了解特定设备的功能至关重要。需要使用金标准工具进行进一步验证,以支持可穿戴技术的准确和实际应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d424/12431447/57971b3b6655/sensors-25-05553-g0A1.jpg

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