区分隐性弱足个体的穿戴式跌倒风险评估。

IF 5.2 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Zhen Song, Jianlin Ou, Shibin Wu, Lin Shu, Qihan Fu, Xiangmin Xu
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引用次数: 0

摘要

背景:基于传感器的技术已广泛应用于跌倒风险评估。为了增强模型的鲁棒性和可靠性,分析和讨论导致某些个体错误分类的因素至关重要,从而实现有目的和可解释的细化。方法:本研究确定了一种被称为“隐性弱足(RWF)”的异常步态模式,其特征是通过弱足特征空间观察到的弱足侧的不连续高风险步态。这种情况对跌倒风险评估模型的训练和性能产生负面影响。为了解决这个问题,我们提出了一种可训练的阈值方法来区分具有这种模式的个体,从而提高模型的泛化性能。我们对两个自建的数据集进行了可行性和消融性研究,并对两个已发表的步态相关帕金森病(PD)数据集进行了兼容性测试。结果:该方法在自定义指标和优化的自适应阈值的指导下,有效筛选了RWF个体。具体而言,经过精细适应,个体模型在增强数据集上的准确率分别达到87.5%和73.6%。与基线相比,所提出的两阶段模型表现出更好的性能,准确率为85.4%,灵敏度为87.5%。在PD数据集中,我们的方法减轻了低特征维度的潜在过拟合,准确率提高了4.7%。结论:我们的研究结果表明,该方法通过允许模型考虑步态模式的个体差异来增强模型的泛化,并作为质量控制的有效工具,有助于减少误诊。RWF步态模式的识别促进了相关研究和理论的联系,为进一步的研究提供了途径。未来的研究需要进一步探索这种步态模式的含义,并验证该方法的兼容性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wearable fall risk assessment by discriminating recessive weak foot individual.

Background: Sensor-based technologies have been widely used in fall risk assessment. To enhance the model's robustness and reliability, it is crucial to analyze and discuss the factors contributing to the misclassification of certain individuals, enabling purposeful and interpretable refinement.

Methods: This study identified an abnormal gait pattern termed "Recessive weak foot (RWF)," characterized by a discontinuous high-risk gait on the weak foot side, observed through weak foot feature space. This condition negatively affected the training and performance of fall risk assessment models. To address this, we proposed a trainable threshold method to discriminate individuals with this pattern, thereby enhancing the model's generalization performance. We conducted feasibility and ablation studies on two self-established datasets and tested the compatibility on two published gait-related Parkinson's disease (PD) datasets.

Results: Guided by a customized index and the optimized adaptive thresholds, our method effectively screened out the RWF individuals. Specifically, after fine adaptation, the individual-specific models could achieve accuracies of 87.5% and 73.6% on an enhanced dataset. Compared to the baseline, the proposed two-stage model demonstrated improved performance, with an accuracy of 85.4% and sensitivity of 87.5%. In PD dataset, our method mitigated potential overfitting from low feature dimensions, increasing accuracy by 4.7%.

Conclusions: Our results indicate the proposed method enhanced model generalization by allowing the model to account for individual differences in gait patterns and served as an effective tool for quality control, helping to reduce misdiagnosis. The identification of the RWF gait pattern prompted connections to related studies and theories, suggesting avenues for further research. Future investigations are needed to further explore the implications of this gait pattern and verify the method's compatibility.

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来源期刊
Journal of NeuroEngineering and Rehabilitation
Journal of NeuroEngineering and Rehabilitation 工程技术-工程:生物医学
CiteScore
9.60
自引率
3.90%
发文量
122
审稿时长
24 months
期刊介绍: Journal of NeuroEngineering and Rehabilitation considers manuscripts on all aspects of research that result from cross-fertilization of the fields of neuroscience, biomedical engineering, and physical medicine & rehabilitation.
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