穿戴式的跌倒风险评估是通过歧视衰退弱脚的个人来进行的
Zhen Song1,2, Jianlin Ou3, Shibin Wu1
1School of Microelectronics, and School of EIE, South China University of Technology, Guangzhou, 510641, China.
Journal of neuroengineering and rehabilitation
|March 21, 2025
概括
这项研究确定了一种"衰退性弱脚"步态模式,影响了跌倒风险模型. 一种新的可训练值方法通过选具有这种异常步行的个体来提高模型的准确性和概括性.
科学领域:
- 生物医学工程 生物医学工程
- 步态分析 步态分析
- 医疗保健中的机器学习
背景情况:
- 基于传感器的技术在跌倒风险评估中占主导地位.
- 模型的稳定性和可靠性需要分析错误分类因素.
- 可解释的精细化对于准确预测下降风险至关重要.
研究的目的:
- 识别和解决影响跌倒风险模型的异常步行模式.
- 开发一种可训练的值方法,以歧视这种步行模式的个体.
- 提高跌倒风险评估模型的概括性.
主要方法:
- 标识 识别 识别 识别 识别
主要成果:
- 拟议的方法有效地选了具有RWF步态模式的个体.
- 经过调整后,个别特定模型获得了高准确度 (87.5%,73.6%).
- 两阶段模型提高了性能 (85.4%的准确度,87.5%的灵敏度) 并减轻了PD数据集的过度匹配.
结论:
- 该方法通过适应个人步态差异来增强模型的概括性.
- 它作为一个有效的质量控制工具,减少误诊.
- 需要进一步的研究来探索RWF步态的影响和方法兼容性.
相关概念视频
Wald-Wolfowitz Runs Test I
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
The test works...
Wald-Wolfowitz Runs Test II
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
First Derivative Test: Problem Solving
Imagine an asset price that crashes to a low point, rebounds sharply as bargain-hunters step in, and then gradually declines. Such behavior can be modeled with a smooth function whose turning points represent locally overvalued and undervalued regions. A convenient example that captures rebound followed by decay is:The high and low points of this curve are identified using the first derivative test, which determines where the function changes from increasing to decreasing or vice versa. To...


