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相关概念视频

Applications of Stress01:04

Applications of Stress

400
Consider a structure made of a boom and a rod designed to support a load. These two components are connected by a pin and stabilized by brackets and pins. The boom and the rod are detached from their supports to assess the different stresses imposed on this structure, and a free-body diagram is drawn. Then, all the forces applied, including the load acting on the structure, are identified. The reaction forces exerted on both the boom and the rod are computed using the equilibrium equations.
The...
400
Physiological Foundation of Stress01:24

Physiological Foundation of Stress

157
Stress triggers a coordinated physiological response involving the sympathetic nervous system (SNS) and the hypothalamic-pituitary-adrenal (HPA) axis. This dual activation ensures that the body is prepared for both immediate and prolonged stress management. The process begins with the perception of a stressor. This initial phase activates the SNS, leading to the rapid release of adrenaline (epinephrine) from the adrenal glands.
Role of the Sympathetic Nervous System
Adrenaline triggers the...
157
Psychological Responses to Stress01:20

Psychological Responses to Stress

98
Psychological responses to stress encompass the various cognitive and emotional reactions individuals experience when faced with challenging or threatening situations, such as a job loss. Prolonged exposure to stressors can disturb emotional balance, increasing negative emotions (e.g., anxiety and sadness) and diminishing positive emotions (e.g., joy and satisfaction). These persistent emotional shifts are associated with an increased risk of both physical illness and mental health issues, such...
98
Introduction to Stress and Lifestyle01:27

Introduction to Stress and Lifestyle

177
Stress is a multifaceted response to events perceived as challenging or threatening, highlighting physical, emotional, cognitive, and behavioral reactions. Physically, stress can lead to fatigue, sleep disruptions, and various health issues such as frequent colds, chest pains, and nausea. Emotionally, it can manifest as anxiety, depression, irritability, and anger triggered by both minor and major life events. Cognitively, it may result in difficulty in concentration, memory, and...
177
Components of Stress01:23

Components of Stress

275
Stress analysis under multiple loading conditions is intricate, necessitating a comprehensive grasp of normal and shearing stresses. Consider a small cube at point O, subjected to stress on all six faces, visible or not. Normal stress components σx, σy, σz act perpendicularly to the x, y, and z axes. Shearing stress components τxy and τxz are exerted on faces perpendicular to these axes.
Interestingly, the hidden cube faces also experience these stresses, equal and...
275
Stress Concentrations01:24

Stress Concentrations

374
Stress concentration is when stress intensifies near discontinuities such as holes or abrupt cross-sectional changes in a structural member. This localized stress can often surpass the average stress within the member. The stress distribution in flat bars, either with a circular hole or varying widths connected by fillets, can be determined experimentally using a photoelastic method. The results are based on ratios of geometric parameters like the ratio of the hole's radius to the smaller...
374

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相关实验视频

Updated: Sep 9, 2025

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研究轻量级和可解释的机器学习模型以有效和可解释的压力检测

Debasish Ghose1, Ayan Chatterjee2, Indika A M Balapuwaduge3

  • 1School of Economics, Innovation, and Technology, Kristiania University College, Bergen, Norway.

Frontiers in digital health
|August 29, 2025
PubMed
概括

轻量级机器学习模型使用最小心率变化 (HRV) 特性准确地检测压力. k-最近邻居 (k-NN) 模型实现了99.3%的准确性,证明了对实时物联网应用的效率.

关键词:
物联网设备ML模型可以解释的AI健康问题压力检测

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科学领域:

  • 计算智能
  • 生物医学信号处理
  • 医疗保健中的机器学习

背景情况:

  • 长期的压力会对精神和身体健康产生负面影响.
  • 心率变化 (HRV) 是压力测量的关键指标.
  • 使用有限的HRV功能和机器学习 (ML) 进行精确的应力检测是具有挑战性的.

研究的目的:

  • 使用最小HRV特征开发计算效率高,轻量级的ML模型进行应力检测.
  • 实现适合物联网 (IoT) 部署的实时压力监控.
  • 评估模型的性能和可用于实际应用的解释性.

主要方法:

  • 使用SWELL-KW数据集进行模型培训和评估.
  • 实现了ML模型的高效特征选择和超参数调整.
  • 开发和比较轻量级模型,包括k-最近邻居 (k-NN) 和决策树.

主要成果:

  • 轻量级模型以降低计算需求实现了竞争性的准确性.
  • 该k-NN算法表现出卓越的性能,只有三个HRV特征达到99.3%的准确性.
  • 最好的k-NN模型在NVIDIA Jetson Orin Nano边缘设备上保持了99.26%的准确性,训练时间为31秒.

结论:

  • 轻量级ML模型,特别是k-NN,对于HRV的精确和高效的应力检测是有效的.
  • 拟议的方法适用于资源有限的物联网环境中的实时压力监测.
  • 局部可解释的模型不可知解释增强了基于ML的压力检测的理解.