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関連する概念動画

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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Updated: Sep 9, 2025

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
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効率的で説明可能なストレス検出のための軽量で解釈可能な機械学習モデルを調査する

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
まとめ
この要約は機械生成です。

軽量な機械学習モデルは,最小心拍数変動 (HRV) 機能を使用してストレスを正確に検出します. k-Nearest Neighbors (k-NN) モデルは99.3%の精度を達成し,リアルタイム IoT アプリケーションで効率的であることが証明されました.

キーワード:
IoTデバイスMLモデル説明可能なAI健康についてストレス検出

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科学分野:

  • コンピューティングインテリジェンス
  • バイオメディカル信号処理
  • 医療のための機械学習

背景:

  • 長く続くストレスは 精神的・身体的健康に悪影響を及ぼします
  • 心拍数変動 (HRV) は,ストレス測定の鍵となる指標です.
  • 機械学習 (ML) の限られたHRV機能を使用したストレスの正確な検出は困難です.

研究 の 目的:

  • 最小限のHRV機能を使用して,ストレスを検出するための計算効率の良い,軽量なMLモデルを開発する.
  • 物事のインターネット (IoT) の展開に適したリアルタイム・ストレスの監視を可能にします.
  • モデルパフォーマンスを評価し,実用的な応用のために解釈できるようにする.

主な方法:

  • SWELL-KWのデータセットをモデルトレーニングと評価に活用した.
  • MLモデルの効率的な機能選択とハイパーパラメータチューニングを実装します.
  • k-近隣 (k-NN) と決定樹を含む軽量モデルを開発し,比較した.

主要な成果:

  • 軽量なモデルは,計算上の要求を減らすことで,競争力のある精度を達成しました.
  • k-NNアルゴリズムは,3つのHRV機能のみで99.3%の精度を達成し,優れたパフォーマンスを示しました.
  • 最高のk-NNモデルはNVIDIA Jetson Orin Nanoエッジデバイスで99.26%の精度を保ち,31秒でトレーニングしました.

結論:

  • 軽量なMLモデル,特にk-NNは,HRVからの正確で効率的なストレス検出に有効です.
  • 提案されたアプローチは,リソースが限られたIoT環境におけるリアルタイムストレスの監視に適しています.
  • 局所的に解釈可能なモデルアグノスティックな説明は,MLベースのストレス検出の理解を高めます.