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センサー支援のインテリジェントケアのための音声ベースの疼痛レベル分類

Andrew Y Lu1, Wei Lu2

  • 1Oyster River High School, Durham, NH 03824, USA.

Sensors (Basel, Switzerland)
|February 13, 2026
PubMed
まとめ

この研究は,音声分析を使用してリアルタイムで痛みを検知するための低コストのセンサー支援システムを導入しています. このフレームワークは,痛みのレベルを分類する上で高い精度を達成し,インテリジェントなヘルスケアシステムのための実用的な解決策を提供します.

キーワード:
コンボリューションニューラルネットワーク (CNN)MFCCCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCは,MFCCはアコースティックセンサの音響センサーです.ヘルスケア AI AI痛みのレベル分類 痛みのレベル分類スペクトルグラフ スペクトルグラフ スペクトルグラフゼロ・エフェスト技術とは,ゼロ・エフェスト技術です.

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科学分野:

  • バイオメディカルエンジニアリング
  • 医療における人工知能
  • シグナル処理 信号処理

背景:

  • インテリジェントなヘルスケアシステムは,スタッフ不足により,痛み評価の課題に直面しています.
  • 伝統的な痛みの検出方法は,コスト,アクセシビリティ,専門家のサポート要件に制限があります.
  • 音響センサーベースの疼痛検知は,リアルタイムモニタリングの有望な代替案です.

研究 の 目的:

  • 痛みのレベルをリアルタイムで分類するための軽量でセンサー支援のシステムを開発する.
  • 痛み検出のための声のスペクトル特性の有効性を調査する.
  • シミュレーションとハードウェアプロトタイプを使用してシステムを検証する.

主な方法:

  • 音声センサーを利用して音声信号を捕捉した.
  • 採用されたコンボリューションニューラルネットワーク (CNN) モデルは,声のスペクトル特性に訓練されました.
  • 3段階の痛みの分類方法を開発しました.
  • Jupiter NotebookのシミュレーションとRaspberry Piのハードウェアプロトタイプを通じてシステムを検証しました.

主要な成果:

  • 3つのレベルの痛み分類で平均72.74%の精度を達成しました.
  • 同様の痛みのレベルの粒度で,既存の方法を18.94-26.74%上回った.
  • 低コストのハードウェアプロトタイプ (<100 USD) で実証されたリアルタイム処理速度 (6-22s).
  • 様々な機械学習アルゴリズム (ANN,XGBoost,ランダムフォレスト,意思決定ツリー) の適用性を示しました.

結論:

  • 提案されているセンサー支援システムは,リアルタイムで痛みを検知するための効果的で手頃な価格のソリューションを提供します.
  • 機械学習,特にCNNと組み合わせた声のスペクトル分析は,痛みの評価のための有効な方法です.
  • このシステムは,インテリジェントなヘルスケアとアシストドリビング環境を向上させる可能性を秘めています.