FPGA実装を指向した未学習クラス推定混合ガウス型識別モデルと複合動作の識別

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  • 柏木 僚太
    横浜国立大学大学院環境情報学府
  • 迎田 隆幸
    神奈川県立産業技術総合研究所 横浜国立大学大学院環境情報研究院
  • 島 圭介
    横浜国立大学大学院環境情報研究院

書誌事項

タイトル別名
  • A Gaussian Mixture Classification Model with Unlearned-class Detection for FPGA Implementation and Application for Classification of Combined Motions
  • FPGA ジッソウ オ シコウ シタ ミガクシュウ クラス スイテイ コンゴウ ガウスガタ シキベツ モデル ト フクゴウ ドウサ ノ シキベツ

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<p>The electromyogram (EMG) signal generated by muscle contraction has been widely utilized for motion estimation of arms and fingers. To develop a myoelectric prosthetic hand that has high general versatility and safeness, a classifier that can consider complex forearm motions and motions that are not assumed during training, is required. However, hardware implementation of complex classifiers that has high classification performance is difficult. To tackle these problems, this paper proposed a novel probabilistic neural network that can be implemented in FPGA (Field Programmable Gate Array), and it was applied to an EMG-based human-machine interface system. The proposed neural network includes two types of probability density functions optimized for hardware implementation and enabled the execution of multi-class discrimination considering the unlearned class on the FPGA. Furthermore, by combining a forearm motion classifier and a hand motion classifier, the consideration of compound motions consisting of multiple hand gestures can be achieved. In experiments, the results showed that the proposed method can be implemented on FPGAs, and demonstrated that it can achieve highly accurate motion classification for compound motions and unlearned motions.</p>

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