手背: 透過手背之手勢辨識研究

Abstract

在這篇論文裡,我們探索透過手背來作為感測手勢的來源,其干擾 遠低於以手套形式作為感測手勢的方法以及提供比手腕和手肘作為來 源更為精準的辨識。我們的裝置利用應變規陣列黏貼於手背表面,並 利用機器學習相關技術來辨識多樣的手勢。 為了更加了解手勢辨識之正確度以及手背上不同感測位置的影響, 我們安排共計十位使用者實施使用者研究。實驗結果顯示出:藉由將 感測器所讀取之數值圖像化後,跨使用者的結果差異極大,而相同的 使用者則相似。對於跨使用者做出 16 個手勢的系統而言,系統辨識 之正確度落於 27.4%,而對於單一使用者而言,同樣的系統則能達到 95.8%的正確率。另一項實驗結果,則顯示以橫列為基礎的排列方式, 感測陣列最適合黏附於坐落在 MCP(手指與手掌間的關節) 至手腕頂端 之間 1/8 到 1/4 的位置。

In this paper, we explore using the back of hands for sensing hand ges- tures, which interferes less than glove-based approaches and provides better recognition than sensing at wrists and forearms. Our prototype, BackHand, uses an array of strain gauge sensors affixed to the back of hands, and applies machine learning techniques to recognize a variety of hand gestures. We conducted a user study with 10 participants to better understand ges- ture recognition accuracy and the effects of sensing locations. Results showed that sensor reading patterns differ significantly across users, but are consis- tent for the same user. The leave-one-user-out accuracy is low at an average of 27.4%, but reaches 95.8% average accuracy for 16 popular hand gestures when personalized for each participant. The most promising location spans the 1/8˷1/4 area between the metacarpophalangeal joints (MCP, the knuckles between the hand and fingers) and the head of ulna (tip of the wrist).