這篇論文提出一個可以辨識連續手勢的系統，輸入是一條三軸加速度計 (3-axis Accelerometer) 和三軸陀螺儀 (3-axis Gyroscope) 所組成的時間序列。此研究所提出的辨識演算法——PairNet，是摺積式類神經網路 (Convolutional Neural Networks) 的變形。和一般摺積式類神經網路不同的點有三個：在摺積層中的過濾片 (Kernel) 使用的大小為 2×1。步伐 (Stride) 大小從常用的 1 改為過濾片的大小 (在這裡即是 2)。在計算完最後的摺積層後，加入了全域平均池化層 (Global Average Pooling) ，使其對整體網路所產生的輸出具備聚合 (Ensembling) 的效果。實驗使用了 HTC One M9 和 Google Daydream 這兩種感測器 (Sensors) 所收集成的資料集，手勢個數分別為 11 種和 14 種。PairNet 在兩種資料集的連續手勢測試中，分別取得了 97.81% 和 99.38% 的準確率，優於長短期記憶遞迴類神經網路和一般摺積式類神經網路。

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