ウェアラブルセンサとRNNによるオンラインジェスチャー認識
Online Human Gesture Recognition using Recurrent Neural Networks and Wearable Sensors
三軸加速度計のデータからRNNの確率的分類器と連続検出機構を用いて、精度・再現率と即時応答性を両立したオンラインジェスチャー認識システムSLOTHを提案した。
著者: Alessandro Carfi, Carola Motolese, Barbara Bruno, Fulvio Mastrogiovanni
分類: cs.RO
原文アブストラクト
Gestures are a natural communication modality for humans. The ability to interpret gestures is fundamental for robots aiming to naturally interact with humans. Wearable sensors are promising to monitor human activity, in particular the usage of triaxial accelerometers for gesture recognition have been explored. Despite this, the state of the art presents lack of systems for reliable online gesture recognition using accelerometer data. The article proposes SLOTH, an architecture for online gesture recognition, based on a wearable triaxial accelerometer, a Recurrent Neural Network (RNN) probabilistic classifier and a procedure for continuous gesture detection, relying on modelling gesture probabilities, that guarantees (i) good recognition results in terms of precision and recall, (ii) immediate system reactivity.
