Airwriting: a wearable handwriting recognition system

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• 作者：Christoph Amma (1)
  Marcus Georgi (1)
  Tanja Schultz (1)
  
• 关键词：Handwriting recognition ; Wearable computing ; Gesture recognition ; Inertial sensors ; Hidden Markov models
• 刊名：Personal and Ubiquitous Computing
• 出版年：2014
• 出版时间：January 2014
• 年：2014
• 卷：18
• 期：1
• 页码：191-203
• 全文大小：929 KB
• 作者单位：Christoph Amma (1)
  Marcus Georgi (1)
  Tanja Schultz (1)
  
  1. Cognitive Systems Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
  
• ISSN：1617-4917

文摘

We present a wearable input system which enables interaction through 3D handwriting recognition. Users can write text in the air as if they were using an imaginary blackboard. The handwriting gestures are captured wirelessly by motion sensors applying accelerometers and gyroscopes which are attached to the back of the hand. We propose a two-stage approach for spotting and recognition of handwriting gestures. The spotting stage uses a support vector machine to identify those data segments which contain handwriting. The recognition stage uses hidden Markov models (HMMs) to generate a text representation from the motion sensor data. Individual characters are modeled by HMMs and concatenated to word models. Our system can continuously recognize arbitrary sentences, based on a freely definable vocabulary. A statistical language model is used to enhance recognition performance and to restrict the search space. We show that continuous gesture recognition with inertial sensors is feasible for gesture vocabularies that are several orders of magnitude larger than traditional vocabularies for known systems. In a first experiment, we evaluate the spotting algorithm on a realistic data set including everyday activities. In a second experiment, we report the results from a nine-user experiment on handwritten sentence recognition. Finally, we evaluate the end-to-end system on a small but realistic data set.