Sensor-Based Continuous Hand Gesture Recognition by Long Short-Term Memory
This work addresses gesture recognition for human-computer interaction, but it is incremental as it applies an existing LSTM method to sensor data.
The authors tackled continuous hand gesture recognition using sensor data from accelerometers and gyroscopes, achieving robust and accurate results with a prototype smartphone system.
This article aims to present a novel sensor-based continuous hand gesture recognition algorithm by long short-term memory (LSTM). Only the basic accelerators and/or gyroscopes are required by the algorithm. Given a sequence of input sensory data, a many-to-many LSTM scheme is adopted to produce an output path. A maximum a posteriori estimation is then carried out based on the observed path to obtain the final classification results. A prototype system based on smartphones has been implemented for the performance evaluation. Experimental results show that the proposed algorithm is an effective alternative for robust and accurate hand-gesture recognition.