This article aims to present a novel sensor-based continuous hand gesture recognition algorithm by long shortterm 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. Index Terms-Sensor applications, continuous hand gesture recognition, human machine interface, long short-term memory (LSTM).

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