# Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes
###### Abstract
The corresponding CNN operations can be regarded as the simple pairing operations, where the input sequences to each convolution layer can be separated into non-overlapping pairs. The operations for each pair are determined by the weights associated with the kernels. Because of the simplicity of the operations, a deep CNN based on PairNet algorithm can then be easily formed for high classification accuracy with low computation time as compared with the basic CNN. Although the PairNet has a simple structure, it may have large depth for full coverage of a single gesture. However, the classification accuracy may be saturated as the depth increases. One approach to further improve the performance is the employment of short-cut connections [27; 28; 29] for some layers. This may be beneficial for providing a good reference for effective training and inference at these layers [27]. The resulting network, termed Residual PairNet, has superior classification accuracy over the PairNet and basic CNN. Similar to the PairNet, the Residual PairNet has low computational complexities as compared with basic CNN. The performance of the feedforward algorithms can be further improved. This is based on the observation that sensory data produced by accelerator and gyroscope may possess both slow and rapid variations dependent on the hand movements. Consequently, the employment of inception modules [20; 30; 31; 32] consisting of different sizes of kernels at the same layer may be beneficial for efficient capturing of gesture features. Furthermore, while having superior classification accuracy, the PairNet with inception is able to lower the computation costs of the PairNet. This is because dimensionality reduction can be carried out in the inception modules for lowering the number of weights [30]. Similarly, the residual PairNet can also operate in conjunction with inception. The employment of short-cut connections and/or inception modules to PairNet provides flexibilitiesto the feedforward networks for varying requirements on classification accuracy, computation costs, and design complexities. To demonstrate the effectiveness of the proposed feedforward algorithms, a smart home system was built, where the home appliances can be controlled remotely by hand gestures. The sequences of hand gestures were acquired by smart phones, and were delivered to the embedded systems in the home appliances for gesture recognition. The trained neural network models were deployed in the embedded systems so that sequences of hand gestures can be recognized in real-time. Experimental results reveal that the proposed algorithms are able to carry out real-time gesture recognition on embedded devices with only limited computation capacity. Furthermore, they have superior classification accuracy over existing works. They are therefore well-suited for the implementation of light-weight smart human-computer interfaces where the only sensors used are for hand gesture recognition. The remaining parts of this paper are organized as follows. Section 2 reviews some advanced works for gesture recognition. Section 3 presents the proposed gesture recognition systems. The PairNet algorithm is studied in detail. The augmentation of short-cut connections and inception modules to the PairNet are also discussed. Experimental results of the proposed feedforward networks are then presented in Section 4. Finally, Section 5 includes some concluding remarks of this work.