# Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes
## 2 Related Works  
VBR approaches can be separated into two classes: data-driven and model-based. The data-driven methods [1; 2; 3; 4] are based on the appearance of gesture images. The gestures are classified by relating the appearance of any gesture to the appearance of pre-defined gestures. Basic model-based methods [33] can be adopted for 3D hand tracking/recognition and 3D gesture estimation. Hand model fitting operations are usually required for the tracking of hand motion. As cameras are required for VBR approaches, computational complexities for these approaches may be high.  
Many existing SBR methods are data-driven. A common feature of some SBR-based techniques is that they are based on wearable sensors such as PPG sensors [9], flex sensors [10], and EMG sensors [11] for attaining high recognition accuracy. However, the systems with wearable sensors demand users to take extra devices for gesture recognition. These techniques may then be intrusive, because the devices sometimes are obstructive and inconvenient for gesture actions. This could undermine the quality of user experience.  
An alternative to wearable sensors is based on RFIDs for the location-sensing of hand gestures. The techniques in [12] carry out the tracking of motion patterns of RFID tags for gesture recognition. Although high accuracy can be achieved, users are still required to wear tags. To alleviate user intrusiveness, the RFID tags and readers were deployed in a fixed location in the study [13]. The recognition is then based on the fact that when gestures are performed in front of tags, the movement information of the gestures can be extracted by the resulting phases of the RFID signals. Although there is no demand for wearing sensors, gesture recognition can only take place in locations where RFID tags and readers are properly deployed.  
Another approach for gesture recognition with no user intrusiveness is based on the CSI information from commercial WiFi. It is known that fluctuation of WiFi signals can be observed by human actions. Variation statics such as variance and correlation coefficients [14] have been found to be beneficial for the detection of human activities. However, the detection performance may be limited by the random noises and/or WiFi channel variations. Furthermore, it may be difficult to carry out accurate gesture classifications from the simple statics. Approaches based on deep learning in [15; 16] are proposed to solve the problem. By the incorporation of RNN and its variants such as LSTM, the features of gestures can be effectively extracted, and accurate recognition can be achieved at the presence of noises. However, similar to their RFID counterparts [12; 13], the performance of WiFi CSI-based techniques may be dependent on the deployments of WiFi transmitters and receivers in an indoor environment.  
As compared with existing SBR-based algorithms, the proposed algorithms have a number of advantages. The first is that they are not user intrusive. Sensors commonly available in smart phones such as accelerometers and gyroscopes are adopted for gesture recognition. Therefore, only a single smart phone is needed for gesture recognition without additional wearable sensors and/or devices. Furthermore, the proposed algorithms provide higher flexibilities for the deployment of gesture recognition systems as compared with the existing RFID and WiFi techniques. The acquisition of sensory data is performed in the smart phone locally from its accelerometers and gyroscopes. The acquired sensory data can be delivered to any external devices accessible by Internet for gesture inference. Therefore, in the proposed algorithms, gesture recognition can take place in either indoor or outdoor environments, wherever Internet access is available.