# Hand Gesture Recognition on Edge Devices:
## 1 Introduction  
This paper specifically deals with the recognition of hand gestures captured by a static observer. Nevertheless, it touches upon similar research areas in order to contextualize the topicamong related research topics, as well as to provide information about the origins of some of the various algorithms for successful execution of this task. Today, an increasing number of electronic devices find their way into our daily lives. Thus, there is an increased need for novel, convenient input. The first implementations of hand gesture recognition (HGR) date back to 1993 as the paper [1] shows. Hand gestures are a common way for people to communicate. Therefore, HGR is a human-machine interface(HMI) that feels natural. There are countless application areas for touch-free interfaces. The transference of germs through vending machines or interactions with Internet of Things (IoT) devices is eliminated. A further application area is the interface with machines in sterile environments. Moreover, the advent of smart watches and virtual reality headsets demands novel input methods, as these devices have a very small display or none at all. Applications related to HGR include people counting, crowd monitoring, and surveillance of a single person. In single-person monitoring, recording very fine-grained movements is possible. This can consist of tracking movements of limbs, hand gestures, and finger gestures, or even facial recognition and vital sensing. Environment scanning, object recognition, and distance calculation are similar applications of HGR. The goal of researching such topics can be the classification of dynamic movements or static surroundings. Many different research areas are related to hand gesture recognition. Some of them are depicted in Figure 1.  
A differentiation needs to be made between hand gesture or object detection and recognition. Detection means the general existence of an object in the field of view of a sensor, whereas recognition deals with the distinction of a specific target or motion compared to others. Further, either humans or objects can be the classification target. Environment scanning encompasses both possibilities and additionally includes distance measurement and obstacle detection. Moreover, the target and/or the sensor(s) can be either moving or static, the most complicated case being when both are changing their position. In all cases, the target has several characteristics, such as size, surface, and velocity. Additionally, the sensor itself may be moving, as in environment scanning for moving robots. Upon researching the systems presented in the scope of this paper, we found that HGR systems usually consist of several common parts. The first one is a sensor type that constitutes the input method. At a subsequent stage, a pre-processing unit is used to gather sensor data and do initial preliminary processing. Pre-processing can for example comprise analog digital conversion, Fourier transform, and segmentation. In cases where classification is based on ML, data acquisition and curation are needed. Usually, pre-processing is required to prepare the data for gesture detection and classification. In a final step, the result is displayed in some form. Depending on the complexity and maturity of the technology, a single hardware can be used for only one of these steps up to all of them. More detailed comparisons of the systems are given in Figure 2, which shows the processing flow for HGR independent of the input sensor type. Possible sensor types range from ultrasound to RGB camera and hybrid systems. A detailed description of vision, radar, WiFi, LTE, 4G, 5G, and ultrasound-based technologies, including their advantages and drawbacks, is given in Section 4. Figure 1: Applications of gesture recognition and similar systems. Despite countless improvements in HGR systems to this day, many open research questions remain. Since the beginning of gesture recognition, many sensor types beyond cameras were considered.