# Hand Gesture Recognition on Edge Devices:
## 8 Efforts to Bring HGR to the Edge  
The survey [72] is the first to attempt a standardization of the definition of edge AI. To this end, it distributes the aspects of edge AI into four main sectors. The first one is fog computing (FC), mobile edge computing (MEC), micro data center (mDC), and cloudlets. FC describes the insertion of a field area network between sensors and the cloud. The field area network allows interconnection and \"intelligence\" close to the sensors and controls the communication with the cloud, while the sensors are connected to the field area network. The idea behind the MEC paradigm is to leverage the radio access network as a network to connect sensors close to the edge. mDC focuses on applications in the context of Industrial Internet of Things (IIoT). In case of cloudlets, several mobile devices would connect to create a network close to the device itself with increased computing power, only connecting to the cloud when required. Making an algorithm edge applicable always goes along with decreasing the memory footprint, lowering processing latency, and power consumption. In most cases, only inference is carried out on the network edge. That means the previously trained model is deployed on the edge to carry out predictions but no actual training takes place in the edge node. A possible way to leverage edge nodes for training is ensemble learning, where the training either takes place in sequential steps, each improving the previous model, or in parallel on different data sets, only combining model parameters to a final shared model with improved accuracy and robustness. But no algorithm based on ensemble learning was used by the considered sources for HGR. Tables 5 and 6 give a detailed comparison of all real-time systems discussed in this paper. All edge implementations are also real-time systems. The column \"Effort for edge applicability\" states which steps were taken to speed up the system and lower resource requirements. Not all of these efforts lead to edge applicability. The approach of [40] still requires a GPU. But the processing requirements highly depend on the type of sensor used and the goal of the paper. While some see edge applicability as their goal, others focus on robustness. Furthermore, hand tracking is significantly more processing intense than classification. Specific optimized implementations are presented in [38; 43; 63; 73]. Some of these even run on FPGAs, as in [38; 40; 74] or in C/C++ as in [75]. Through specific implementation, the hardware can be used optimally. Therefore, this approach has the highest potential. However, the implementations cannot be used for different hardware, tasks, and models. Another possibility to make a model edge applicable is quantization, Refs. [56; 76], or lowering the sampling frequency or frame rate, Refs. [39; 44; 69]. Lowering model size while keeping high accuracy is a challenge that the authors of [77; 78; 79; 80] made the main topic of their paper. Researchers and companies are making an effort to facilitate inference at the edge by offering optimization methods to compress ML models. Ref. [76] uses Model Optimizer Open VINO, Ref. [81] relies on LiteRT (formerly TensorFlow Lite), and [20] use TensorFlow Lite Micro. It is worth giving more detailed information about the realization of edge applicability of the following systems. The authors of [39] propose a pulsed single-frequency ultrasound-based solution for object localization with one sender and eight receivers. The Texas Instruments TMS320F28379D microcontroller is used to implement the entire processing pipeline. Bandpass filters, window assessment, and thresholds are used in the processing. Beamforming and DOA estimation lead to successful localization of several objects. Another ultrasound-based HGR that can be trained in a neuroshield is presented in [82]. It requires one sender and one receiver and is based on an ultrasound pulse of a single frequency.