# Hand Gesture Recognition on Edge Devices:
## 4 Sensor Technologies
### Sensor Technology Specific Comparison
#### 4.6.5 Comparison of Ultrasound Beamforming Solutions  
The authors of paper [25] positioned an array of eight transducers in the upper left corner of the phone. Combined with the phone's built-in loudspeaker, these transducers serve as the system's hardware. They chose this placement to minimize user disturbance. In both directions Delay-and-Sum beamforming is used to evaluate the data before it is pre-processed into a spectrogram that is evaluated by a TCN. Paper [36] is a HGR system based on distance calculation on an dedicated hardware, with extremely small power requirements. At a FoV of \(\pm\) 45\({}^{\circ}\) and at a distance of between 4.5 cm to 1 m. The output of the system is an indication of size, distance and location of objects in the FoV. Ref. [37] uses one loudspeaker and eight microphones that surround the loudspeaker. The evaluated features in this case are distance, derived from the peak of the data in time and the intensity of the signal in the surrounding of the peak. Evaluation is done by CNN with LSTM for two to five gestures. This paper is one of the few that compares their system's performance for different numbers of gestures. Ref. [38] uses custom hardware of seven receivers and one transmitter for object localization based on beamforming in a FoV of 90\({}^{\circ}\) within a distance of 1 m. A finite state machine can be used to do gesture recognition as the location of an object like a hand directly allows the evaluation of changes of the position and thereby a gesture. Gesture detection is done by evaluating if an object is being found in a specified start area in the FoV. Swipe gestures can successfully be recognized in this way. A hexagonal array of six transducers, with one sender in the middle is used in [38], to do Doppler-shift evaluation at different angles for dynamic gesture recognition. The classification is carried out in a very processing power efficient way with a TCN. Ref. [39] combines a big piezoelectric transmitter with a circle of eight MEMSreceivers. Using a processing combination of DOA estimation with a search algorithm and delay and sum beamformer. Only locations where both algorithms return high values are kept. In this way several static objects can be detected. Ref. [40] relies on an array of 64 transducers in a square arrangement for the creation of an image of the surrounding. As the transducers used are rather large, a waveguide is required to achieve the benifical \(\frac{\lambda}{2}\) distance. Sender side beamforming as well as beamforming only on the receiver side are evaluated. The system works at 40 kHz. Ref. [41], like [39], it utilizes a small amount of transducers. Different arrangements were theoretically considered. The outcome was that a square arrangement is most promising. The software approach consists of a mix of sound source localization with MUSIC algorithm and minimum-variance-distortionless-response (MVDR) beamforming. By contrast to [39] an array is also used on the sender side, not only on the receiver side. The authors of [40] also published about a fermat spiral based arrangement of their 64 transducers and developed a sophisticated sender and receiver side approach. All four, Refs. [39; 40; 41; 42], focus on scanning the surrounding. This task is different from dynamic hand gesture recognition, in that it scans static objects rather than moving objects. The approach presented in [43] uses receiver side delay-and-sum beamforming in both direction along the l-shaped array. The transmit pulse is sent from a different loudspeaker at a distance of several centimeters. The classification is done with a TCN on the 1D time domain data. Ref. [44] evaluates the three different beamforming methods MVDR, Delay and Sum, and Filter and Sum, for receiver side beamforming in combination with an omnidirectional sender. A TCN is used for classification. This approach is similar to DoA estimation and evaluation of frequency shift, which allows for classification of tap gestures from different angles.