Wrist-worn devices have organically grown to capture the market. For dealing with and improving the quality of such devices, a hand gesture customization framework has been developed for upgrading user quality. For this, collection and analysis of data have been done so that effective and accurate measures can be taken. For developing this improved gesture customization we cannot degrade the pre-existing gesture set. The purpose is to implement the real-time customization gesture set so that accurate results can be derived.
Hand Gesture Customization
The hand gesture customization model is a framework that recognizes several pre-existing gesture sets. It cannot identify the gestures that are out of the dictionary and it possesses robustness towards the noise. A custom gesture can easily be added with the help of three samples. You need to make sure that you add the new gesture set without disrupting the pre-existing gesture sets.
The system is designed in such a way that provides real-time feedback when the new gesture and the pre-existing gestures are made to come in contact with each other. However, it can sometimes be inconsistent if it does not get adapted to daily and common activities. With more facilities, users can also add more samples when the performance of the gesture sets becomes optimal.
Requirements for gesture customization
The systems that are meant to achieve gesture customization, have to fulfill two main requirements that are stated as follows:
- The system must support a rapid Data collection process that is also minimal. It will help to reduce the burden on the user. For example, you may try using four samples at first and then keep on increasing if that works for you.
- The system must fulfill the requirement of fine-tuning and it will be better if it offers certain other features as well. In that way, it will help you to get rid of the problems where the performance of the pre-existing model decreases rapidly. Another problem is overfitting with the new classes. The new gesture set then causes the problem of less generalizability.
Thus, you must analyze a gesture set properly before choosing and implementing it. The rightly chosen gesture set will provide you with many advantages but if you choose a gesture set that is not compatible with your system then it can become a ban for you.
Pre-trained gesture model
A gesture model goes through a lot of steps and procedures before its implementation. The various steps and series of a gesture model can be enumerated as follows:
- The first and foremost thing for gesture customization is to collect reliable data.
- The raw input of the model is then transformed into a gesture set architecture. This built the structure of the gesture set.
- Then the model is set forth for training. After the model has gained sufficient training its performance is then analyzed.
After performing all these steps in a series, the gesture set is then said to make performance predictions. Only after going through a training process, the gesture sets are allowed to make predictions and deliver accurate results.
In recent times, promising hand gesture recognition models are being developed. Wrist-worn devices should give the users freedom so that they can add their own gestures to the pre-designed model. For more device learning, you can learn Compressed Embeddings for On-Device Inference and expand your knowledge.