# Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier
###### Abstract  
In the past few years, home appliances have been influenced by the latest technologies and changes in consumer trends. One of the most desired gadgets of this time is a universal remote control for gestures. Hand gestures are the best way to control home appliances. This paper presents a novel method of recognizing hand gestures for smart home appliances using imaging sensors. The proposed model is divided into six steps. First, preprocessing is done to de-noise the video frames and resize each frame to a specific dimension. Second, the hand is detected using a single shot detector-based convolution neural network (SSD-CNN) model. Third, landmarks are localized on the hand using the skeleton method. Fourth, features are extracted based on point-based trajectories, frame differencing, orientation histograms, and 3D point clouds. Fifth, features are optimized using fuzzy logic, and last, the H-Hash classifier is used for the classification of hand gestures. The system is tested on two benchmark datasets, namely, the IPN hand dataset and Jester dataset. The recognition accuracy on the IPN hand dataset is 88.46% and on Jester datasets is 87.69%. Users can control their smart home appliances, such as television, radio, air conditioner, and vacuum cleaner, using the proposed system.  
25 May 20224 June  
hand gestures [10; 11; 12]. By making gestures, you can control all the devices in your home. A motion sensor is one of the most important tools in your smart home [13; 14; 15].  
Different models proposed for controlling smart home appliances using hand gesture recognition can be divided into two main streams. The first approach is based on the recognition of hand gestures using motion sensors embedded in smart home appliances [16; 17; 18]. In motion-based sensors, one inertial sensor or an array of sensors is used. These sensors are responsible for tracking the acceleration, velocity, and position of the hand. Such type of motion features help to control smart appliances such as television, radio, and lighting of rooms [19; 20; 21], however, the drawback of using motion-based sensors in smart home appliances is the high sensitivity. The second approach is the use of image sensors [22; 23; 24] or cameras to obtain the commands from hand gestures [25]; the sensors are trained on image features which include color, shape, texture, position contours, and motion of the hands. Our proposed model is based on the second approach and recognizes the hand gestures using imagery sensors or cameras [26].  
In this research article, we propose a robust method for recognizing hand gestures for controlling smart home appliances. For this, we use the IPN hand dataset and Jester dataset. Initially, preprocessing of the video samples for frame conversion, motion blur noise reduction, and resizing is performed. The next step is hand detection via SSD-CNN. After that, the hand skeleton is extracted to process these data sources by various algorithms for features extraction, optimization of the extracted features, and recognition of the hand gestures. The main contributions of this paper are:  
* For hand motion and position analysis, we propose a method for extracting hand skeletons;
* For the recognition of image-based hand gestures, we have extracted novel features based on point-based trajectories, frame differencing, orientation histogram, and 3D point clouds.  
The article is subdivided as follows: we start with the related works section, which is followed by our system methodology. Then, the detailed experimental setup is discussed, and finally, an overview of the paper is presented in the Conclusions Section.