Design and Implementation of an Infrared-Based Sensor for Finger Movement Detection
With the increasing interest in smart devices and convenient remote control, the need for accurate wireless means of control has become imperative. This gives rise to research in the field of gesture and finger movement detection. This design focuses on exploring techniques involved in hand and finger movement detection, using the depth-sensing infrared cameras embedded on Xbox Kinect Module. The generated 3-D images are first filtered along the z-axis, then two distinct techniques; Haar-Like Features, and Deep Learning using a Convolution Neural Network, are performed on the images to detect hands. Useful metrics like, Precision, Recall, F1-Score and Accuracy are then used to evaluate the efficiency of these techniques. The results show that while the deep learning model is the most accurate with a weighted accuracy of 1.0 (due to the absence of noise in the images) in contrast with 0.97 observed for the Haar-Like features, the Haar-like features technique runs faster due to its static nature. These findings point to the conclusion that the deep learning model is a better technique for detecting hands despite its longer running time.