Hand Gesture Recognition Using an IR-UWB Radar with an Inception Module-Based Classifier

Abstract

The emerging integration of technology in daily lives has increased the need for more convenient methods for human–computer interaction (HCI). Given that the existing HCI approaches exhibit various limitations, hand gesture recognition-based HCI may serve as a more natural mode of man–machine interaction in many situations. Inspired by an inception module-based deep-learning network (GoogLeNet), this paper presents a novel hand gesture recognition technique for impulse-radio ultra-wideband (IR-UWB) radars which demonstrates a higher gesture recognition accuracy. First, methodology to demonstrate radar signals as three-dimensional image patterns is presented and then, the inception module-based variant of GoogLeNet is used to analyze the pattern within the images for the recognition of different hand gestures. The proposed framework is exploited for eight different hand gestures with a promising classification accuracy of 95%. To verify the robustness of the proposed algorithm, multiple human subjects were involved in data acquisition.

Hand Gestures Recognition Using Radar Sensors for Human-Computer-Interaction: A Review

“…This includes data representation, useful features extraction, and classification. The classification can be performed by using signal-processing approaches, traditional machine-learning approaches [12,15,16,19] or deep-learning approaches [14].…”

“…Similar research presented by [8] used milli-metric-wave radar for occupancy detection. In addition to this, radar sensors have shown their footprints in hand-motion sensing and dynamic HGR [9][10][11][12][13][14][15][16][17][18][19]. The interest in radar-based gesture recognition has surged in recent years.…”

“…Another study published by same authors presented a continuous alphabet writing based on the gestures drawn in front of two radars [20]. A work presented by Ahmed and Cho [12] (2020) demonstrated a performance comparison of different Deep Convolutional Neural Network (DCNN)-based deep-learning algorithms using multiple radars. In addition to that, hand gesture recognition through radar technology also found application in the operating room to assist medical staff in processing and manipulating medical images [21].…”

Doppler-Spectrum Feature-Based Human–Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor

“…Recently, research on the detection of human activities and motions was conducted. Examples include hand gesture recognition [2], human gait indication [3], human fall detection [4], and human vital signal detection [5].…”

Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet

“…As far as network architecture is concerned, a network proposed by Google called GoogLeNet, residual network (ResNet), and densely connected convolutional network (DenseNet) are all innovative. In addition, GoogLeNet [ 20 , 21 ] and ResNet [ 22 , 23 , 24 ] are used widely in hand gesture recognition, image retrieval, and visual recognition. DenseNet can reuse the features of initial samples and has better performance than other CNN models.…”