Effect of sparsity-aware time–frequency analysis on dynamic hand gesture classification with radar micro-Doppler signatures

Dynamic hand gesture recognition is of great importance in human-computer interaction. In this study, the authors investigate the effect of sparsity-driven time-frequency analysis on hand gesture classification. The time-frequency spectrogram is first obtained by sparsity-driven time-frequency analysis. Then three empirical micro-Doppler features are extracted from the time-frequency spectrogram and a support vector machine is used to classify six kinds of dynamic hand gestures. The experimental results on measured data demonstrate that, compared to traditional time-frequency analysis techniques, sparsity-driven time-frequency analysis provides improved accuracy and robustness in dynamic hand gesture classification

Joint Skeletal and Semantic Embedding Loss for Micro-gesture Classification

In this paper, we briefly introduce the solution of our team HFUT-VUT for the Micros-gesture Classification in the MiGA challenge at IJCAI 2023. The micro-gesture classification task aims at recognizing the action category of a given video based on the skeleton data. For this task, we propose a 3D-CNNs-based micro-gesture recognition network, which incorporates a skeletal and semantic embedding loss to improve action classification performance. Finally, we rank 1st in the Micro-gesture Classification Challenge, surpassing the second-place team in terms of Top-1 accuracy by 1.10%.Comment: 1st Place in Micro-gesture Classification sub-challenge in MiGA at IJCAI-202

A psychometric measure of working memory capacity for configured body movement.

Working memory (WM) models have traditionally assumed at least two domain-specific storage systems for verbal and visuo-spatial information. We review data that suggest the existence of an additional slave system devoted to the temporary storage of body movements, and present a novel instrument for its assessment: the movement span task. The movement span task assesses individuals' ability to remember and reproduce meaningless configurations of the body. During the encoding phase of a trial, participants watch short videos of meaningless movements presented in sets varying in size from one to five items. Immediately after encoding, they are prompted to reenact as many items as possible. The movement span task was administered to 90 participants along with standard tests of verbal WM, visuo-spatial WM, and a gesture classification test in which participants judged whether a speaker's gestures were congruent or incongruent with his accompanying speech. Performance on the gesture classification task was not related to standard measures of verbal or visuo-spatial working memory capacity, but was predicted by scores on the movement span task. Results suggest the movement span task can serve as an assessment of individual differences in WM capacity for body-centric information

Dynamic Hand Gesture Classification Based on Radar Micro-Doppler Signatures

Dynamic hand gesture recognition is of great importance for human-computer interaction. In this paper, we present a method to discriminate the four kinds of dynamic hand gestures, snapping fingers, flipping fingers, hand rotation and calling, using a radar micro-Doppler sensor. Two micro-Doppler features are extracted from the time-frequency spectrum and the support vector machine is used to classify these four kinds of gestures. The experimental results on measured data demonstrate that the proposed method can produce a classification accuracy higher than 88.56%

UWB Based Static Gesture Classification

Our paper presents a robust framework for UWB-based static gesture recognition, leveraging proprietary UWB radar sensor technology. Extensive data collection efforts were undertaken to compile datasets containing five commonly used gestures. Our approach involves a comprehensive data pre-processing pipeline that encompasses outlier handling, aspect ratio-preserving resizing, and false-color image transformation. Both CNN and MobileNet models were trained on the processed images. Remarkably, our best-performing model achieved an accuracy of 96.78%. Additionally, we developed a user-friendly GUI framework to assess the model's system resource usage and processing times, which revealed low memory utilization and real-time task completion in under one second. This research marks a significant step towards enhancing static gesture recognition using UWB technology, promising practical applications in various domains

Human gesture classification by brute-force machine learning for exergaming in physiotherapy

In this paper, a novel approach for human gesture classification on skeletal data is proposed for the application of exergaming in physiotherapy. Unlike existing methods, we propose to use a general classifier like Random Forests to recognize dynamic gestures. The temporal dimension is handled afterwards by majority voting in a sliding window over the consecutive predictions of the classifier. The gestures can have partially similar postures, such that the classifier will decide on the dissimilar postures. This brute-force classification strategy is permitted, because dynamic human gestures show sufficient dissimilar postures. Online continuous human gesture recognition can classify dynamic gestures in an early stage, which is a crucial advantage when controlling a game by automatic gesture recognition. Also, ground truth can be easily obtained, since all postures in a gesture get the same label, without any discretization into consecutive postures. This way, new gestures can be easily added, which is advantageous in adaptive game development. We evaluate our strategy by a leave-one-subject-out cross-validation on a self-captured stealth game gesture dataset and the publicly available Microsoft Research Cambridge-12 Kinect (MSRC-12) dataset. On the first dataset we achieve an excellent accuracy rate of 96.72%. Furthermore, we show that Random Forests perform better than Support Vector Machines. On the second dataset we achieve an accuracy rate of 98.37%, which is on average 3.57% better then existing methods

Gesture Classification Based on Electromyography

The purpose of the work is to identify different hand poses based in the Electromyography raw signals provided from a Myo armband, using various signal processing, feature extraction and pattern recognition techniques. First we will replicate the gesture dictionary provided by the manufacturer, and then we will explore different hand poses that are compatible with the basic dictionary of gestures, and then expanding the amount of recognizable gestures for the tested classifiers.The purpose of the work is to identify different hand poses based in the Electromyography raw signals provided from a Myo armband, using various signal processing, feature extraction and pattern recognition techniques. First we will replicate the gesture dictionary provided by the manufacturer, and then we will explore different hand poses that are compatible with the basic dictionary of gestures, and then expanding the amount of recognizable gestures for the tested classifiers