Human-Computer Interaction in Smart Environments

Hand Gesture Recognition Using FSK Radar Sensors

Hand gesture recognition, which is one of the fields of human-computer interaction (HCI) research, extracts the user's pattern using sensors. Radio detection and ranging (RADAR) sensors are robust under severe environments and convenient to use for hand gestures. The existing studies mostly adopted continuous-wave (CW) radar, which only shows a good performance at a fixed distance, which is due to its limitation of not seeing the distance. This paper proposes a hand gesture recognition system that utilizes frequency-shift keying (FSK) radar, allowing for a recognition method that can work at the various distances between a radar sensor and a user. The proposed system adopts a convolutional neural network (CNN) model for the recognition. From the experimental results, the proposed recognition system covers the range from 30 cm to 180 cm and shows an accuracy of 93.67% over the entire range.

Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module

The increasing integration of technology in our daily lives demands the development of more convenient human-computer interaction (HCI) methods. Most of the current hand-based HCI strategies exhibit various limitations, e.g., sensibility to variable lighting conditions and limitations on the operating environment. Further, the deployment of such systems is often not performed in resource-constrained contexts. Inspired by the MobileNetV1 deep learning network, this paper presents a novel hand gesture recognition system based on frequency-modulated continuous wave (FMCW) radar, exhibiting a higher recognition accuracy in comparison to the state-of-the-art systems. First of all, the paper introduces a method to simplify radar preprocessing while preserving the main information of the performed gestures. Then, a deep neural classifier with the novel Depthwise Expansion Module based on the depthwise separable convolutions is presented. The introduced classifier is optimized and deployed on the Coral Edge TPU board. The system defines and adopts eight different hand gestures performed by five users, offering a classification accuracy of 98.13% while operating in a low-power and resource-constrained environment.

Qualitative Assessment of Effective Gamification Design Processes Using Motivators to Identify Game Mechanics

This research focuses on the study and qualitative assessment of the relationships between motivators and game mechanics per the ratings of expert gamification consultants. By taking this approach, it is intended that during the design phase of a gamified system, decisions can be made about the design of the system based on the motivators of each of the profiles. These motivators can be determined from the information provided by the potential players themselves. The research presented starts from a previous analysis in which, based on the three most used gamification frameworks and through a card sorting technique that allows the user to organize and classify the content, a set of mechanics are determined. In the present study, each of the mechanics is analyzed, and a more precise motive is decided. As a result, a higher level of personalization is achieved and, consequently, approximates a higher level of gamification effectiveness. The main conclusions are implemented in the development of the Game4City 3.0 project, which addresses gamified and interactive strategies to visualize urban environments in 3D at an educational and social level.

Robust 3D Hand Detection from a Single RGB-D Image in Unconstrained Environments

Three-dimensional hand detection from a single RGB-D image is an important technology which supports many useful applications. Practically, it is challenging to robustly detect human hands in unconstrained environments because the RGB-D channels can be affected by many uncontrollable factors, such as light changes. To tackle this problem, we propose a 3D hand detection approach which improves the robustness and accuracy by adaptively fusing the complementary features extracted from the RGB-D channels. Using the fused RGB-D feature, the 2D bounding boxes of hands are detected first, and then the 3D locations along the z-axis are estimated through a cascaded network. Furthermore, we represent a challenging RGB-D hand detection dataset collected in unconstrained environments. Different from previous works which primarily rely on either the RGB or D channel, we adaptively fuse the RGB-D channels for hand detection. Specifically, evaluation results show that the D-channel is crucial for hand detection in unconstrained environments. Our RGB-D fusion-based approach significantly improves the hand detection accuracy from 69.1 to 74.1 comparing to one of the most state-of-the-art RGB-based hand detectors. The existing RGB- or D-based methods are unstable in unseen lighting conditions: in dark conditions, the accuracy of the RGB-based method significantly drops to 48.9, and in back-light conditions, the accuracy of the D-based method dramatically drops to 28.3. Compared with these methods, our RGB-D fusion based approach is much more robust without accuracy degrading, and our detection results are 62.5 and 65.9, respectively, in these two extreme lighting conditions for accuracy.

Accurate Hand Detection from Single-Color Images by Reconstructing Hand Appearances

Hand detection is a crucial pre-processing procedure for many human hand related computer vision tasks, such as hand pose estimation, hand gesture recognition, human activity analysis, and so on. However, reliably detecting multiple hands from cluttering scenes remains to be a challenging task because of complex appearance diversities of dexterous human hands (e.g., different hand shapes, skin colors, illuminations, orientations, and scales, etc.) in color images. To tackle this problem, an accurate hand detection method is proposed to reliably detect multiple hands from a single color image using a hybrid detection/reconstruction convolutional neural networks (CNN) framework, in which regions of hands are detected and appearances of hands are reconstructed in parallel by sharing features extracted from a region proposal layer, and the proposed model is trained in an end-to-end manner. Furthermore, it is observed that the generative adversarial network (GAN) could further boost the detection performance by generating more realistic hand appearances. The experimental results show that the proposed approach outperforms the state-of-the-art on public challenging hand detection benchmarks.

Online Learners' Reading Ability Detection Based on Eye-Tracking Sensors

The detection of university online learners' reading ability is generally problematic and time-consuming. Thus the eye-tracking sensors have been employed in this study, to record temporal and spatial human eye movements. Learners' pupils, blinks, fixation, saccade, and regression are recognized as primary indicators for detecting reading abilities. A computational model is established according to the empirical eye-tracking data, and applying the multi-feature regularization machine learning mechanism based on a Low-rank Constraint. The model presents good generalization ability with an error of only 4.9% when randomly running 100 times. It has obvious advantages in saving time and improving precision, with only 20 min of testing required for prediction of an individual learner's reading ability.