Air Gesture Research Articles

An Air Gesture Based Keyboard for Visually Impaired People Using Machine Learning and Embedded System Approach

This report outlines the development of an air gesture keyboard utilizing machine learning technology, designed to assist visually impaired individuals in entering text more easily. This innovative system removes the need for physical keyboards, instead employing a remote-based system that translates hand gestures into text through the use of an accelerometer, Arduino, and switches. There is a significant need for alternative input methods for visually impaired individuals due to the limitations of existing technologies. The proposed air gesture keyboard captures and interprets hand motions in the air, enabling users to input text without physical contact. A literature review was conducted to examine related research on hand gesture recognition using various techniques.

Sonically-enhanced in-vehicle air gesture interactions: evaluation of different spearcon compression rates

Driver distraction is a major contributor to road vehicle crashes, and visual distraction caused by using in-vehicle infotainment systems (IVIS) degrades driving performance and increases crash risk. Air gesture interfaces have been developed to mitigate driver distraction, and using auditory displays showed a decrease in off-road glances and an improved perceived workload. However, the potential of auditory display was not fully investigated. The present paper presents directional research in the design of auditory displays for air-gesture IVIS through a dual-task experiment of driving a simulator and air-gesture menu navigation. Twenty-four participants utilized the in-vehicle air gesture interfaces while driving with four auditory display conditions (text-to-speech, 70% compressed spearcons, 40% compressed spearcson, and no sound). The results showed that 70% spearcon reduced visual distraction and increased menu navigation accuracy but with increased navigation time. 70% spearcon was most preferred by the participants. Driving performance or workload did not show any difference among the conditions. Implications are discussed with the design guidelines for future implementations.

In-vehicle air gesture design: impacts of display modality and control orientation

In-vehicle air gesture design: impacts of display modality and control orientation

Classification, Application, Challenge, and Future of Midair Gestures in Augmented Reality

Augmented Reality (AR) technology provides many opportunities to enhance people’s experience in interacting with data. Midair gesture, a natural interaction mode in AR, interacts with virtual elements without any auxiliary devices. It has become a hot topic of interest for researchers with the development of gesture recognition technology. From the perspective of user experience, the types of midair gesture, gesture recognition technology, applications were discussed. The challenges of air gesture interaction from two aspects of user experience and technology were analyzed, and the importance of collaborative gesture and low-cost user experience in the future were emphasized. Finally, the application prospect of air gesture interaction distance education, medical health, industry, office, and so on is discussed.

Air Gesture Recognition Using WLAN Physical Layer Information

In recent years, the researchers have witnessed the important role of air gesture recognition in human-computer interactive (HCI), smart home, and virtual reality (VR). The traditional air gesture recognition method mainly depends on external equipment (such as special sensors and cameras) whose costs are high and also with a limited application scene. In this paper, we attempt to utilize channel state information (CSI) derived from a WLAN physical layer, a Wi-Fibased air gesture recognition system, namely, WiNum, which solves the problems of users’ privacy and energy consumption compared with the approaches using wearable sensors and depth cameras. In the process of recognizing the WiNum method, the collected raw data of CSI should be screened, among which can reflect the gesture motion. Meanwhile, the screened data should be preprocessed by noise reduction and linear transformation. After preprocessing, the joint of amplitude information and phase information is extracted, to match and recognize different air gestures by using the S-DTW algorithm which combines dynamic time warping algorithm (DTW) and support vector machine (SVM) properties. Comprehensive experiments demonstrate that under two different indoor scenes, WiNum can achieve higher recognition accuracy for air number gestures; the average recognition accuracy of each motion reached more than 93%, in order to achieve effective recognition of air gestures.

Design and Development of Air Gesture Algorithm based on Video Processing

Design and Development of Air Gesture Algorithm based on Video Processing

Design and evaluation of auditory-supported air gesture controls in vehicles

Using touchscreens while driving introduces competition for visual attention that increases crash risk. To resolve this issue, we have developed an auditory-supported air gesture system. We conducted two experiments using the driving simulator to investigate the influence of this system on driving performance, eye glance behavior, secondary task performance, and driver workload. In Experiment 1 we investigated the impact of menu layout and auditory displays with 23 participants. In Experiment 2 we compared the best systems from Experiment 1 with equivalent touchscreen systems with 24 participants. Results from Experiment 1 showed that menus arranged in 2 × 2 grids outperformed systems with 4 × 4 grids across all measures and also demonstrated that auditory displays can be used to reduce visual demands of in-vehicle controls. In Experiment 2 auditory-supported air gestures allowed drivers to look at the road more, showed equivalent driver workload and driving performance, and slightly decreased secondary task performance compared to touchscreens. Implications are discussed with multiple resources theory and Fitts’s law.

WIRELESS CONTROL OF AN AUTOMOBILE USING AIR GESTURES

In the present world people with disabilities wish to be independent and thus the dependence on automatic machinery has increased drastically. People with physical disabilities and partial paralysis find it difficult to navigate without the assistance of someone. The system proposed will be a boost to the physically challenged people as it will help them to be self-reliable. Air gesture model is being as a key component to derive the benefit and gesture recognition is one obvious way to create a useful, highly adaptive interface between machines and their users. The gestures of the hand are read by a camera which is attached to a computer where further processing occurs. Hand gesture recognition technology allows for the navigation of an automobile using only a series of finger and hand movements, eliminating the need for physical contact between operator and machine. If the hand gesture is similar then Arduino is programmed such that the robotic car moves forward. This model can be extended to other areas such as efficient control by traffic corps, patients and senior citizens to call or interact with others, and so on. However, in this paper, the focus will be more as part of driving vehicles.

GESMO- MOtion GESture based Mobile Application

has been number of android mobile apps available for clients to solve their daily problems. This paper presents a Motion Gesture based mobile (GESMO) app in Android which allows user to draw gestures in the air resulting opening of a desired app assigned to corresponding gesture. This gesture may be the first letter of an app name which is to be launched. In order to start the motion, user must press and hold any point on the screen until the motion is completed. Once motion completes, it is released. For sensing the motion, we used two built-in sensors from mobile devices: accelerometer and gyroscope. The paper proposes a two-stage approach for spotting and recognition of generated stroke gesture. The spotting stage uses a Support Vector Machine (SVM) to identify data fragments containing one stroke gesture. The recognition stage uses Hidden Markov Models (HMM) to generate the text representation from the motion sensor data. With this technique, you can successfully receive 70-80% accuracy in detecting an air gesture for mobile devices.

Static Hand Gesture Recognition using an Android Device

The need to enhance communication between humans and computers has been instrumental in determining new communication models, and accordingly new ways of interacting with machines. A vision based Hand Gesture Recognition system can be useful to recognize hand gesture in air, with devices like camera equipped smart phones and cameras connected to computers. The fast improvement of smartphones during the last decade has been predominantly determined by interaction and visualization innovations. Despite the fact that touchscreens have significantly enhanced interaction technology, future smartphone clients will request more natural inputs, for example, free-hand association in 3D space. To extract the features of air gesture we used statistical technique which is Principal Component Analysis (PCA). The recognition approach used in this paper is based on Support Vector Machine (SVM). Proposed Hand Gesture System is location and orientation invariant. All the processes to recognize the hand gesture are done on the device. This approach can be easily adapted to a real time system.

An Improved SIFT Algorithm for Unmanned Aerial Vehicle Imagery

The Unmanned Aerial Vehicle (UAV) platform has the benefits of low cost and convenience compared with satellites. Recently, UAVs have shown a wide range of applications such as land use change, mineral resources management and local topographic mapping. Because of the instability of the UAV air gesture, an image matching method is necessary to match different images of an object or scene. Scale Invariant Feature Transform (SIFT) features are invariant to image scaling, rotation and translation. However, the main drawback of a SIFT algorithm is its significant memory consumption and low computational speed, particularly in the case of high-resolution imagery. In this study, in order to overcome these drawbacks, we have analysed the construction of the scale-space in the SIFT algorithm and selected new parameters to construct the SIFT scale-space to improve the memory consumption and computational speed for the processing of UAV imagery. Here, we propose a restriction on the number of octaves and levels for Gaussian image pyramids. Our experiment shows that the proposed algorithm effectively reduces memory consumption and significantly improves the operational efficiency of the feature point extraction and matching under the premise of maintaining the precision of the extracted feature points.