Air Gestures Research Articles

Magnetic-interaction assisted hybridized triboelectric-electromagnetic nanogenerator for advanced human-machine interfaces

A magnetic-interaction assisted hybridized triboelectric-electromagnetic nanogenerator (MAHN) is designed and optimized with two magnets in attraction, and a silicone-based cushion with microstructures. The whole device including a fixed part and a moving part has a small size of 35 mm × 35 mm × 8 mm. After various energy harvesting and sensing characterizations, the MAHN has been verified as an efficient energy harvester, as well as a self-powered sensor for advanced human-computer interface (HMI). For energy harvesting, the triboelectric nanogenerator (TENG) delivers a peak power of 6.89 mW at the load of 1.1 MΩ, and the electromagnetic generator (EMG) delivers a peak power of 2.7 mW at the load of 1.1 kΩ. With the hybridized buck circuit, outputs from both the TENG and the EMG can be rectified and integrated efficiently to charge capacitors. A capacitor of 100 μF is charged to 2.03 V within 30 s, which is better than the performances of individual generators and their parallel-connection with only rectifiers. A thermometer and a Bluetooth module are powered with different amounts of the attracted magnet, implying the wide adaptability of the MAHN. Advanced applications of HMI have also been developed with cross-divided electrodes in the MAHN, such as orientation control in a game of Snake, real-time operation of a PowerPoint presentation, and recognition of simple air gestures in contactless control. Moreover, a 3 × 3 array is prepared and signal channels are simplified by traversal method without loss of functions to achieve higher-level control. A virtual football game is demonstrated by a shoe moving on the array, and different kinds of shootings have been mapped to the player in the game.

Real-Time Macro Gesture Recognition Using Efficient Empirical Feature Extraction With Millimeter-Wave Technology

Human Machine Interaction based on air gestures finds an increasing number of applications in consumer electronics. The availability of mmWave technology, combined with machine learning, allows the detection and classification of gestures, avoiding high-resolution LIDAR or video sensors. Nevertheless, in most of the existing studies, the processing takes place offline, takes into account only the velocity and distance of the moving arm, and can handle only gestures that are conducted very close to the sensor device, which limits the range of possible applications. Here, we use an experimental multi-channel mmWave-based system that can detect small targets, like a moving arm, up to a few meters away from the sensor. As our pipeline can estimate and take into account the angle of arrival in azimuth and elevation, it has the ability to classify a greater variety of dynamic gestures. Furthermore, the digital signal processing chain we present here, runs in real-time, incorporating an event detector. Whenever an event is detected, a novel empirical feature extraction takes place and a Multi-Layer Perceptron is deployed to infer the type of the gesture. To evaluate our setup and signal processing pipeline, a dataset with ten subjects, performing nine gestures was recorded. Our method yielded 94.3% accuracy on the test set, indicating a successful combination of our proposed sensor and signal processing pipeline for real time applications.

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.

Cooperative Sensing and Wearable Computing for Sequential Hand Gesture Recognition

Hand gestures recognition (HGR) has been considered as one of the crucial research fields of human–computer interaction (HCI). Computer vision is a very active research field in the HGR, traditional vision-based methods, which used camera and ultrasonic/optical sensor to collect the videos or images of the hand gestures shown by participants, have some limitations, such as fixed in-lab location, complex lighting conditions, and cluttered backgrounds. In order to provide new approaches, we described the development of a novel hand gesture recognition system that combined wearable armband and smart glove made by customizable pressure sensor arrays to detect sequential hand gestures. A deep learning technique long short-term memory (LSTM) algorithm had been computed to build an effective model to classify hand gestures by training and testing the collected inertial measurement unit (IMU), electromyographic (EMG), and finger and palm’s pressure data. Furthermore, we built a relatively large database of ten sequential hand gestures consisted by five dynamic gestures and five air gestures collected from ten participants. Our experimental results showed an outstanding classification performance of the proposed LSTM algorithm. These findings have promising implications for sequential hand gesture recognition and the HCI research status.

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.

Feasibility of Wrist-Worn, Real-Time Hand, and Surface Gesture Recognition via sEMG and IMU Sensing

While most wearable gesture recognition approaches focus on the forearm or fingers, the wrist may be a more suitable location for practical use. We present the design and validation of a real-time gesture recognition wristband based on surface electromyography and inertial measurement unit sensing fusion, which can recognize 8 air gestures and 4 surface gestures with 2 distinct force levels. Ten healthy subjects performed an initial gesture recognition experiment, followed by a second experiment 1 h later and a third experiment 1 day later. Classification accuracies for the initial experiment were 92.6% and 88.8% for air and surface gestures, respectively, and there were no changes in accuracy results during testing 1 h. and 1 day later ( $p$ $>$ 0.05). These results demonstrate the feasibility of wrist-based gesture recognition paving the way for potential future integration in to a smart watch or other wrist-worn wearable for intuitive human computer interaction.

Comparison of Surface Gestures and Air Gestures for In-Vehicle Menu Navigation

Background Traditionally, in-vehicle systems have been operated using physical controls such as buttons and knobs. The array of tasks that these controls can efficiently accomplish in modern invehicle systems has outpaced their capabilities. The use of gestures for navigating interfaces has become increasingly prevalent solution to these shortcomings. However, best practices for the use of gestures have not been developed. leaving designers at risk of implementing systems that can increase a driver’s workload and raise the risk of accidents. Methods The current paper examines four gesture interaction techniques for completing a menu navigation task in terms of their effects on drivers’ primary driving and secondary menu navigation performance, as well as self-reported workload and preferences. Participants used either a tap (point) gesture or a swipe gesture either in the air or on a gesture surface. Results It was found that air gestures were slower than surface gestures and led to higher workload. Swipe gestures were lower workload than tap gestures, and the surface swipe gesture led to the lowest workload overall. Conclusions These results indicate that, at present, surface gestures are a lower workload alternative to air gestures that retain some of their flexibility and naturalistic potential.

Studying the Timing of Discrete Musical Air Gestures

Motion-sensing technologies enable musical interfaces where a performer controls sound by moving his or her body “in the air,” without touching a physical object. These interfaces work well when the movement and resulting sound are smooth and continuous, but it has proven difficult to design air instruments that trigger discrete sounds with precision that feels natural to performers and allows them to play rhythmically complex music. This article presents a study of “air drumming” gestures. Participants performed drumming-like gestures in time to simple recorded rhythms. These movements were recorded and examined to look for aspects of the movement that correspond to the timing of the sounds. The goal is to understand what we do with our bodies when we gesture in the air to trigger a sound. Two movement features of the hand are studied: Hits are the moment where the hand changes direction at the end of the striking gesture, and acceleration peaks are sharp peaks in magnitude acceleration as the hand decelerates. Hits and acceleration peaks are also detected for the movement of the wrist. It is found that the acceleration peaks are more useful than the hits because they occur earlier and with less variability, and their timing changes less with note speed. It is also shown that timing differences between hand and wrist features can be used to group performers into different movement styles.

Book Group Therapy

groups, whether library-sponsored or privately hosted, continue to grow in popularity. Perhaps the opportunity to connect to others face-to-face in what is an increasingly virtual world motivates people to come together to talk about their reading. Or perhaps it is the food. any case, reader interest in book discussions offers libraries a lot of opportunities to interact with their reading community and is a chance for libraries to reinforce their value to the community, a useful thing in unsettled economic times. 2008, the RUSA CODES Readers' Advisory Committee surveyed book group participants across the country Among the most interesting of the survey results was the discovery of a common set of problems that book groups seem to face no matter where they are or how long they have been meeting. Here, Megan McArdle explores these ongoing book group issues and offers suggestions that libraries can use when working with their local groups. These suggestions also will be useful for book group members seeking to improve the quality of their book group experience. Megan McArdle is the Manager for Collection Development and Technical Services at the Berkeley (Calif.) Public Library. Active in ALA and the Public Library Association, she is the past chair of the RUSA's Readers' Advisory Committee and is on the Advisory Committee for H.W. Wilson's Fiction Catalog.--Editor At the far end of a dimly lit hallway, in a mostly unoccupied office building, there's a plain, unassuming door with the words Book Group Therapist hand-lettered on the glass. As you gingerly open the door and enter the office, a cool-eyed blond with an authoritarian air gestures to the enormous couch that stretches across the far wall of the room. Ah! You must be my ten o'clock clients. Come in and have a seat. Your book group dutifully files into the office and jostles for position on the couch, while the therapist slowly looks you over. In this room I require absolute honesty if we are to get at the root of your group's dysfunctional issues. She makes eye contact with each member, pausing over those members fussing with cell phones or furtively looking for the coffee. Shall we begin? Let's start by talking about your childhood reading... Oh, if only this kind of therapy was a reality for a troubled book group! Whether brand new or long-established, book groups can run into problems. These can range from the benign (a book fails flat) to the group-killing (unpleasant meetings filled with bickering that leave members intimidated and afraid to return). Learning what some of the most commonly occurring problems are with book groups, what some of the most successful books discussed in their groups are, and what members would most like to change about their groups are just some of the useful results that came out of a 2008 survey conducted by the RUSA CODES Readers' Advisory Committee. A review of the survey findings should help all those working with book groups to get ideas on how to provide some therapy, or at least some therapeutically good reads. late 2007, the Readers' Advisory Committee was planning for a program at the 2008 ALA Annual Conference in Anaheim, California, called Group Therapy: How to Repair, Revamp, and Revitalize Your Group. This program was intended to help librarians who host library book groups or work with community book groups. The committee members had all experienced and heard from our peers that there were some common issues that book groups run into, and we wanted to provide some strategies to help solve these problems. As research for the program we decided to conduct a survey of book groups to try and find out more about what makes them tick. We put together some questions that we found interesting, let it loose on the Web (via the wonderful SurveyMonkey application), told people we thought would be interested (through posts to blogs, discussion lists, and newsletters), and slowly watched the responses come in. …