Computer mice have their displacement sensors in various locations (center, front, and rear). However, there has been little research into the effects of sensor position or on engineering approaches to exploit it. This paper first discusses the mechanisms via which sensor position affects mouse movement and reports the results from a study of a pointing task in which the sensor position was systematically varied. Placing the sensor in the center turned out to be the best compromise: improvements over front and rear were in the 11--14% range for throughput and 20--23% for path deviation. However, users varied in their personal optima. Accordingly, variable-sensor-position mice are then presented, with a demonstration that high accuracy can be achieved with two static optical sensors. A virtual sensor model is described that allows software-side repositioning of the sensor. Individual-specific calibration should yield an added 4% improvement in throughput over the default center position.
In a few months the computer mouse will be half-a-century-old. It is known to have many drawbacks, the main ones being: loss of productivity due to constant switching between keyboard and mouse, and health issues such as RSI. Like the keyboard, it is an unnatural human-computer interface. However the vast majority of computer users still use computer mice nowadays. In this article, we explore computer mouse alternatives. Our research shows that moving the mouse cursor can be done efficiently with camera-based head tracking system such as the SmartNav device, and mouse clicks can be emulated in…
Mouse-tracking of computer system users represents a less expensive, but also a far more applicable alternative to eye-tracking. The main disadvantage of mouse-tracking are errors manifested as discrepancies between the actual eye-gaze position and the mouse cursor position. This paper presents a method for automated correction of errors arising in mouse-tracking. Our approach draws upon information theory and employs Shannon entropy. The method is based on calculating the entropy of a visual representation of a Web page, i.e., we quantify information potential values of various positions. Inf…
This manuscript presented brain-computer interface (STM32 and ADS1299) with the embedded board with sensors to monitor the subject's state and environment. To reduce power consumption and device size, we used sensors made in Micro-Electro-Mechanical Systems technology (MEMS) - a gyroscope, accelerometer, and environmental monitoring sensors: CO2, temperature, humidity, ambient sound, and pulse and blood oxygen saturation. Data from the device is transmitted using TCP-PI (UART by Bluetooth) protocol to a computer or mobile device. Open-source https://github.com/Ildaron/ironbci
A custom head-mounted system to track smooth eye movements for control of a mouse cursor is implemented and evaluated. The system comprises a head-mounted infrared camera, an infrared light source, and a computer. Software-based image processing techniques, implemented in Microsoft Visual Studio, OpenCV, and Pupil, detect the pupil position and direction of pupil movement in near real-time. The identified direction is used to determine the desired positioning of the cursor, and the cursor moves towards the target. Two users participated in three tests to quantify the differences between increm…
National University of Science and Technology（国立科技大学）；Pakistan Mobile Communications Limited（巴基斯坦移动通信有限公司）
Human Activity Recognition (HAR) is an ongoing research topic. It has applications in medical support, sports, fitness, social networking, human-computer interfaces, senior care, entertainment, surveillance, and the list goes on. Traditionally, computer vision methods were employed for HAR, which has numerous problems such as secrecy or privacy, the influence of environmental factors, less mobility, higher running costs, occlusion, and so on. A new trend in the use of sensors, especially inertial sensors, has lately emerged. There are several advantages of employing sensor data as an alternati…
Optimal feedback control (OFC) is a theory from the motor control literature that explains how humans move their body to achieve a certain goal, e.g., pointing with the finger. OFC is based on the assumption that humans aim to control their body optimally, within the constraints imposed by body, environment, and task. In this paper, we explain how this theory can be applied to understanding Human-Computer Interaction (HCI) in the case of pointing. We propose that the human body and computer dynamics can be interpreted as a single dynamical system. The system state is controlled by the user via…
Emotion Detection on User Front-Facing App Interfaces for Enhanced Schedule Optimization: A Machine Learning Approach
The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada（电气与计算机工程系，多伦多大学，多伦多，加拿大）；Department of Software and IT Engineering, École de technologie supérieure, Montreal, Canada（软件与信息技术工程系，École de technologie supérieure，蒙特利尔，加拿大）
California State University, Northridge（加州州立大学北岭分校）
Wearable computing is one of the fastest growing technologies today. Smart watches are poised to take over at least of half the wearable devices market in the near future. Smart watch screen size, however, is a limiting factor for growth, as it restricts practical text input. On the other hand, wearable devices have some features, such as consistent user interaction and hands-free, heads-up operations, which pave the way for gesture recognition methods of text entry. This paper proposes a new text input method for smart watches, which utilizes motion sensor data and machine learning approaches…
Method for Evaluating the Number of Signal Sources and Application to Non-invasive Brain-computer Interface
Institute of Artificial Intelligence（人工智能研究所）；M.V.Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；Rebis LLC（Rebis有限责任公司）；Aicumene LLC（Aicumene有限责任公司）；Neurosputnik LLC（Neurosputnik有限责任公司）
User interfaces provide an interactive window between physical and virtual environments. A new concept in the field of human-computer interaction is a soft user interface; a compliant surface that facilitates touch interaction through deformation. Despite the potential of these interfaces, they currently lack a signal processing framework that can efficiently extract information from their deformation. Here we present OrbTouch, a device that uses statistical learning algorithms, based on convolutional neural networks, to map deformations from human touch to categorical labels (i.e., gestures) …
