To Draw or Not to Draw: Recognizing Stroke-Hover Intent in Gesture-Free Bare-Hand Mid-Air Drawing Tasks

Over the past several decades, technological advancements have introduced new modes of communication with the computers, introducing a shift from traditional mouse and keyboard interfaces. While touch based interactions are abundantly being used today, latest developments in computer vision, body tracking stereo cameras, and augmented and virtual reality have now enabled communicating with the computers using spatial input in the physical 3D space. These techniques are now being integrated into several design critical tasks like sketching, modeling, etc. through sophisticated methodologies and use of specialized instrumented devices. One of the prime challenges in design research is to make this spatial interaction with the computer as intuitive as possible for the users. Drawing curves in mid-air with fingers, is a fundamental task with applications to 3D sketching, geometric modeling, handwriting recognition, and authentication. Sketching in general, is a crucial mode for effective idea communication between designers. Mid-air curve input is typically accomplished through instrumented controllers, specific hand postures, or pre-defined hand gestures, in presence of depth and motion sensing cameras. The user may use any of these modalities to express the intention to start or stop sketching. However, apart from suffering with issues like lack of robustness, the use of such gestures, specific postures, or the necessity of instrumented controllers for design specific tasks further result in an additional cognitive load on the user. To address the problems associated with different mid-air curve input modalities, the presented research discusses the design, development, and evaluation of data driven models for intent recognition in non-instrumented, gesture-free, bare-hand mid-air drawing tasks. The research is motivated by a behavioral study that demonstrates the need for such an approach due to the lack of robustness and intuitiveness while using hand postures and instrumented devices. The main objective is to study how users move during mid-air sketching, develop qualitative insights regarding such movements, and consequently implement a computational approach to determine when the user intends to draw in mid-air without the use of an explicit mechanism (such as an instrumented controller or a specified hand-posture). By recording the user’s hand trajectory, the idea is to simply classify this point as either hover or stroke. The resulting model allows for the classification of points on the user’s spatial trajectory. Drawing inspiration from the way users sketch in mid-air, this research first specifies the necessity for an alternate approach for processing bare hand mid-air curves in a continuous fashion. Further, this research presents a novel drawing intent recognition work flow for every recorded drawing point, using three different approaches. We begin with recording mid-air drawing data and developing a classification model based on the extracted geometric properties of the recorded data. The main goal behind developing this model is to identify drawing intent from critical geometric and temporal features. In the second approach, we explore the variations in prediction quality of the model by improving the dimensionality of data used as mid-air curve input. Finally, in the third approach, we seek to understand the drawing intention from mid-air curves using sophisticated dimensionality reduction neural networks such as autoencoders. Finally, the broad level implications of this research are discussed, with potential development areas in the design and research of mid-air interactions

On the role of gestures in human-robot interaction

This thesis investigates the gestural interaction problem and in particular the usage of gestures for human-robot interaction. The lack of a clear definition of the problem statement and a common terminology resulted in a fragmented field of research where building upon prior work is rare. The scope of the research presented in this thesis, therefore, consists in laying the foundation to help the community to build a more homogeneous research field. The main contributions of this thesis are twofold: (i) a taxonomy to define gestures; and (ii) an ingegneristic definition of the gestural interaction problem. The contributions resulted is a schema to represent the existing literature in a more organic way, helping future researchers to identify existing technologies and applications, also thanks to an extensive literature review. Furthermore, the defined problem has been studied in two of its specialization: (i) direct control and (ii) teaching of a robotic manipulator, which leads to the development of technological solutions for gesture sensing, detection and classification, which can possibly be applied to other contexts

Gesture-controlled interfaces for self-service machines and other applications

A gesture recognition interface for use in controlling self-service machines and other devices is disclosed. A gesture is defined as motions and kinematic poses generated by humans, animals, or machines. Specific body features are tracked, and static and motion gestures are interpreted. Motion gestures are defined as a family of parametrically delimited oscillatory motions, modeled as a linear-in-parameters dynamic system with added geometric constraints to allow for real-time recognition using a small amount of memory and processing time. A linear least squares method is preferably used to determine the parameters which represent each gesture. Feature position measure is used in conjunction with a bank of predictor bins seeded with the gesture parameters, and the system determines which bin best fits the observed motion. Recognizing static pose gestures is preferably performed by localizing the body/object from the rest of the image, describing that object, and identifying that description. The disclosure details methods for gesture recognition, as well as the overall architecture for using gesture recognition to control of devices, including self-service machines

Machine Learning approach to sport activity recognition from inertial data

In this thesis we consider an Activity recognition problem for Cross-Country Skiing; the goal of this work is to recognize different types of Cross Country techniques from inertial sensors equipped on a wear- able device. We want to apply the SAX technique to the acceleration signals, specifically on the Atomic Gestures extracted from them. Using the SAX Distance we want to able to recognize which activity an athlete is performin

User-independent accelerometer-based gesture recognition for mobile devices

Many mobile devices embed nowadays inertial sensors. This enables new forms of human-computer interaction through the use of gestures (movements performed with the mobile device) as a way of communication. This paper presents an accelerometer-based gesture recognition system for mobile devices which is able to recognize a collection of 10 different hand gestures. The system was conceived to be light and to operate in a user -independent manner in real time. The recognition system was implemented in a smart phone and evaluated through a collection of user tests, which showed a recognition accuracy similar to other state-of-the art techniques and a lower computational complexity. The system was also used to build a human -robot interface that enables controlling a wheeled robot with the gestures made with the mobile phone

Handwriting Recognition in Free Space Using WIMU-Based Hand Motion Analysis

We present a wireless-inertial-measurement-unit- (WIMU-) based hand motion analysis technique for handwriting recognition in three-dimensional (3D) space. The proposed handwriting recognition system is not bounded by any limitations or constraints; users have the freedom and flexibility to write characters in free space. It uses hand motion analysis to segment hand motion data from a WIMU device that incorporates magnetic, angular rate, and gravity sensors (MARG) and a sensor fusion algorithm to automatically distinguish segments that represent handwriting from nonhandwriting data in continuous hand motion data. Dynamic time warping (DTW) recognition algorithm is used to recognize handwriting in real-time. We demonstrate that a user can freely write in air using an intuitive WIMU as an input and hand motion analysis device to recognize the handwriting in 3D space. The experimental results for recognizing handwriting in free space show that the proposed method is effective and efficient for other natural interaction techniques, such as in computer games and real-time hand gesture recognition applications

Real-time 3d person tracking and dense stereo maps using GPU acceleration

Interfacing with a computer, especially when interacting with a virtual three di- mensional (3D) scene, found in video games for example, can be frustrating when using only a mouse and keyboard. Recent work has been focused on alternative modes of interactions, including 3D tracking of the human body. One of the essential steps in this process is acquiring depth information of the scene. Stereo vision is the process of using two separate images of the same scene, taken from slightly different positions, to get a three dimensional view of the scene. One of the largest issues with dense stereo map generation is the high processor usage, usually preventing this process from being done in real time. In order to solve this problem, this project attempts to move the bulk of the processing to the GPU. The depth map extraction is done by matching points between the images, and using the difference in their positions to determine the depth, using multiple passes in a series of openGL vertex and fragment shaders. Once a depth map has been created, the software uses it to track a person’s movement and pose in three dimensions, by tracking key points on the person across frames, and using the depth map to find the third dimension