Vision systems with the human in the loop

The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed

Typing on Any Surface: A Deep Learning-based Method for Real-Time Keystroke Detection in Augmented Reality

Frustrating text entry interface has been a major obstacle in participating in social activities in augmented reality (AR). Popular options, such as mid-air keyboard interface, wireless keyboards or voice input, either suffer from poor ergonomic design, limited accuracy, or are simply embarrassing to use in public. This paper proposes and validates a deep-learning based approach, that enables AR applications to accurately predict keystrokes from the user perspective RGB video stream that can be captured by any AR headset. This enables a user to perform typing activities on any flat surface and eliminates the need of a physical or virtual keyboard. A two-stage model, combing an off-the-shelf hand landmark extractor and a novel adaptive Convolutional Recurrent Neural Network (C-RNN), was trained using our newly built dataset. The final model was capable of adaptive processing user-perspective video streams at ~32 FPS. This base model achieved an overall accuracy of $91.05\%$ when typing 40 Words per Minute (wpm), which is how fast an average person types with two hands on a physical keyboard. The Normalised Levenshtein Distance also further confirmed the real-world applicability of that our approach. The promising results highlight the viability of our approach and the potential for our method to be integrated into various applications. We also discussed the limitations and future research required to bring such technique into a production system

Glove-Enabled Computer Operations (GECO): Design and Testing of an Extravehicular Activity Glove Adapted for Human-Computer Interface

The Glove-Enabled Computer Operations (GECO) system enables an extravehicular activity (EVA) glove to be dual-purposed as a human-computer interface device. This paper describes the design and human participant testing of a right-handed GECO glove in a pressurized glove box. As part of an investigation into the usability of the GECO system for EVA data entry, twenty participants were asked to complete activities including (1) a Simon Says Games in which they attempted to duplicate random sequences of targeted finger strikes and (2) a Text Entry activity in which they used the GECO glove to enter target phrases in two different virtual keyboard modes. In a within-subjects design, both activities were performed both with and without vibrotactile feedback. Participants' mean accuracies in correctly generating finger strikes with the pressurized glove were surprisingly high, both with and without the benefit of tactile feedback. Five of the subjects achieved mean accuracies exceeding 99% in both conditions. In Text Entry, tactile feedback provided a statistically significant performance benefit, quantified by characters entered per minute, as well as reduction in error rate. Secondary analyses of responses to a NASA Task Loader Index (TLX) subjective workload assessments reveal a benefit for tactile feedback in GECO glove use for data entry. This first-ever investigation of employment of a pressurized EVA glove for human-computer interface opens up a wide range of future applications, including text "chat" communications, manipulation of procedures/checklists, cataloguing/annotating images, scientific note taking, human-robot interaction, and control of suit and/or other EVA systems

WearPut : Designing Dexterous Wearable Input based on the Characteristics of Human Finger Motions

Department of Biomedical Engineering (Human Factors Engineering)Powerful microchips for computing and networking allow a wide range of wearable devices to be miniaturized with high fidelity and availability. In particular, the commercially successful smartwatches placed on the wrist drive market growth by sharing the role of smartphones and health management. The emerging Head Mounted Displays (HMDs) for Augmented Reality (AR) and Virtual Reality (VR) also impact various application areas in video games, education, simulation, and productivity tools. However, these powerful wearables have challenges in interaction with the inevitably limited space for input and output due to the specialized form factors for fitting the body parts. To complement the constrained interaction experience, many wearable devices still rely on other large form factor devices (e.g., smartphones or hand-held controllers). Despite their usefulness, the additional devices for interaction can constrain the viability of wearable devices in many usage scenarios by tethering users' hands to the physical devices. This thesis argues that developing novel Human-Computer interaction techniques for the specialized wearable form factors is vital for wearables to be reliable standalone products. This thesis seeks to address the issue of constrained interaction experience with novel interaction techniques by exploring finger motions during input for the specialized form factors of wearable devices. The several characteristics of the finger input motions are promising to enable increases in the expressiveness of input on the physically limited input space of wearable devices. First, the input techniques with fingers are prevalent on many large form factor devices (e.g., touchscreen or physical keyboard) due to fast and accurate performance and high familiarity. Second, many commercial wearable products provide built-in sensors (e.g., touchscreen or hand tracking system) to detect finger motions. This enables the implementation of novel interaction systems without any additional sensors or devices. Third, the specialized form factors of wearable devices can create unique input contexts while the fingers approach their locations, shapes, and components. Finally, the dexterity of fingers with a distinctive appearance, high degrees of freedom, and high sensitivity of joint angle perception have the potential to widen the range of input available with various movement features on the surface and in the air. Accordingly, the general claim of this thesis is that understanding how users move their fingers during input will enable increases in the expressiveness of the interaction techniques we can create for resource-limited wearable devices. This thesis demonstrates the general claim by providing evidence in various wearable scenarios with smartwatches and HMDs. First, this thesis explored the comfort range of static and dynamic touch input with angles on the touchscreen of smartwatches. The results showed the specific comfort ranges on variations in fingers, finger regions, and poses due to the unique input context that the touching hand approaches a small and fixed touchscreen with a limited range of angles. Then, finger region-aware systems that recognize the flat and side of the finger were constructed based on the contact areas on the touchscreen to enhance the expressiveness of angle-based touch input. In the second scenario, this thesis revealed distinctive touch profiles of different fingers caused by the unique input context for the touchscreen of smartwatches. The results led to the implementation of finger identification systems for distinguishing two or three fingers. Two virtual keyboards with 12 and 16 keys showed the feasibility of touch-based finger identification that enables increases in the expressiveness of touch input techniques. In addition, this thesis supports the general claim with a range of wearable scenarios by exploring the finger input motions in the air. In the third scenario, this thesis investigated the motions of in-air finger stroking during unconstrained in-air typing for HMDs. The results of the observation study revealed details of in-air finger motions during fast sequential input, such as strategies, kinematics, correlated movements, inter-fingerstroke relationship, and individual in-air keys. The in-depth analysis led to a practical guideline for developing robust in-air typing systems with finger stroking. Lastly, this thesis examined the viable locations of in-air thumb touch input to the virtual targets above the palm. It was confirmed that fast and accurate sequential thumb touch can be achieved at a total of 8 key locations with the built-in hand tracking system in a commercial HMD. Final typing studies with a novel in-air thumb typing system verified increases in the expressiveness of virtual target selection on HMDs. This thesis argues that the objective and subjective results and novel interaction techniques in various wearable scenarios support the general claim that understanding how users move their fingers during input will enable increases in the expressiveness of the interaction techniques we can create for resource-limited wearable devices. Finally, this thesis concludes with thesis contributions, design considerations, and the scope of future research works, for future researchers and developers to implement robust finger-based interaction systems on various types of wearable devices.ope

Air Keyboard: Mid-Air Text Input Using Wearable EMG Sensors and a Predictive Text Model

The human body is full of electrical signals. We propose to use the electric signals produced by the human body to input text without the use of a physical keyboard. We allow users to tap their fingers in the air as if typing on an imaginary keyboard. To detect the tapping, we created a wearable armband that uses electromyography (EMG) sensors to track individual finger muscle activation. Each finger is mapped to several characters, and based on the finger-sequence the user taps, a list of possible typed words is presented. Augmented reality and virtual reality headsets are becoming more prevalent (Oculus Rift, Microsoft Hololens, Google Cardboard, Magic Leap), and yet none of the existing typing techniques allow the user to easily input text while using these devices away from a desk. Giving users the ability to input text without using a physical keyboard opens up the possibility of using AR or VR in any location. We discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research