Determining Desirable Cursor Control Device Characteristics for NASA Exploration Missions

A test battery was developed for cursor control device evaluation: four tasks were taken from ISO 9241-9, and three from previous studies conducted at NASA. The tasks focused on basic movements such as pointing, clicking, and dragging. Four cursor control devices were evaluated with and without Extravehicular Activity (EVA) gloves to identify desirable cursor control device characteristics for NASA missions: 1) the Kensington Expert Mouse, 2) the Hulapoint mouse, 3) the Logitech Marble Mouse, and 4) the Honeywell trackball. Results showed that: 1) the test battery is an efficient tool for differentiating among input devices, 2) gloved operations were about 1 second slower and had at least 15% more errors; 3) devices used with gloves have to be larger, and should allow good hand positioning to counteract the lack of tactile feedback, 4) none of the devices, as designed, were ideal for operation with EVA gloves

The Challenges in Modeling Human Performance in 3D Space with Fitts’ Law

With the rapid growth in virtual reality technologies, object interaction is becoming increasingly more immersive, elucidating human perception and leading to promising directions towards evaluating human performance under different settings. This spike in technological growth exponentially increased the need for a human performance metric in 3D space. Fitts' law is perhaps the most widely used human prediction model in HCI history attempting to capture human movement in lower dimensions. Despite the collective effort towards deriving an advanced extension of a 3D human performance model based on Fitts' law, a standardized metric is still missing. Moreover, most of the extensions to date assume or limit their findings to certain settings, effectively disregarding important variables that are fundamental to 3D object interaction. In this review, we investigate and analyze the most prominent extensions of Fitts' law and compare their characteristics pinpointing to potentially important aspects for deriving a higher-dimensional performance model. Lastly, we mention the complexities, frontiers as well as potential challenges that may lay ahead.Comment: Accepted at ACM CHI 2021 Conference on Human Factors in Computing Systems (CHI '21 Extended Abstracts

Process Mining IPTV Customer Eye Gaze Movement Using Discrete-time Markov Chains

Human-Computer Interaction (HCI) research has extensively employed eye-tracking technologies in a variety of fields. Meanwhile, the ongoing development of Internet Protocol TV (IPTV) has significantly enriched the TV customer experience, which is of great interest to researchers across academia and industry. A previous study was carried out at the BT Ireland Innovation Centre (BTIIC), where an eye tracker was employed to record user interactions with a Video-on-Demand (VoD) application, the BT Player. This paper is a complementary and subsequent study of the analysis of eye-tracking data in our previously published introductory paper. Here, we propose a method for integrating layout information from the BT Player with mining the process of customer eye movement on the screen, thereby generating HCI and Industry-relevant insights regarding user experience. We incorporate a popular Machine Learning model, a discrete-time Markov Chain (DTMC), into our methodology, as the eye tracker records each gaze movement at a particular frequency, which is a good example of discrete-time sequences. The Markov Model is found suitable for our study, and it helps to reveal characteristics of the gaze movement as well as the user interface (UI) design on the VoD application by interpreting transition matrices, first passage time, proposed ‘most likely trajectory’ and other Markov properties of the model. Additionally, the study has revealed numerous promising areas for future research. And the code involved in this study is open access on GitHub

HUMAN CONTROL OF ROBOTIC MECHANISMS: MODELLING AND ASSESSMENT OF ASSISTIVE DEVICES

The prescription and use of Assistive Technology, particularly teleprostheses, may be enhanced by the use of standard assessment techniques. For input devices, in particular, existing assessment studies, most of which are based on Fitts' Law, have produced contradictory results. This thesis has made contributions to these and related fields, particularly in the following four areas. Fitts' Law (and background information theory) is examined. The inability of this paradigm to match experimental results is noted and explained. Following a review of the contributing fields, a new method of assessing input devices is proposed, based on Fitts' Law, classical control and the concept of 'profiling'. To determine the suitability of the proposed method, it is applied to the results of over 2000 trials. The resulting analysis emphasises the importance of interaction effects and their influence on general comparison techniques for input devices. The process of verification has highlighted gain susceptability as a performance criterion which reflects user susceptability; a technique which may be particularly applicable to Assistive Technology.Dept. of Mechanical and Marine Engineerin

Brain-Machine Interface for Reaching: Accounting for Target Size, Multiple Motor Plans, and Bimanual Coordination

<p>Brain-machine interfaces (BMIs) offer the potential to assist millions of people worldwide suffering from immobility due to loss of limbs, paralysis, and neurodegenerative diseases. BMIs function by decoding neural activity from intact cortical brain regions in order to control external devices in real-time. While there has been exciting progress in the field over the past 15 years, the vast majority of the work has focused on restoring of motor function of a single limb. In the work presented in this thesis, I first investigate the expanded role of primary sensory (S1) and motor (M1) cortex during reaching movements. By varying target size during reaching movements, I discovered the cortical correlates of the speed-accuracy tradeoff known as Fitts' law. Similarly, I analyzed cortical motor processing during tasks where the motor plan is quickly reprogrammed. In each study, I found that parameters relevant to the reach, such as target size or alternative movement plans, could be extracted by neural decoders in addition to simple kinematic parameters such as velocity and position. As such, future BMI functionality could expand to account for relevant sensory information and reliably decode intended reach trajectories, even amidst transiently considered alternatives.</p><p> The second portion of my thesis work was the successful development of the first bimanual brain-machine interface. To reach this goal, I expanded the neural recordings system to enable bilateral, multi-site recordings from approximately 500 neurons simultaneously. In addition, I upgraded the experiment to feature a realistic virtual reality end effector, customized primate chair, and eye tracking system. Thirdly, I modified the tuning function of the unscented Kalman filter (UKF) to conjointly represent both arms in a single 4D model. As a result of widespread cortical plasticity in M1, S1, supplementary motor area (SMA), and posterior parietal cortex (PPC), the bimanual BMI enabled rhesus monkeys to simultaneously control two virtual limbs without any movement of their own body. I demonstrate the efficacy of the bimanual BMI in both a subject with prior task training using joysticks and a subject naïve to the task altogether, which simulates a common clinical scenario. The neural decoding algorithm was selected as a result of a methodical comparison between various neural decoders and decoder settings. I lastly introduce a two-stage switching model with a classify step and predict step which was designed and tested to generalize decoding strategies to include both unimanual and bimanual movements.</p>Dissertatio

Development of a construction methodology of goal directed, optimal complexity, flexible and task oriented (GOFT) training materials for novice computer users: application and evaluation in adults with mental health problems

A number of information technology schemes have been developed in order to provide people with mental health problems the opportunity to acquire skills in micro-computer technology. Even though positive results have been reported a high incidence of dropouts during the beginning of the training have been found. The research is based on the assumption that in order for a computer training method to be effective in fostering computer skills and confidence to adult novice users with mental health problems has to: (a) bridge the gap between the user's capacities, needs, and preferences and the demands of the computer interfaces and their real task applications; (b) consider the ways adult novice users prefer to learn and the skill acquisition theories; (c) facilitate a goal directed interaction with the computer system; (d) maintain an optimal complexity level across training; and (e) allow flexibility of use. Based on the relevant literature, a methodology model and a set of design propositions and construction guidelines have been derived and have been implemented for the development of Goaldirected, optimal complexity, Flexible & Task oriented (GOFT) training materials for adult, novice users with mental health problems. The GOFT training materials were based on three different models, the one for the creation of a goal directed instruction format and the other two for the organisation of the training, and the estimation of the difficulty level of each new computer operation or real task application. Evaluation of use of the GOFT Training Materials by 34 adult, novice users (aged 18-51) with mental health problems revealed positive results. More specifically, the use of the GOFT training materials as compared to traditional methods resulted in a significant increase in the number of participants at the different training stages (85.3% versus 47.2%; and 44.5% versus 22.2% at three and twelve months respectively), in perfect & regular attendance rate ( 44,12% versus 11.11% & 32.35% versus 16.67%) and in the performance level (means of 3.75 versus 2.67) of the users. The subjective evaluation by the users also revealed significant differences between the GOFT and traditional training materials. In their evaluation the GOFT materials were rated significantly higher in terms of systematic arrangement, personal affect, understandability, task relevance, fitness, sense of control, confidence in using the mastered functions and in supporting goal directed learning approach