KamalPreet Singh Saluja

Pradipta Biswas, Himanshu Vishwakarma, Mukund Mitra et al.

Human Space Flight missions often require interaction with touchscreen displays. This paper presents a study of investigating human machine interaction with touchscreen using both finger and stylus in the International Space Station. The study also reports cognitive state of astronauts in the form of spatial 2-back test and mental well-being through self-reported scales. We presented a series of results comparing pointing and selection performance among ISS crews, ground crews and university students, finger-based touching and stylus-based touching in microgravity and mental well-being scores. We reported that finger-based pointing is statistically significantly faster than stylus-based pointing in microgravity based on analysis of 420 pointing tasks in ISS from 2 astronauts. We also did not find any significant difference among pointing performance and mental state of astronauts and students on ground. Results from the study can be used to predict pointing and selection time from dimension and position of GUI (Graphical User Interface) elements for cockpits of spacecraft.

Somnath Arjun, KamalPreet Singh Saluja, Pradipta Biswas

This paper proposes a set of techniques to investigate eye gaze and fixation patterns while users interact with electronic user interfaces. In particular, two case studies are presented - one on analysing eye gaze while interacting with deceptive materials in web pages and another on analysing graphs in standard computer monitor and virtual reality displays. We analysed spatial and temporal distributions of eye gaze fixations and sequence of eye gaze movements. We used this information to propose new design guidelines to avoid deceptive materials in web and user-friendly representation of data in 2D graphs. In 2D graph study we identified that area graph has lowest number of clusters for user's gaze fixations and lowest average response time. The results of 2D graph study were implemented in virtual and mixed reality environment. Along with this, it was ob-served that the duration while interacting with deceptive materials in web pages is independent of the number of fixations. Furthermore, web-based data visualization tool for analysing eye tracking data from single and multiple users was developed.

Vinay Krishna Sharma, L. R. D. Murthy, KamalPreet Singh Saluja et al.

Background: People with severe speech and motor impairment (SSMI) often uses a technique called eye pointing to communicate with outside world. One of their parents, caretakers or teachers hold a printed board in front of them and by analyzing their eye gaze manually, their intentions are interpreted. This technique is often error prone and time consuming and depends on a single caretaker. Objective: We aimed to automate the eye tracking process electronically by using commercially available tablet, computer or laptop and without requiring any dedicated hardware for eye gaze tracking. The eye gaze tracker is used to develop a video see through based AR (augmented reality) display that controls a robotic device with eye gaze and deployed for a fabric printing task. Methodology: We undertook a user centred design process and separately evaluated the web cam based gaze tracker and the video see through based human robot interaction involving users with SSMI. We also reported a user study on manipulating a robotic arm with webcam based eye gaze tracker. Results: Using our bespoke eye gaze controlled interface, able bodied users can select one of nine regions of screen at a median of less than 2 secs and users with SSMI can do so at a median of 4 secs. Using the eye gaze controlled human-robot AR display, users with SSMI could undertake representative pick and drop task at an average duration less than 15 secs and reach a randomly designated target within 60 secs using a COTS eye tracker and at an average time of 2 mins using the webcam based eye gaze tracker.

LRD Murthy, Somnath Arjun, Kamalpreet Singh Saluja et al.

This paper presents development of a smart sensor dashboard for Industry 4.0 encompassing both 2D and 3D visualization modules. In 2D module, we described physical connections among sensors and visualization modules and rendering data on 2D screen. A user study was presented where participants answered a few questions using four types of graphs. We analyzed eye gaze patterns in screen, number of correct answers and response time for all the four graphs. For 3D module, we developed a VR digital twin for sensor data visualization. A user study was presented evaluating the effect of different feedback scenarios on quantitative and qualitative metrics of interaction in the virtual environment. We compared visual and haptic feedback and a multimodal combination of both visual and haptic feedback for VR environment. We found that haptic feedback significantly improved quantitative metrics of interaction than a no feedback case whereas a multimodal feedback is significantly improved qualitative metrics of the interaction.