HMOS: Head Control Mouse Person with Disability

This paper presents an idea to build a human machine interface for disable persons. The purposed idea is very economical and useful for those disable persons who cannot use their hands to control computers.The main focus is to control the mouse through their head movements by using head-tilt sensor and air blow sensor. The system uses dual axis accelerometer based tilt sensor for detecting the movement of the head which is mounted on the headset and clicking of the mouse is activated by the 2 air blow sensor which are placed near the mouth to detect the left and right click of the mouse from the effect of the air blow in to the sensors. Since the device relies only on the user?s head and air blow, so it can be used easily without requiring too much energy neither in the head movement nor in the air blow for clicking. This system encourage the disable person to start their independent professional life

Augmenting Graphical User Interfaces with Haptic Assistance for Motion-Impaired Operators

Haptic assistance is an emerging field of research that is designed to improve human-computer interaction (HCI) by reducing error rates and targeting times through the use of force feedback. Haptic feedback has previously been investigated to assist motion-impaired computer users, however, limitations such as target distracters have hampered its integration with graphical user interfaces (GUIs). In this paper two new haptic assistive techniques are presented that utilise the 3DOF capabilities of the Phantom Omni. These are referred to as deformable haptic cones and deformable virtual switches. The assistance is designed specifically to enable motion-impaired operators to use existing GUIs more effectively. Experiment 1 investigates the performance benefits of the new haptic techniques when used in conjunction with the densely populated Windows on-screen keyboard (OSK). Experiment 2 utilises the ISO 9241-9 point-and-click task to investigate the effects of target size and shape. The results of the study prove that the newly proposed techniques improve interaction rates and can be integrated with existing software without many of the drawbacks of traditional haptic assistance. Deformable haptic cones and deformable virtual switches were shown to reduce the mean number of missed-clicks by at least 75% and reduce targeting times by at least 25%

A forecasting algorithm for latency compensation in indirect human-computer interactions

International audienceHuman-computer interactions are greatly affected by the latency between the human input and the system visual response. The compensation of this latency is an important problem for the HCI (human-computer interaction) community. In this work, a simple forecasting algorithm is developed for latency compensation in indirect interaction using a mouse, based on numerical differentiation. Several differentiators are compared, including a novel algebraic version. An optimized procedure is developed for tuning the parameters of the algorithm. The efficiency is demonstrated on real data, measured with a 1 ms sampling time

Bayesian Intent Prediction in Object Tracking Using Bridging Distributions.

In several application areas, such as human computer interaction, surveillance and defence, determining the intent of a tracked object enables systems to aid the user/operator and facilitate effective, possibly automated, decision making. In this paper, we propose a probabilistic inference approach that permits the prediction, well in advance, of the intended destination of a tracked object and its future trajectory. Within the framework introduced here, the observed partial track of the object is modeled as being part of a Markov bridge terminating at its destination, since the target path, albeit random, must end at the intended endpoint. This captures the underlying long term dependencies in the trajectory, as dictated by the object intent. By determining the likelihood of the partial track being drawn from a particular constructed bridge, the probability of each of a number of possible destinations is evaluated. These bridges can also be employed to produce refined estimates of the latent system state (e.g., object position, velocity, etc.), predict its future values (up until reaching the designated endpoint) and estimate the time of arrival. This is shown to lead to a low complexity Kalman-filter-based implementation of the inference routine, where any linear Gaussian motion model, including the destination reverting ones, can be applied. Free hand pointing gestures data collected in an instrumented vehicle and synthetic trajectories of a vessel heading toward multiple possible harbors are utilized to demonstrate the effectiveness of the proposed approach

On Computer Mouse Pointing Model Online Identification and Endpoint Prediction

International audienceThis paper proposes a new simplified pointing model as a feedback-based dynamical system, including both human and computer sides of the process. It takes into account the commutation between the correction and ballistic phases in pointing tasks. We use the mouse position increment signal from noisy experimental data to achieve our main objectives: to estimate the model parameters online and predict the task endpoint. Some estimation tools and validation results, applying linear regression techniques on the experimental data are presented. We also compare with a similar prediction algorithm to show the potential of our algorithm's implementation

Bayesian Intent Prediction in Object Tracking Using Bridging Distributions.

In several application areas, such as human computer interaction, surveillance and defence, determining the intent of a tracked object enables systems to aid the user/operator and facilitate effective, possibly automated, decision making. In this paper, we propose a probabilistic inference approach that permits the prediction, well in advance, of the intended destination of a tracked object and its future trajectory. Within the framework introduced here, the observed partial track of the object is modeled as being part of a Markov bridge terminating at its destination, since the target path, albeit random, must end at the intended endpoint. This captures the underlying long term dependencies in the trajectory, as dictated by the object intent. By determining the likelihood of the partial track being drawn from a particular constructed bridge, the probability of each of a number of possible destinations is evaluated. These bridges can also be employed to produce refined estimates of the latent system state (e.g., object position, velocity, etc.), predict its future values (up until reaching the designated endpoint) and estimate the time of arrival. This is shown to lead to a low complexity Kalman-filter-based implementation of the inference routine, where any linear Gaussian motion model, including the destination reverting ones, can be applied. Free hand pointing gestures data collected in an instrumented vehicle and synthetic trajectories of a vessel heading toward multiple possible harbors are utilized to demonstrate the effectiveness of the proposed approach.The authors would like to thank Jaguar Land Rover and the UK Engineering and Physical Science Research Council (BTaRoT grant EP/K020153/1) for funding this research