Device, system, and method of detecting identity of a user based on motor-control loop model. A method includes: during a first session of a user who utilizes a pointing device for interacting with a computerized service, monitoring the pointing device dynamics and gestures of the user; based on the monitored dynamics and gestures, estimating parameters that characterize a sensorimotor control loop model of the user; storing in a database a record indicating that the user is associated with the parameters that characterize the sensorimotor control loop model of the user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: during a first session of a user who utilizes a pointing device for interacting with a computerized service, monitoring the pointing device dynamics and gestures of said user; based on the monitored dynamics and gestures, estimating parameters that characterize a sensorimotor control loop model of said user. 2. The method of claim 1, comprising: storing in a database a record indicating that said user is associated with said parameters that characterize the sensorimotor control loop model of said user. 3. The method of claim 2, comprising: in a subsequent session of interaction with said computerized service, monitoring pointing device dynamics and gestures of a subsequent user, estimating current parameters that characterize a sensorimotor control loop of said subsequent user, comparing the current parameters to said record of parameters, and based on results of said comparing, determining whether said subsequent user of the second session is the same person as said user of the first session. 4. The method of claim 2, comprising: in a subsequent session of interaction with said computerized service, monitoring pointing device gestures of a subsequent user, estimating current parameters that characterize a sensorimotor control loop of said subsequent user, comparing the current parameters to said record of parameters, and based on results of said comparing, determining whether to authenticate identity of said subsequent user. 5. The method of claim 1, wherein estimating parameters of a motor control loop of said user comprises: estimating the parameters that characterize the sensorimotor control loop as a function of translation error, current velocity, and motor control noise, based on monitored pointing device dynamics and gestures. 6. The method of claim 1, wherein estimating parameters of a motor control loop of said user comprises: estimating a linear control loop model as a linear function of translation error, current velocity, and motor control noise, based on monitored pointing device dynamics and gestures. 7. The method of claim 1, comprising: estimating parameters of a first sensorimotor control loop, associated with pointing device based interaction of a first user during a first session at said computerized service; estimating parameters of a second sensorimotor control loop, associated with pointing device based interaction of a second user during a second session at said computerized service; if the parameters of the first sensorimotor control loop match the parameters of the second sensorimotor control loop, then determining that the first user and the second user are the same person. 8. The method of claim 1, comprising: estimating parameters of a first sensorimotor control loop, associated with pointing device based interaction of a first user during a first session at a first computerized service; estimating parameters of a second sensorimotor control loop, associated with pointing device based interaction of a second user during a second session at a second, different, computerized service; if the parameters of the first sensorimotor control loop match the parameters of the second sensorimotor control loop, then determining that the first user and the second user are the same person. 9. The method of claim 1, wherein estimating the parameters of the sensorimotor control loop comprises: estimating parameters of a sensorimotor control loop which comprises sensory organ, muscle, and brain. 10. The method of claim 1, wherein estimating the parameters of the sensorimotor control loop comprises: estimating parameters of a sensorimotor control loop which comprises eye, hand, and brain coordination and control of the pointing device. 11. The method of claim 1, comprising: estimating a first user-specific biometric feature corresponding to a first motor control loop of said user across an x-axis; estimating a second user-specific biometric feature corresponding to a second motor control loop of said user across a y-axis. 12. The method of claim 11, comprising: estimating a third user-specific biometric feature by calculating a statistics function, applied to one of said first and second motor control loops; wherein the statistics function is selected from the group consisting of: mean, standard deviation, range, maximum, minimum, kurtosis, skewness, quantiles. 13. The method of claim 1, comprising: estimating a first user-specific biometric feature corresponding to a motor control loop of said user across a combination of x-axis and y-axis. 14. The method of claim 1, comprising: estimating a user-specific muscular profile which characterizes the motor control loop; estimating a user-specific coordination index which characterizes the motor control loop; differentiating between two or more users based on the user-specific muscular profile and the user-specific coordination index. 15. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating a length of an arm of said user; based on the estimated length of arm of said user, differentiating between said user and another user interacting with said computerized service. 16. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating a length of a wrist of said user; based on the estimated length of wrist of said user, differentiating between said user and another user interacting with said computerized service. 17. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating a range of a wrist of said user; based on the estimated range of wrist of said user, differentiating between said user and another user interacting with said computerized service. 18. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating level of flexibility of movement of a wrist of said user; based on the estimated level of flexibility of movement of wrist of said user, differentiating between said user and another user interacting with said computerized service. 19. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating movement agility of said user; based on the estimated movement agility of said user, differentiating between said user and another user interacting with said computerized service. 20. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating movement dexterity of said user; based on the estimated movement dexterity of said user, differentiating between said user and another user interacting with said computerized service. 21. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating a movement range of fingers of said user; based on the estimated movement range of fingers of said user, differentiating between said user and another user interacting with said computerized service. 22. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating a movement range of a mouse-wheel operating finger of said user; based on the estimated movement range of the mouse-wheel operating finger of said user, differentiating between said user and another user interacting with said computerized service. 23. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating whether or not an elbow of said user is resting on a surface; based on estimation of whether or not the elbow of said user is resting on the surface, differentiating between said user and another user interacting with said computerized service. 24. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating whether the user is right-handed; based on estimation of whether said user is right-handed, differentiating between said user and another user interacting with said computerized service. 25. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating whether the user is left-handed; based on estimation of whether said user is left-handed, differentiating between said user and another user interacting with said computerized service. 26. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating one or more parameters characterizing an eye-hand cognitive correction feedback of said user; based on the estimated one or more parameters characterizing the eye-hand cognitive correction feedback of said user, differentiating between said user and another user interacting with said computerized service. 27. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating a level of accuracy of said user in reaching an on-screen user interface element; based on the estimated level of accuracy of said user, differentiating between said user and another user interacting with said computerized service. 28. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating an eye saccade model of said user; based on the estimated eye saccade model of said user, differentiating between said user and another user interacting with said computerized service. 29. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating a smooth pursuit movement model of said user; based on the estimated smooth pursuit movement model of said user, differentiating between said user and another user interacting with said computerized service. 30. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating an eye-hand coordination model of said user in response to an introduced interference to user experience at said computerized service; based on the estimated eye-hand coordination model of said user in response to the introduced interference to user experience at said computerized service, differentiating between said user and another user interacting with said computerized service. 31. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating a level of awareness of said user to a freshly-loaded page of said computerized service; based on the estimated level of awareness of said user, differentiating between said user and another user interacting with said computerized service. 32. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating a level of awareness of said user to a freshly-modified non-reloaded page of said computerized service; based on the estimated level of awareness of said user, differentiating between said user and another user interacting with said computerized service. 33. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating a level of awareness of said user to a modification in one or more user interface elements of said computerized service; based on the estimated level of awareness of said user, differentiating between said user and another user interacting with said computerized service. 34. The method of claim 1, comprising: presenting to said user a number of choices; subsequently, modifying the number of choices presented to said user; based on the monitored pointing device dynamics and gestures of said user, estimating a level of awareness of said user to modification of the number of choices; based on the estimated level of awareness of said user, differentiating between said user and another user interacting with said computerized service. 35. The method of claim 1, comprising: based on the monitored pointing device dynamics and gestures of said user, estimating parameters of a Fitts's Law function indicating ability of said user to rapidly reach an on-screen target; based on the estimated parameters of the Fitts's Law function of said user, differentiating between said user and another user interacting with said computerized service. 36. A system comprising: a monitoring module configured to operate during a first session of a user who utilizes a pointing device for interacting with a computerized service, wherein the monitoring module is to monitor the pointing device dynamics and gestures of said user; a motor control loop model estimator, to estimate, based on the monitored dynamics and gestures, parameters that characterize a sensorimotor control loop model of said user. 37. The system of claim 36, further comprising: a database to store a record indicating that said user is associated with said parameters that characterize the sensorimotor control loop model of said user; wherein, in a subsequent session of interaction with said computerized service, the monitoring module is to monitor pointing device dynamics and gestures of a subsequent user, wherein the motor control loop model estimator is to estimate current parameters that characterize a sensorimotor control loop of said subsequent user; wherein the system comprises a comparator to compare the current parameters to said record of parameters, and based on comparison results, to determine whether said subsequent user of the second session is the same person as said user of the first session.