Predictive feedback control and Fitts' law

Fitts’ law is a well established empirical formula, known for encapsulating the “speed-accuracy trade-off”. For discrete, manual movements from a starting location to a target, Fitts’ law relates movement duration to the distance moved and target size. The widespread empirical success of the formula is suggestive of underlying principles of human movement control. There have been previous attempts to relate Fitts’ law to engineering-type control hypotheses and it has been shown that the law is exactly consistent with the closed-loop step-response of a time-delayed, first-order system. Assuming only the operation of closed-loop feedback, either continuous or intermittent, this paper asks whether such feedback should be predictive or not predictive to be consistent with Fitts law. Since Fitts’ law is equivalent to a time delay separated from a first-order system, known control theory implies that the controller must be predictive. A predictive controller moves the time-delay outside the feedback loop such that the closed-loop response can be separated into a time delay and rational function whereas a non- predictive controller retains a state delay within feedback loop which is not consistent with Fitts’ law. Using sufficient parameters, a high-order non-predictive controller could approximately reproduce Fitts’ law. However, such high-order, “non-parametric” controllers are essentially empirical in nature, without physical meaning, and therefore are conceptually inferior to the predictive controller. It is a new insight that using closed-loop feedback, prediction is required to physically explain Fitts’ law. The implication is that prediction is an inherent part of the “speed-accuracy trade-off”

Even Turing Should Sometimes Not Be Able To Tell: Mimicking Humanoid Usage Behavior for Exploratory Studies of Online Services

Online services such as social networks, online shops, and search engines deliver different content to users depending on their location, browsing history, or client device. Since these services have a major influence on opinion forming, understanding their behavior from a social science perspective is of greatest importance. In addition, technical aspects of services such as security or privacy are becoming more and more relevant for users, providers, and researchers. Due to the lack of essential data sets, automatic black box testing of online services is currently the only way for researchers to investigate these services in a methodical and reproducible manner. However, automatic black box testing of online services is difficult since many of them try to detect and block automated requests to prevent bots from accessing them. In this paper, we introduce a testing tool that allows researchers to create and automatically run experiments for exploratory studies of online services. The testing tool performs programmed user interactions in such a manner that it can hardly be distinguished from a human user. To evaluate our tool, we conducted - among other things - a large-scale research study on Risk-based Authentication (RBA), which required human-like behavior from the client. We were able to circumvent the bot detection of the investigated online services with the experiments. As this demonstrates the potential of the presented testing tool, it remains to the responsibility of its users to balance the conflicting interests between researchers and service providers as well as to check whether their research programs remain undetected

Intelligent Interfaces to Empower People with Disabilities

Severe motion impairments can result from non-progressive disorders, such as cerebral palsy, or degenerative neurological diseases, such as Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), or muscular dystrophy (MD). They can be due to traumatic brain injuries, for example, due to a traffic accident, or to brainste

M2TA - Mobile mouse touchscreen accessible for users with motor disabilities

This paper addresses the accessibility challenges of people with motor impairments regarding their access to the computer. Our focus is a new mouse design, which in its traditional ergonomics may affect the interaction with a computer and, consequently, with the Web. We introduce the design and development of a mobile application, the M2TA, which transforms a touchscreen mobile device into a mouse controller. The mobile application provides more flexible/customizable interfaces, it is portable, and is cheaper. Two users with motor limitations, cerebral palsy, participated in the development process of the M2TA. They used mobile interfaces interacting with computer applications of their preference freely. We aimed to observe possible bugs and receive suggestions for the M2TA improvement. We also collected their satisfaction with the use of M2TA interfaces. Preliminary results are promising and indicate a good level of acceptance. Further studies are in progress to attest the M2TA potential, such as improving the quality of life of people with neuropsychomotor sequelae caused by TBI - Traumatic Brain Injury and Stroke - Stroke

A Fitts’ Law Evaluation of Hands-Free and Hands-On Input on a Laptop Computer

We used the Fitts’ law two-dimensional task in ISO 9241-9 to evaluate hands-free and hands-on point-select tasks on a laptop computer. For the hands-free method, we required a tool that can simulate the functionalities of a mouse to point and select without having to touch the device. We used a face tracking software called Camera Mouse in combination with dwell-time selection. This was compared with three hands-on methods, a touchpad with dwell-time selection, a touchpad with tap selection, and face tracking with tap selection. For hands-free input, throughput was 0.65Â bps. The other conditions yielded higher throughputs, the highest being 2.30Â bps for the touchpad with tap selection. The hands-free condition demonstrated erratic cursor control with frequent target re-entries before selection, particularly for dwell-time selection. Subjective responses were neutral or slightly favourable for hands-free input

Machine Learning Based Interaction Technique Selection For 3D User Interfaces

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