This work describes a new human-in-the-loop (HitL) assistive grasping system for individuals with varying levels of physical capabilities. We investigated the feasibility of using four potential input devices with our assistive grasping system interface, using able-bodied individuals to define a set of quantitative metrics that could be used to assess an assistive grasping system. We then took these measurements and created a generalized benchmark for evaluating the effectiveness of any arbitrary input device into a HitL grasping system. The four input devices were a mouse, a speech recognition device, an assistive switch, and a novel sEMG device developed by our group that was connected either to the forearm or behind the ear of the subject. These preliminary results provide insight into how different interface devices perform for generalized assistive grasping tasks and also highlight the potential of sEMG based control for severely disabled individuals.
Not only is the form of input into the system important, but so is the design of the interface. Much work and care must be put into making sure the system is robust to user input and also allows the user to accurately see what is happening in the environment as a result of the system @cite_4 . Because HitL software is so focused on getting accurate input from the user, there is not much room for ambiguity @cite_5 .
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A software system interacts with its environment through interfaces. Improper handling of exceptional returns from system interfaces can cause robustness problems. Robust- ness of software systems are governed by various tempo- ral properties related to interfaces. Static verification has been shown to be effective in checking these temporal prop- erties. But manually specifying these properties is cum- bersome and requires the knowledge of interface specifica- tions, which are often either unavailable or undocumented. In this paper, we propose a novel framework to automati- cally infer system-specific interface specifications from pro- gram source code. We use a model checker to generate traces related to the interfaces. From these model check- ing traces, we infer interface specification details such as return value on success or failure. Based on these inferred specifications, we translate generically specified interface robustness rules to concrete robustness properties verifi- able by static checking. Hence the generic rules can be specified at an abstract level that needs no knowledge of the source code, system, or interfaces. We implement our framework for an existing static analyzer that employs push down model checking and apply the analyzer to the well known POSIX-API system interfaces. We found 28 robust- ness violations in 10 open source packages using our frame- work. The user interface is the central element of a telepresence robotic system and its visualization modalities greatly affect the operator's situation awareness, and thus its performance. Depending on the task at hand and the operator's preferences, going from ego- and exocentric viewpoints and improving the depth representation can provide better perspectives of the operation environment. Our system, which combines a 3D reconstruction of the environment using laser range finder readings with two video projection methods, allows the operator to easily switch from ego- to exocentric viewpoints. This paper presents the interface developed and demonstrates its capabilities by having 13 operators teleoperate a mobile robot in a navigation task.