Creating Human Machine Interfaces Using Visual Basic Pdf

Demand for teleoperation systems is increasing due to the emergence of pandemic which essentially changed the social behavior and work pattern in daily life. Physical interactions and contacts between humans are discouraged and digitalization of interactions towards remote or online interactions is accelerated. Teleoperation system is therefore, getting more attention and interest, where the applications range from telepresence systems1 in the convenience stores, to teleoperation systems at workplaces, such as teleoperation of heavy machineries at construction sites2,3,4 or industrial vehicles at warehouses5,6. However, transition from physical or manned operation to teleoperation is not easy because of issues such as implementation cost, safety, and usability of new teleoperation systems. This usability is typically dependent on the visual stimuli shown on the displays of Human Machine Interface (HMI). In case of teleoperation HMI for heavy machineries such as cranes2,3,4, the recommended visual stimuli usually cover a relatively small working area around the machine itself. Thus, views from an overhead camera covering this working area are consistently recognized as the optimal visual stimuli to facilitate teleoperation of cranes. However, these visual stimuli may not be suitable for different applications which may have different operation characteristics. For example, some applications require multiple tasks such as driving and handling of load. Thus, the attention of operators may need to have multiple perspectives.

From this section onwards, the method of computing the optimal visual stimuli Y1, Y2, and Y3 for HMI elements is elaborated. The adaptability of the HMI is supported by the ability of the system to recognize basic work states of forklift operation. In this case, the operation task defined in Fig. 2 is segmented into 14 basic work states which are typical of any forklift operations (see Fig. 6 which illustrates a cycle of basic work states). This approach is adapted from the preceding study8 which recognizes 6 basic work states. In the current study, the model is expanded to recognize 14 basic work states, thus enabling the model to recognize typical forklift work using higher resolution.

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The preference for AVS for teleoperation system can be traced to the trend in manned operation systems. Increasing sensing capability using wide angle cameras like fisheye18 and omnidirectional19 cameras provide rich visual information to operators so that it is no longer necessary to search for the desired visual information. Coupled with the improvement in computing power and cutting-edge algorithms for computer vision and machine learning, rich visual information can be processed quickly to facilitate operations of autonomous or manned systems. Therefore, in the case of semi-autonomous operations, it is important to have a support system to present the optimal visual stimuli at the appropriate timing, especially for teleoperation of multiple vehicles.

Composite user interfaces (CUIs) are UIs that interact with two or more senses. The most common CUI is a graphical user interface (GUI), which is composed of a tactile UI and a visual UI capable of displaying graphics. When sound is added to a GUI, it becomes a multimedia user interface (MUI). There are three broad categories of CUI: standard, virtual and augmented. Standard CUI use standard human interface devices like keyboards, mice, and computer monitors. When the CUI blocks out the real world to create a virtual reality, the CUI is virtual and uses a virtual reality interface. When the CUI does not block out the real world and creates augmented reality, the CUI is augmented and uses an augmented reality interface. When a UI interacts with all human senses, it is called a qualia interface, named after the theory of qualia.[citation needed] CUI may also be classified by how many senses they interact with as either an X-sense virtual reality interface or X-sense augmented reality interface, where X is the number of senses interfaced with. For example, a Smell-O-Vision is a 3-sense (3S) Standard CUI with visual display, sound and smells; when virtual reality interfaces interface with smells and touch it is said to be a 4-sense (4S) virtual reality interface; and when augmented reality interfaces interface with smells and touch it is said to be a 4-sense (4S) augmented reality interface.

The input side of the user interfaces for batch machines was mainly punched cards or equivalent media like paper tape. The output side added line printers to these media. With the limited exception of the system operator's console, human beings did not interact with batch machines in real time at all.

The spatial representation of visual field is preserved and repeated multiple time across visual cortices, especially in the early visual areas [89]. Subject-specific retinotopic mapping of early visual areas can be obtained using functional MRI and projected on the unfolded cortical surface [91]. However, this requires some degree of visual function hence it is not applicable to blind individuals. Therefore, interactive phosphene mapping after implantation of visual cortical prosthesis is a necessary step to generate spatially relevant visual sensation. The most basic paradigm of phosphene mapping involves stimulation of each electrode and recording the location of the phosphene based on subjects feedback.

Measuring threshold levels and phosphene mapping provide essential data for the integration of camera and the neurostimulator to form a brain computer interface (BCI). A visual prosthetic BCI in its simplest form maps the light intensities in the visual field to charge/amplitude levels deposited on the surface of the primary visual cortex. This however may be problematic given the limited number of electrode and nonuniform arrangement of their corresponding phosphenes in the visual field. Incorporation of machine learning into the BCI could potentially improve the device performance in different ways. The TR approach being developed by the Monash group is an example of using machine learning for image processing to produce visual perception optimized for the specific task in hand such as navigation or face detection.

There have been significant advances in decoding intracranial recording of the visual pathway to detect and discriminate the complex visual perceptions using machine learning [97,98,99]. Moreover, stimulation of cortical areas further down the visual processing stream affects higher level and complex visual perceptions [100,101,102]. For instance, stimulation of fusiform gyrus is reported to alter face perception [103, 104] and reading capabilities [105, 106]. Although our understanding of this approach is too limited to produce relevant visual information, given the to-down and recurrent organization of the visual system [107, 108], it could potentially serve as a supplement to earlier visual cortex stimulation to produce a rich and more natural visual experience.

Brock Hinzmann, a partner in the Business Futures Network who worked for 40 years as a futures researcher at SRI International, was hopeful in his comments but also issued a serious warning. He wrote: Most of the improvements in the technologies we call AI will involve machine learning from big data to improve the efficiency of systems, which will improve the economy and wealth. It will improve emotion and intention recognition, augment human senses and improve overall satisfaction in human-computer interfaces. There will also be abuses in monitoring personal data and emotions and in controlling human behavior, which we need to recognize early and thwart. Intelligent machines will recognize patterns that lead to equipment failures or flaws in final products and be able to correct a condition or shut down and pinpoint the problem. Autonomous vehicles will be able to analyze data from other vehicles and sensors in the roads or on the people nearby to recognize changing conditions and avoid accidents. In education and training, AI learning systems will recognize learning preferences, styles and progress of individuals and help direct them toward a personally satisfying outcome.

Computer vision trains machines to perform these functions, but it has to do it in much less time with cameras, data and algorithms rather than retinas, optic nerves and a visual cortex. Because a system trained to inspect products or watch a production asset can analyze thousands of products or processes a minute, noticing imperceptible defects or issues, it can quickly surpass human capabilities.

HCDE 411 Information Visualization (5) SSc/A&H

Introduces the design and presentation of digital information. Covers the use of graphics, animation, sound, and other modalities in presenting information to the user; understanding vision and perception; methods of presenting complex information to enhance comprehension and analysis; and the incorporation of visualization techniques into human-computer interfaces. Prerequisite: HCDE 308 and HCDE 310.

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HCDE 439 Physical Computing (5)

Introduction to engineering and prototyping interactive systems and environments for human-centered applications that employ basic digital electronics components and circuits. Students build systems using micro-controllers and software tools. Provides hands-on experience in a project-based, studio environment. Prerequisite: HCDE 310 or permission of instructor.

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HCDE 511 Information Visualization (4)

Covers the design and presentation of digital information. Uses graphics, animation, sound, and other modalities in presenting information to users. Studies understanding vision and perception. Includes methods of presenting complex information to enhance comprehension and analysis; and incorporation of visualization techniques into human-computer interfaces.

View course details in MyPlan: HCDE 511

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