No description available.

No description available.

Artificial Intelligence (AI) promises to deeply alter the structure of organizations and work. This dissertation explores how firms and their human workers interact with the diffusion of automation and related technologies in the workplace, and how this informs our general understanding of organizations. I use three experiments to examine the consequences and implications of human-machine interaction in organizations. Chapter 1 studies the introduction of AI agents and human new hires into "laboratory firms" as they engage in a coordination-based game. Chapter 2 focuses on the sources of AI bias and offers practical solutions managers can adopt to limit bias. Finally, Chapter 3 studies how organizations can enjoy the benefits of AI and ensure that human collaborators remain engaged and exert effort. Overall, my dissertation develops an organizational and team perspective on the impact of workplace automation. Successful human/AI collaboration requires going beyond the technical capabilities of AI and developing a human-centered approach that incorporates firm strategies, behavioral responses, and managerial choices.

Decision making in a parallel human/computer system is considered. In this type of system, those tasks for which the computer identical to has the decision making responsibility tasks for which the human are has responsibility. For optimal system performance, it is crucial that the human and computer avoid redundant actions. The traditional method of avoiding redundancies is to have the human engage in an. explicit dialogue with the computer. This method adds an additional task for the human. An alternative method which does not increase workload is to provide the computer with a model of human decision making. If this model is accurate, the computer could predict the actions of the human and avoid those actions which are redundant. The mathematical development of such a predictive model is presented. The model suggested has two stages. The first stage uses discriminant analysis to describe human event detection behavior. The output from the first stage of the model is a vector of "event detected" probabilities, each entry in the vector representing a different system task. The second stage of the model uses dynamic programming to determine the optimal action at a specific point in time. The output from this stage of the model is the appropriate action for the human to take. Two experiments were presented to validate the first and second stage of the model, respectively. The experimental situation depicted a sheet metal plant in which the subjects were to monitor machines for failures. The first stage of the model predicted over 80% of the actions correctly, while the entire model predicted nearly 85% correctly. In the third experiment, the computer was implemented as a parallel decision maker. A significant improvement in performance was observed when the computer based decisions on a model of human decision making vs. when the model was ignored. A modeling approach is suggested as a reasonable alternative to explicit human/computer systems. communication in the design of Further research is suggested to determine the situations in which model based communication would be preferable to dialogue based communication. / Ph. D.

Little research effort has been devoted to human interaction with telephone information systems. This study investigated the effects of system parameters and user characteristics on human behavior in an interactive telephone-based information system. The research method utilized a centraI-composite design to study four variables at five levels each. The four factors manipulated were: synthesized speech rate, time available for user input, subject age, and background music level. Subjects searched a fictitious department store database for 16 specific store items and transcribed 16 information messages which were spoken by a computer speech synthesizer. Subjective ratings of certain features of the system were solicited from the subjects and performance measures were also collected from the subjects on an on-line basis. Performance was evaluated by calculating regression equations relating the dependent measures and the independent variables. A response surface was plotted, and optimal settings for the Information system were also calculated. Two seconds was found to be an optimal time for users to enter their selection. The computer synthesized speech rate should be set close to 120-150 words per minute. Background music or noise level should be kept below 50 dB(A); sound level above 50 dB(A) seriously affected user's ability to understand synthetic speech. Younger subjects (age 14 - 22) performed better in this study than older subjects (age 36- 62). / Master of Science / incomplete_metadata

This study examined the effects of five dichotomous variables on human performance using a computer-based telephone information system. The five variables were: speech rate (120 or 240 words per minute), length of input time-out (two or ten seconds), feedback (available or not available), wallet guide - a graphical representation of the information (available or not available), and the database structure (8x2 or 2x6). The research methodology implemented a one-half fraction of a 2⁵ factorial design, requiring only 16 of the 32 possible treatment combinations. Two tasks were included in this study: a search task and a transcription task. The search task consisted of each subject accessing an information system through a touch-tone telephone. The subject listened to the computer as it used synthesized speech to list available menu options. The search task continued until the subject found the target item. The transcription task consisted of listening to and typing an information message for each target item. The experiment ended when 16 target items were found. Four dependent measures were used to evaluate user performance. The search task was evaluated with three measures: user added time (the amount of additional time the subject required to complete the search in excess of the minimum search time imposed by the system design); invalid key presses (the number of times undefined keys on the touch tone telephone were pressed during the search); and user added key presses (the number of additional, valid key presses the subject required to complete the search in excess of the minimum number of key presses required to complete the search). Only one measure was used to evaluate user performance of the transcription task: transcription accuracy score (the number of words that each subject transcribed correctly). The results show four of the five variables (speech rate, database structure, input time-out, and wallet guide) to have a significant effect on human performance. The following interactions were found to have a significant effect on at least one of the dependent measures: database structure by input timeout, database structure by wallet guide, input timeout by wallet guide, and speech rate by wallet guide. Twelve subjective ratings were also analyzed. The results show at least one of the 12 subjective ratings was significantly affected by speech rate, input time-out, or the database structure. Perhaps the most important finding of this research is that complicated auditory information structures can be accessed easily if a wallet guide is provided. In addition to decreasing search time, a wallet guide reduces the number of search errors users make. / Master of Science

Objectives: Research suggests the general public has a lack of faith in highly automated vehicles (HAV) stems from a lack of system transparency while in motion (e.g., the user not being informed on roadway perception or anticipated responses of the car in certain situations). This problem is particularly prevalent in public transit or ridesharing applications, where HAVs are expected to debut, and when the user has minimal training on, and control over, the vehicle. To improve user trust and their perception of comfort and safety, this study aimed to develop more detailed and tailored human-machine interfaces (HMI) aimed at relying automated vehicle intended actions (i.e., "intentions") and perceptions of the driving environment to the user. Methods: This project developed HMI systems, with a focus on visual and auditory displays, and implemented them into a HAV developed at the Virginia Tech Transportation Institute (VTTI). Volunteer participants were invited to the Smart Roads at VTTI to experience these systems in real-world driving scenarios, especially ones typically found in rideshare or public transit operations. Participant responses and opinions about the HMIs and their perceived levels of comfort, safety, trust, and situational awareness were captured via paper-based surveys administered during experimentation. Results: There was a considerable link found between HMI modality and users' reported levels of comfort, safety, trust, and situational awareness during experimentation. In addition, there were several key behavioral factors that made users more or less likely to feel comfortable in the HAV. Conclusions: Moving forward, it will be necessary for HAVs to provide ample feedback to users in an effort to increase system transparency and understanding. Feedback should consistently and accurately represent the driving landscape and clearly communicate vehicle states to users. / Master of Science / One of the greatest barriers to the entry of highly automated vehicles (HAV) into the market is the lack of user trust in the vehicle. Research has shown that this lack of faith in the system primarily stems from a lack of system transparency while in motion (e.g., the user not being told how the car will react in a certain situation) and not having an effective way to control the vehicle in the event of a system failure. This problem is particularly prevalent in public transit or ridesharing applications, where HAVs are expected to first appear and where the user has less training and control over the vehicle. To improve user trust and perceptions of comfort and safety, this study developed human-machine interface (HMI) systems, focusing on visual and auditory displays, to better relay automated vehicle "intentions" and the perceived driving environment to the user. These HMI systems were then implemented into a HAV developed at the Virginia Tech Transportation Institute (VTTI) and tested with volunteer participants on the Smart Roads.

<p dir="ltr">As automated vehicle (AV) systems become increasingly more intelligent, understanding the complex interplay between drivers' trust in these systems and their resulting behavior is paramount for the successful integration of autonomous technologies into the transportation landscape. Currently, the effects of displaying AV system-certainty information, concerning its navigability around obstacles, on drivers' trust, decision-making, and behavior is underexplored. This thesis seeks to address this research gap and evaluate a set of dynamic and continuous human-machine interfaces (HMIs) that present self-assessed system-certainty information to drivers of AVs. A simulated driving study was conducted wherein participants were exposed to four different linear and curvilinear AV system-certainty patterns when their AV approached a construction zone. The certainty patterns represented the vehicle’s confidence in safely avoiding the construction. Using this information, drivers needed to decide whether or not to take over from the vehicle. The AV’s reliability and system-certainty were not directly proportional to one another. During the study, drivers' trust, workload, takeover decisions and performance, eye movement behavior, and heart?rate measures were captured to comprehensively understand of the factors influencing drivers' interactions with automated vehicles. Overall, participants took over in 41.3% of the drives. Results suggest that the communication of different system-certainty trends had a significant effect on drivers’ takeover response times and gaze behavior, but did not affect their trust in the system nor their workload. Ultimately, the results of this work can be used to inform the design of in vehicle interfaces in future autonomous vehicles, aiming to enhance safety and driver acceptance. By elucidating the intricate relationship between drivers' trust and behavior, this study provides valuable insights for both researchers and developers, contributing to the ongoing discourse on the human factors associated with the integration of autonomous technologies into the transportation ecosystem.</p>

This dissertation focuses on improving the capabilities of an assistive robotic exoskeleton glove designed for patients with Brachial Plexus Injuries (BPI). The aim of this research is to develop a force control method, an automatic force planning method, and a Human-Machine Interface (HMI) to refine the grasping functionalities of the exoskeleton glove, thus helping rehabilitation and independent living for individuals with BPI. The exoskeleton glove is a useful tool in post-surgery therapy for patients with BPI, as it helps counteract hand muscle atrophy by allowing controlled and assisted hand movements. This study introduces an assistive exoskeleton glove with rigid side-mounted linkages driven by Series Elastic Actuators (SEAs) to perform five different types of grasps. In the aspect of force control, data-driven SEA fingertip force prediction methods were developed to assist force control with the Linear Series Elastic Actuators (LSEAs). This data-driven force prediction method can provide precise prediction of SEA fingertip force taking into account the deformation and friction force on the exoskeleton glove. In the aspect of force planning, a slip-grasp force planning method with hybrid slip detection is implemented. This method incorporates a vision-based approach to estimate object properties to refine grasp force predictions, thus mimicking human grasping processes and reducing the trial-and-error iterations required for the slip- grasp method, increasing the grasp success rate from 71.9% to 87.5%. In terms of HMI, the Configurable Voice Activation and Speaker Verification (CVASV) system was developed to control the proposed exoskeleton glove, which was then complemented by an innovative one-shot learning-based alternative, which proved to be more effective than CVASV in terms of training time and connectivity requirements. Clinical trials were conducted successfully in patients with BPI, demonstrating the effectiveness of the exoskeleton glove. / Doctor of Philosophy / This dissertation focuses on improving the capabilities of a robotic exoskeleton glove designed to assist individuals with Brachial Plexus Injuries (BPI). The goal is to enhance the glove's ability to grasp and manipulate objects, which can help in the recovery process and enable patients with BPI to live more independently. The exoskeleton glove is a tool for patients with BPI to used after surgery to prevent the muscles of the hand from weakening due to lack of use. This research introduces an exoskeleton glove that utilizes special mechanisms to perform various types of grasp. The study has three main components. First, it focuses on ensuring that the glove can accurately control its grip strength. This is achieved through a special method that takes into account factors such as how the materials in the glove change when it moves and the amount of friction present. Second, the study works on a method for planning how much force the glove should use to hold objects without letting them slip. This method combines a camera-based object and material detection to estimate the weight and size of the target object, making the glove better at holding things without dropping them. The third part involves designing how people can instruct the glove what to do. The command can be sent to the robot by voice. This study proposed a new method that quickly learns how you talk and recognizes your voice. The exoskeleton glove was tested on patients with BPI and the results showed that it is successful in helping them. This study enhances assistive technology, especially in the field of assistive exoskeleton glove, making it more effective and beneficial for individuals with hand disabilities.

Yes / The development of many tools and technologies for people with visual impairment has become a major priority in the field of assistive technology research. However, many of these technology advancements have limitations in terms of the human aspects of the user experience (e.g., usability, learnability, and time to user adaptation) as well as difficulties in translating research prototypes into production. Also, there was no clear distinction between the assistive aids of adults and children, as well as between “partial impairment” and “total blindness”. As a result of these limitations, the produced aids have not gained much popularity and the intended users are still hesitant to utilise them. This paper presents a comprehensive review of substitutive interventions that aid in adapting to vision loss, centred on laboratory research studies to assess user-system interaction and system validation. Depending on the primary cueing feedback signal offered to the user, these technology aids are categorized as visual, haptics, or auditory-based aids. The context of use, cueing feedback signals, and participation of visually impaired people in the evaluation are all considered while discussing these aids. Based on the findings, a set of recommendations is suggested to assist the scientific community in addressing persisting challenges and restrictions faced by both the totally blind and partially sighted people.