When people interact with each other, they not only listen to what the other says, they react to facial expressions, gaze direction, and head movement. Human-computer interaction would be enhanced in a friendly and non-intrusive way if computers could understand and respond to users’ body language in the same way. This thesis aims to investigate new methods for human computer interaction by combining information from the body language of the head to recognize the emotional and cognitive states. We concentrated on the integration of facial expression, eye gaze and head movement using soft computing techniques. The whole procedure is done in two-stage. The first stage focuses on the extraction of explicit information from the modalities of facial expression, head movement, and eye gaze. In the second stage, all these information are fused by soft computing techniques to infer the implicit emotional states. In this thesis, the frequency of head movement (high frequency movement or low frequency movement) is taken into consideration as well as head nods and head shakes. A very high frequency head movement may show much more arousal and active property than the low frequency head movement which differs on the emotion dimensional space. The head movement frequency is acquired by analyzing the tracking results of the coordinates from the detected nostril points. Eye gaze also plays an important role in emotion detection. An eye gaze detector was proposed to analyze whether the subject's gaze direction was direct or averted. We proposed a geometrical relationship of human organs between nostrils and two pupils to achieve this task. Four parameters are defined according to the changes in angles and the changes in the proportion of length of the four feature points to distinguish avert gaze from direct gaze. The sum of these parameters is considered as an evaluation parameter that can be analyzed to quantify gaze level. The multimodal fusion is done by hybridizing the decision level fusion and the soft computing techniques for classification. This could avoid the disadvantages of the decision level fusion technique, while retaining its advantages of adaptation and flexibility. We introduced fuzzification strategies which can successfully quantify the extracted parameters of each modality into a fuzzified value between 0 and 1. These fuzzified values are the inputs for the fuzzy inference systems which map the fuzzy values into emotional states.

The varied impact parameters that characterize an impact to the head have shown to influence the resulting type and severity of outcome injury, both in terms of the dynamic response, and the corresponding deformation of neural tissue. Therefore, when determining head injury risks through event reconstruction, it is important to understand how individual impact characteristics influence these responses. The effect of inbound mass had not yet been documented in the literature. The purpose of this study was to determine the effects of inbound mass on the dynamic impact response and brain tissue deformation. A 50th percentile Hybrid III adult male head form was impacted using a simple pendulum system. Impacts to a centric and a non-centric impact location were performed with six varied inbound masses at a velocity of 4.0 m/s. The peak linear and peak angular accelerations were measured. A finite element model, (UCDBTM) was used to determine brain deformation, namely peak maximum principal strain and peak von Mises stress. Inbound mass produced significant differences for peak linear acceleration for centric (F(5, 24) = 217.55, p=.0005) and non-centric (F(5, 24) = 161.98, p=.0005), and for peak angular acceleration for centric (F(5, 24) = 52.51, p=.0005) and non-centric (F(5, 24) = 4.18, p=.007) impact locations. A change in inbound mass also had a significant effect on peak maximum principal strain for centric (F(5, 24) = 11.04, p=.0005) and non-centric (F(5, 24) = 5.87, p =.001), and for peak von Mises stress for centric (F(5, 24) = 24.01, p=.0005) and non-centric (F(5, 24) = 4.62, p=.004) impact locations. These results indicate the inbound mass of an impact should be of consideration when determining risks and prevention to head and brain injury.

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2025-12-31

There are several essential qualities required for success as a chair of surgery. These include determination and resilience, thoughtful planning, superb organization skills, a balance of hard (accounting, management and finance) and soft skills (interpersonal including faculty development), and careful execution is absolutely essential as is a commitment to maintaining momentum.

Objectives Motorcycle helmet laws vary by state, with Kentucky requiring helmets only for younger riders. We hypothesized that motorcyclists injured in Kentucky and seen at a Tennessee trauma center would be more likely to be unhelmeted, have more severe head injuries, and sustain more fatal injuries than those injured in Tennessee or Virginia. Methods A Trauma Registry review of 729 injured motorcyclists from January 2005 through June 2015 examined state location of crash, demographics, helmet use, and clinical outcomes. Multivariate logistic regression analysis evaluated predictors for head injury severity and death. Results Unhelmeted motorcycle rider status predicted more severe head injuries (relative risk 15.3, P < 0.001) and death (relative risk 4.2, P < 0.001). Motorcyclists injured in the state of Kentucky were more likely to be unhelmeted, require an operative procedure, have more severe head injuries, have longer lengths of stay, and sustain more fatal injuries (all with < 0.001) than motorcyclists injured in Tennessee or Virginia. Conclusions This study lends support for maintaining and enforcing current universal motorcycle helmet laws for all ages in states where they are in effect and for upgrading helmet laws that apply only to some riders.

This dissertation examines the developments and progress of spatial calibration procedures for Optical See-Through (OST) Head-Mounted Display (HMD) devices for visual Augmented Reality (AR) applications. Rapid developments in commercial AR systems have created an explosion of OST device options for not only research and industrial purposes, but also the consumer market as well. This expansion in hardware availability is equally matched by a need for intuitive standardized calibration procedures that are not only easily completed by novice users, but which are also readily applicable across the largest range of hardware options. This demand for robust uniform calibration schemes is the driving motive behind the original contributions offered within this work. A review of prior surveys and canonical description for AR and OST display developments is provided before narrowing the contextual scope to the research questions evolving within the calibration domain. Both established and state of the art calibration techniques and their general implementations are explored, along with prior user study assessments and the prevailing evaluation metrics and practices employed within. The original contributions begin with a user study evaluation comparing and contrasting the accuracy and precision of an established manual calibration method against a state of the art semi-automatic technique. This is the first formal evaluation of any non-manual approach and provides insight into the current usability limitations of present techniques and the complexities of next generation methods yet to be solved. The second study investigates the viability of a user-centric approach to OST HMD calibration through novel adaptation of manual calibration to consumer level hardware. Additional contributions describe the development of a complete demonstration application incorporating user-centric methods, a novel strategy for visualizing both calibration results and registration error from the user’s perspective, as well as a robust intuitive presentation style for binocular manual calibration. The final study provides further investigation into the accuracy differences observed between user-centric and environment-centric methodologies. The dissertation concludes with a summarization of the contribution outcomes and their impact on existing AR systems and research endeavors, as well as a short look ahead into future extensions and paths that continued calibration research should explore.

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