Reliability of the Clinical Application of a Mechanical Inclinometer in Measuring Glenohumeral Motion

Objective: Establish intra- and inter-examiner reliability of glenohumeral range of motion (ROM) measures taken by a single-clinician using a mechanical inclinometer. Design: A single-session, repeated-measure, randomized, counterbalanced design. Setting: Athletic Training laboratory. Participants: Ten college-aged volunteers (9 right-hand dominant; 4 males, 6 females; age=23.2±2.4y, mass=73±16kg, height=170±8cm) without shoulder or neck injuries within one year. Interventions: Two Certified Athletic Trainers separately assessed passive glenohumeral (GH) internal (IR) and external (ER) rotation bilaterally. Each clinician secured the inclinometer to each subject’s distal forearm using elastic straps. Clinicians followed standard procedures for assessing ROM, with the participants supine on a standard treatment table with 90° of elbow flexion. A second investigator recorded the angle. Clinicians measured all shoulders once to assess inter-clinician reliability and eight shoulders twice to assess intra-clinician reliability. We used SPSS 14.0 (SPSS Inc., Chicago, IL) to calculate standard error of measure (SEM) and Intraclass Correlation Coefficients (ICC) to evaluate intra- and inter-clinician reliability. Main Outcome Measures: Dependent variables were degrees of IR, ER, glenohumeral internal rotation deficit (GIRD) and total arc of rotation. We calculated GIRD as the bilateral difference in IR (nondominant–dominant) and total arc for each shoulder (IR+ER). Results: Intra-clinician reliability for each examiner was excellent (ICC[1,1] range=0.90-0.96; SEM=2.2°-2.5°) for all measures. Examiners displayed excellent inter-clinician reliability (ICC[2,1] range=0.79-0.97; SEM=1.7°-3.0°) for all measures except nondominant IR which had good reliability(0.72). Conclusions: Results suggest that clinicians can achieve reliable measures of GH rotation and GIRD using a single-clinician technique and an inexpensive, readily available mechanical inclinometer.

3D navigation with six degrees-of-freedom using a multi-touch display

With the introduction of new input devices, such as multi-touch surface displays, the Nintendo WiiMote, the Microsoft Kinect, and the Leap Motion sensor, among others, the field of Human-Computer Interaction (HCI) finds itself at an important crossroads that requires solving new challenges. Given the amount of three-dimensional (3D) data available today, 3D navigation plays an important role in 3D User Interfaces (3DUI). This dissertation deals with multi-touch, 3D navigation, and how users can explore 3D virtual worlds using a multi-touch, non-stereo, desktop display. ^ The contributions of this dissertation include a feature-extraction algorithm for multi-touch displays (FETOUCH), a multi-touch and gyroscope interaction technique (GyroTouch), a theoretical model for multi-touch interaction using high-level Petri Nets (PeNTa), an algorithm to resolve ambiguities in the multi-touch gesture classification process (Yield), a proposed technique for navigational experiments (FaNS), a proposed gesture (Hold-and-Roll), and an experiment prototype for 3D navigation (3DNav). The verification experiment for 3DNav was conducted with 30 human-subjects of both genders. The experiment used the 3DNav prototype to present a pseudo-universe, where each user was required to find five objects using the multi-touch display and five objects using a game controller (GamePad). For the multi-touch display, 3DNav used a commercial library called GestureWorks in conjunction with Yield to resolve the ambiguity posed by the multiplicity of gestures reported by the initial classification. The experiment compared both devices. The task completion time with multi-touch was slightly shorter, but the difference was not statistically significant. The design of experiment also included an equation that determined the level of video game console expertise of the subjects, which was used to break down users into two groups: casual users and experienced users. The study found that experienced gamers performed significantly faster with the GamePad than casual users. When looking at the groups separately, casual gamers performed significantly better using the multi-touch display, compared to the GamePad. Additional results are found in this dissertation.^

3D Navigation with Six Degrees-of-Freedom using a Multi-Touch Display

With the introduction of new input devices, such as multi-touch surface displays, the Nintendo WiiMote, the Microsoft Kinect, and the Leap Motion sensor, among others, the field of Human-Computer Interaction (HCI) finds itself at an important crossroads that requires solving new challenges. Given the amount of three-dimensional (3D) data available today, 3D navigation plays an important role in 3D User Interfaces (3DUI). This dissertation deals with multi-touch, 3D navigation, and how users can explore 3D virtual worlds using a multi-touch, non-stereo, desktop display. The contributions of this dissertation include a feature-extraction algorithm for multi-touch displays (FETOUCH), a multi-touch and gyroscope interaction technique (GyroTouch), a theoretical model for multi-touch interaction using high-level Petri Nets (PeNTa), an algorithm to resolve ambiguities in the multi-touch gesture classification process (Yield), a proposed technique for navigational experiments (FaNS), a proposed gesture (Hold-and-Roll), and an experiment prototype for 3D navigation (3DNav). The verification experiment for 3DNav was conducted with 30 human-subjects of both genders. The experiment used the 3DNav prototype to present a pseudo-universe, where each user was required to find five objects using the multi-touch display and five objects using a game controller (GamePad). For the multi-touch display, 3DNav used a commercial library called GestureWorks in conjunction with Yield to resolve the ambiguity posed by the multiplicity of gestures reported by the initial classification. The experiment compared both devices. The task completion time with multi-touch was slightly shorter, but the difference was not statistically significant. The design of experiment also included an equation that determined the level of video game console expertise of the subjects, which was used to break down users into two groups: casual users and experienced users. The study found that experienced gamers performed significantly faster with the GamePad than casual users. When looking at the groups separately, casual gamers performed significantly better using the multi-touch display, compared to the GamePad. Additional results are found in this dissertation.