As the increasing usage scenarios of tablet computers led to more non-neutral postures, optimizing the touchscreen gesture input became imminent to improve system performance and users’ well-being. Therefore, we conducted a study to investigate the influence of four tablet configurations and seven touchscreen gestures on electromyography, performance, and subjective assessment. Our results indicated that muscular loads of shoulder decreased under the Stand-Hand configuration while it increased under the Sit-Table during gesture interaction. We also found that Drag-Up and Drag-Left tended to possess higher muscular loads of shoulder while Drag-Down caused greater muscular loads of index finger. Besides, two-touch gestures spent longer duration when performing long-distance movements. Dragging in the inner direction was supposed to be more efficient than that in the outer direction. Our findings could provide a scientific basis for guiding the appropriate selection and the use of touchscreen interaction in the future HCI field.

Research on gesture input modalities, including touchscreen and mid-air interactions, is getting more widespread. A few studies have compared these two input modalities according to subjective methods. However, there is no empirical research on their differences in physiological measures. The purpose of this study was to quantify the differences between touchscreen and mid-air interactions when performing swipe gestures in four orthogonal directions on a tablet combining the indices of electromyography (EMG), electrogoniometry, user performance, and subjective assessment. Our results indicated that mid-air interaction obtained significantly lower muscular loads in the upper limb, smaller wrist joint excursions, shorter task completion time, and better subjective ratings than touchscreen interaction when users with elbow support performed swipe gestures on a tablet. We also found that performing swipe gestures in the vertical direction tended to possess higher muscular loads than in the horizontal direction during touchscreen interaction. Besides, we revealed that swipe right had the largest radial/ulnar deviation, and swipe down had the largest flexion/extension excursion. Furthermore, swipe up brought the worst subjective ratings among the four gesture types. These findings could provide a scientific basis for guiding the appropriate selection and use of the two input modalities in the future HCI field.

Traditional gesture elicitation studies generally adopted the frequency ratio to select popular gestures among users. However, the chosen gestures were not always optimal and might pose a potential risk of musculoskeletal disorder under long-term use. The purpose of our research was to apply a novel assessment system combining the indices of electromyography (EMG), electrogoniometry, and subjective preference to the end-user elicitation experiment. In this study, we conducted a two-stage experiment to compare and analyze the results of the physiological and psychological measures for 33 candidate gestures of 16 given commands. Our results indicated that this assessment system could effectively determine the candidate gestures with lower physiological loads for all given commands except Maximize, and most of the selected gestures were in line with users’ mental models. Finally, we developed an optimal user-defined gesture set for the commands accommodated to human-computer interaction (HCI).

In this paper, a novel hardware implementation of analog deep neural network (DNN) based on NOR Flash Computing Array (NFCA) is presented. The approach eliminates additional analog-to-digital/digital-to-analog (AD/DA) conversion between adjacent layers. Applied to the MNIST recognition task, the simulations indicate that the designed DNN based on the novel implementation approach has the excellent performance such as the time delay of 3×10-7s and energy consumption of 1.97×10-8J per image, which brings 8× and 123× enhancements compared to the conventional digital scheme. The NFCA based analog DNN also saves 86.4% of area. All of the improvements benefit from the analog-signal based scheme. The proposed high speed and energy efficient hardware implementation would be promising in terms of artificial intelligence (AI) at the edge.

Soft-tissue deformation presents a confounding factor to rigid image registration by introducing image content inconsistent with the underlying motion model, presenting non-correspondent structure with potentially high power, and creating local minima that challenge iterative optimization. In this paper, we introduce a model for registration performance that includes deformable soft tissue as a power-law noise distribution within a statistical framework describing the Cramer-Rao lower bound (CRLB) and root-mean-squared error (RMSE) in registration performance. The model incorporates both cross-correlation and gradient-based similarity metrics, and the model was tested in application to 3D-2D (CT-to-radiograph) and 3D-3D (CT-to-CT) image registration. Predictions accurately reflect the trends in registration error as a function of dose (quantum noise), and the choice of similarity metrics for both registration scenarios. Incorporating soft-tissue deformation as a noise source yields important insight on the limits of registration performance with respect to algorithm design and the clinical application or anatomical context. For example, the model quantifies the advantage of gradient-based similarity metrics in 3D-2D registration, identifies the low-dose limits of registration performance, and reveals the conditions for which the registration performance is fundamentally limited by soft-tissue deformation.

A novel convolution computing paradigm based on the NOR Flash Array is proposed. Significant improvements both in computing speed and energy consumption are achieved compared to CMOS-based logic computing paradigms. Regarding to the feature extraction task from a 256×256 image, the computing speed of 3.9×104frame per second (fps) and the energy consumption of 0.057nJ/pixel are achieved using the proposed computing paradigm.

For image-guided procedures, the imaging task is often tied to the registration of intraoperative and preoperative images to a common coordinate system. While the accuracy of this registration is a vital factor in system performance, there is a relatively little work that relates registration accuracy to image quality factors, such as dose, noise, and spatial resolution. To create a theoretical model for such a relationship, we present a Fisher information approach to analyze registration performance in explicit dependence on the underlying image quality factors of image noise, spatial resolution, and signal power spectrum. The model yields analysis of the Cramer-Rao lower bound (CRLB), in registration accuracy as a function of factors governing image quality. Experiments were performed in simulation of computed tomography low-contrast soft tissue images and high-contrast bone (head and neck) images to compare the measured accuracy [root mean squared error (RMSE) of the estimated transformations] with the theoretical lower bound. Analysis of the CRLB reveals that registration performance is closely related to the signal-to-noise ratio of the cross-correlation space. While the lower bound is optimistic, it exhibits consistent trends with experimental findings and yields a method for comparing the performance of various registration methods and similarity metrics. Further analysis validated a method for determining optimal post-processing (image filtering) for registration. Two figures of merit (CRLB and RMSE) are presented that unify models of image quality with registration performance, providing an important guide to optimizing intraoperative imaging with respect to the task of registration.