This study proposes using Fitts’ Law metrics to objectively assess laparoscopic skills, specifically the adaptation to the inverted laparoscopic tool motion. A multi-tapping Fitts’ law task is designed for a custom-developed laparoscopic virtual reality haptic training simulator. The task has two experimental conditions: with and without inverted visual tool movements, performed by two groups of twenty-four novices. The Mann–Whitney–Wilcoxon analysis indicated a significant difference in movement time (p < 0.001) between inversion and non-inversion conditions. Inverted motion takes 11.86% longer with lower throughput (2.573 bits/sec) compared to the non-inversion (3.133 bits/sec). Movement offset and variability along the y and z axes are significantly larger (p < 0.05) during inversion than in non-inversion. The findings demonstrate that Fitts’ law metrics are correlated with laparoscopic tool inversion. Additional research with surgeons of diverse skill levels will facilitate applying Fitts’ law as a standardized method for laparoscopic assessment.

Physical rehabilitation programs frequently begin with a brief stay in the hospital and continue with home-based rehabilitation. Lack of feedback on exercise correctness is a significant issue in home-based rehabilitation. Deep learning-based movement quality assessment (MQA) can assist with home-based rehabilitation by providing the necessary quantitative feedback. However, systems developed for home-based settings must be fast and offer interpretability of the generated assessment. In this paper, we explore an attention-guided transformer-based architecture for MQA. A comparative analysis against the current state-of-the-art methods is undertaken to establish the validity of the proposed model. Further, we show that the proposed model offers significant performance improvement in training and inference time, which is pivotal for any real-time system. Finally, we show that analysis of the attention maps of the proposed model can give critical insights into the decision-making process of the deep-learning model, thus improving the overall interpretability of predicted assessment scores.

Electrovibration is used in touch enabled devices to render different textures. Tactile sub-modal stimuli can enhance texture perception when presented along with electrovibration stimuli. Perception of texture depends on the threshold of electrovibration. In the current study, we have conducted a psychophysical experiment on 13 participants to investigate the effect of introducing a subthreshold electrotactile stimulus (SES) to the perception of electrovibration. Interaction of tactile sub-modal stimuli causes masking of a stimulus in the presence of another stimulus. This study explored the occurrence of tactile masking of electrovibration by electrotactile stimulus. The results indicate the reduction of electrovibration threshold by 12.46% and 6.75% when the electrotactile stimulus was at 90% and 80% of its perception threshold, respectively. This method was tested over a wide range of frequencies from 20 Hz to 320 Hz in the tuning curve, and the variation in percentage reduction with frequency is reported. Another experiment was conducted to measure the perception of combined stimuli on the Likert scale. The results showed that the perception was more inclined towards the electrovibration at 80% of SES and was indifferent at 90% of SES. The reduction in the threshold of electrovibration reveals that the effect of tactile masking by electrotactile stimulus was not prevalent under subthreshold conditions. This study provides significant insights into developing a texture rendering algorithm based on tactile sub-modal stimuli in the future.

This paper introduces mathematical models of touch sensors for mobile robots based on visibility. Serving a purpose similar to the pinhole camera model for computer vision, the introduced models are expected to provide a useful, idealized characterization of task-relevant information that can be inferred from their outputs or observations. Possible tasks include navigation, localization and mapping when a mobile robot is deployed in an unknown environment. These models allow direct comparisons to be made between traditional depth sensors, highlighting cases in which touch sensing may be interchangeable with time of flight or vision sensors, and char-acterizing unique advantages provided by touch sensing. The models include contact detection, compression, load bearing, and deflection. The results could serve as a basic building block for innovative touch sensor designs for mobile robot sensor fusion systems.

Considering 3D interactions in Virtual-Reality (VR), it is critical to study how visual awareness of real hands influences users scaled interaction performance in different VR environments. We used Fitts’s law to analyze user performance with five different Control-Display (CD) ratios (1:1 to 1:5). Fifteen participants performed a 3D selection task in three different setups: Head-Mounted Display (HMD), and two variations of the Active One-walled 3D Projection (AOP), with and without visual awareness of the real hand (AOP-A and AOP-B, respectively). The results show that the throughput of AOP-B is significantly higher than that of the AOP-A and HMD (p = .00001 and 0.0002, respectively) which suggests the existence of a conflict between the kinesthetic and visual real-hand movements, which we term as Virtual Kinesthetic Conflict (VKC). To reduce VKC during scaled movements, tasks should be designed such that the visual awareness of the real hand is avoided.

The hypothesis of this study is to verify the sensory dominance with the combinations of three sensory modalities (Audio-Haptics (AH), Visual-Haptics (VH), Audio-Visual-Haptics (AVH)) using Virtual Reality (VR) based Cardiopulmonary Resuscitation (CPR) simulator. To test this hypothesis three experiments with three different groups of participants were conducted with the above three modes of combinations. Finally, three groups were tested for their CPR performance on an unknown linear chest stiffness of mannequin-based CPR simulator and their performance score was compared. The % mean and standard deviation of the performance score (p-value: 0.00006) in the testing phase for group A-AH, B-VH, and C-AVH is 77.95%±8.27%, 89.47%±6.19%, and 91.73%±3.14% respectively. The results show that the group who trained with the three sensory modalities have better performance than that of the other two groups. Our future work is to incorporate rescue breathing in the CPR training simulator for better skill training.

Efficient velocity estimation plays an important role in the stability of haptic interfaces. In this work, a new digital circuit is realized to reduce the noise level in low velocity estimation. The proposed adaptive-method (A-method) is based on the concept of measuring the time-interval between two or more incoming quadrature pulses from the optical encoder to the order of picoseconds (ps). A time-to-digital converter (TDC) is used initially to implement the conventional velocity estimation techniques like frequency-count method (M-method) and period-count method (T-method). Later, the two methods are implemented simultaneously using the TDC for the adaptive estimation. The range of TDC used is 0–110 nanoseconds with 420–425 ps resolution. A carry-chain was implemented in order to remove non-linearity and increase precision. Experimental results demonstrate the superiority of the proposed A-method over the conventional T- and M-methods.

In traditional animation, keyframes are modeled and standard graphics pipeline is used to animate the scene. In this paper we consider volume animation where the 3D world and its components are represented as a voxel model. The voxel model is annotated with hierarchical feature information and thus facilitates easy volume editing. These annotations are based on regularised Minkowski operators used in constructing/sculpting the model. The Selective editing of volume models also becomes possible with these annotations. The pure voxel or volumetric approach used from start to finish has the advantage that extremely simple data structures and algorithms enable us to generate complex volume animations.