arXiv subjects

Juan Wachs

Publications and source records attributed to Juan Wachs.

24 records · Page 2Linked to original sources

arXiv2020

DAISI: Database for AI Surgical Instruction

Telementoring surgeons as they perform surgery can be essential in the treatment of patients when in situ expertise is not available. Nonetheless, expert mentors are often unavailable to provide trainees with real-time medical guidance. When mentors are unavailable, a fallback autonomous mechanism should provide medical practitioners with the required guidance. However, AI/autonomous mentoring in medicine has been limited by the availability of generalizable prediction models, and surgical procedures datasets to train those models with. This work presents the initial steps towards the development of an intelligent artificial system for autonomous medical mentoring. Specifically, we present the first Database for AI Surgical Instruction (DAISI). DAISI leverages on images and instructions to provide step-by-step demonstrations of how to perform procedures from various medical disciplines. The dataset was acquired from real surgical procedures and data from academic textbooks. We used DAISI to train an encoder-decoder neural network capable of predicting medical instructions given a current view of the surgery. Afterwards, the instructions predicted by the network were evaluated using cumulative BLEU scores and input from expert physicians. According to the BLEU scores, the predicted and ground truth instructions were as high as 67% similar. Additionally, expert physicians subjectively assessed the algorithm using Likert scale, and considered that the predicted descriptions were related to the images. This work provides a baseline for AI algorithms to assist in autonomous medical mentoring.

cs.CV↗

arXiv2019

Gesture Agreement Assessment Using Description Vectors

Participatory design is a popular design technique that involves the end users in the early stages of the design process to obtain user-friendly gestural interfaces. Guessability studies followed by agreement analyses are often used to elicit and comprehend the preferences (or gestures/proposals) of the participants. Previous approaches to assess agreement, grouped the gestures into equivalence classes and ignored the integral properties that are shared between them. In this work, we represent the gestures using binary description vectors to allow them to be partially similar. In this context, we introduce a new metric referred to as soft agreement rate (SAR) to quantify the level of consensus between the participants. In addition, we performed computational experiments to study the behavior of our partial agreement formula and mathematically show that existing agreement metrics are a special case of our approach. Our methodology was evaluated through a gesture elicitation study conducted with a group of neurosurgeons. Nevertheless, our formulation can be applied to any other user-elicitation study. Results show that the level of agreement obtained by SAR metric is 2.64 times higher than the existing metrics. In addition to the mostly agreed gesture, SAR formulation also provides the mostly agreed descriptors which can potentially help the designers to come up with a final gesture set.

cs.HC↗

arXiv2019

Extending Policy from One-Shot Learning through Coaching

Humans generally teach their fellow collaborators to perform tasks through a small number of demonstrations. The learnt task is corrected or extended to meet specific task goals by means of coaching. Adopting a similar framework for teaching robots through demonstrations and coaching makes teaching tasks highly intuitive. Unlike traditional Learning from Demonstration (LfD) approaches which require multiple demonstrations, we present a one-shot learning from demonstration approach to learn tasks. The learnt task is corrected and generalized using two layers of evaluation/modification. First, the robot self-evaluates its performance and corrects the performance to be closer to the demonstrated task. Then, coaching is used as a means to extend the policy learnt to be adaptable to varying task goals. Both the self-evaluation and coaching are implemented using reinforcement learning (RL) methods. Coaching is achieved through human feedback on desired goal and action modification to generalize to specified task goals. The proposed approach is evaluated with a scooping task, by presenting a single demonstration. The self-evaluation framework aims to reduce the resistance to scooping in the media. To reduce the search space for RL, we bootstrap the search using least resistance path obtained using resistive force theory. Coaching is used to generalize the learnt task policy to transfer the desired quantity of material. Thus, the proposed method provides a framework for learning tasks from one demonstration and generalizing it using human feedback through coaching.

cs.RO↗

arXiv2019

DESK: A Robotic Activity Dataset for Dexterous Surgical Skills Transfer to Medical Robots

Datasets are an essential component for training effective machine learning models. In particular, surgical robotic datasets have been key to many advances in semi-autonomous surgeries, skill assessment, and training. Simulated surgical environments can enhance the data collection process by making it faster, simpler and cheaper than real systems. In addition, combining data from multiple robotic domains can provide rich and diverse training data for transfer learning algorithms. In this paper, we present the DESK (Dexterous Surgical Skill) dataset. It comprises a set of surgical robotic skills collected during a surgical training task using three robotic platforms: the Taurus II robot, Taurus II simulated robot, and the YuMi robot. This dataset was used to test the idea of transferring knowledge across different domains (e.g. from Taurus to YuMi robot) for a surgical gesture classification task with seven gestures. We explored three different scenarios: 1) No transfer, 2) Transfer from simulated Taurus to real Taurus and 3) Transfer from Simulated Taurus to the YuMi robot. We conducted extensive experiments with three supervised learning models and provided baselines in each of these scenarios. Results show that using simulation data during training enhances the performance on the real robot where limited real data is available. In particular, we obtained an accuracy of 55% on the real Taurus data using a model that is trained only on the simulator data. Furthermore, we achieved an accuracy improvement of 34% when 3% of the real data is added into the training process.

cs.RO↗

arXiv2017

What makes a gesture a gesture? Neural signatures involved in gesture recognition

Previous work in the area of gesture production, has made the assumption that machines can replicate "human-like" gestures by connecting a bounded set of salient points in the motion trajectory. Those inflection points were hypothesized to also display cognitive saliency. The purpose of this paper is to validate that claim using electroencephalography (EEG). That is, this paper attempts to find neural signatures of gestures (also referred as placeholders) in human cognition, which facilitate the understanding, learning and repetition of gestures. Further, it is discussed whether there is a direct mapping between the placeholders and kinematic salient points in the gesture trajectories. These are expressed as relationships between inflection points in the gestures' trajectories with oscillatory mu rhythms (8-12 Hz) in the EEG. This is achieved by correlating fluctuations in mu power during gesture observation with salient motion points found for each gesture. Peaks in the EEG signal at central electrodes (motor cortex) and occipital electrodes (visual cortex) were used to isolate the salient events within each gesture. We found that a linear model predicting mu peaks from motion inflections fits the data well. Increases in EEG power were detected 380 and 500ms after inflection points at occipital and central electrodes, respectively. These results suggest that coordinated activity in visual and motor cortices is sensitive to motion trajectories during gesture observation, and it is consistent with the proposal that inflection points operate as placeholders in gesture recognition.

cs.HC↗

arXiv2017

Coherency in One-Shot Gesture Recognition

User's intentions may be expressed through spontaneous gesturing, which have been seen only a few times or never before. Recognizing such gestures involves one shot gesture learning. While most research has focused on the recognition of the gestures itself, recently new approaches were proposed to deal with gesture perception and production as part of the same problem. The framework presented in this work focuses on learning the process that leads to gesture generation, rather than mining the gesture's associated features. This is achieved using kinematic, cognitive and biomechanic characteristics of human interaction. These factors enable the artificial production of realistic gesture samples originated from a single observation. The generated samples are then used as training sets for different state-of-the-art classifiers. Performance is obtained first, by observing the machines' gesture recognition percentages. Then, performance is computed by the human recognition from gestures performed by robots. Based on these two scenarios, a composite new metric of coherency is proposed relating to the amount of agreement between these two conditions. Experimental results provide an average recognition performance of 89.2% for the trained classifiers and 92.5% for the participants. Coherency in recognition was determined at 93.6%. While this new metric is not directly comparable to raw accuracy or other pure performance-based standard metrics, it provides a quantifier for validating how realistic the machine generated samples are and how accurate the resulting mimicry is.

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