ترغب بنشر مسار تعليمي؟ اضغط هنا

Making Sense of Complex Sensor Data Streams

54   0   0.0 ( 0 )
 نشر من قبل Birgitta Dresp-Langley
 تاريخ النشر 2021
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

This concept paper draws from our previous research on individual grip force data collected from biosensors placed on specific anatomical locations in the dominant and non dominant hands of operators performing a robot assisted precision grip task for minimally invasive endoscopic surgery. The specificity of the robotic system on the one hand, and that of the 2D image guided task performed in a real world 3D space on the other, constrain the individual hand and finger movements during task performance in a unique way. Our previous work showed task specific characteristics of operator expertise in terms of specific grip force profiles, which we were able to detect in thousands of highly variable individual data. This concept paper is focused on two complementary data analysis strategies that allow achieving such a goal. In contrast with other sensor data analysis strategies aimed at minimizing variance in the data, it is in this case here necessary to decipher the meaning of the full extent of intra and inter individual variance in the sensor data by using the appropriate statistical analyses, as shown in the first part of this paper. Then, it is explained how the computation of individual spatio temporal grip force profiles permits detecting expertise specific differences between individual users. It is concluded that these two analytic strategies are complementary. They enable drawing meaning from thousands of biosensor data reflecting human grip performance and its evolution with training, while fully taking into account their considerable inter and intra individual variability.

قيم البحث

اقرأ أيضاً

We present a simple yet effective approach for learning word sense embeddings. In contrast to existing techniques, which either directly learn sense representations from corpora or rely on sense inventories from lexical resources, our approach can in duce a sense inventory from existing word embeddings via clustering of ego-networks of related words. An integrated WSD mechanism enables labeling of words in context with learned sense vectors, which gives rise to downstream applications. Experiments show that the performance of our method is comparable to state-of-the-art unsupervised WSD systems.
Reinforcement learning (RL) combines a control problem with statistical estimation: The system dynamics are not known to the agent, but can be learned through experience. A recent line of research casts `RL as inference and suggests a particular fram ework to generalize the RL problem as probabilistic inference. Our paper surfaces a key shortcoming in that approach, and clarifies the sense in which RL can be coherently cast as an inference problem. In particular, an RL agent must consider the effects of its actions upon future rewards and observations: The exploration-exploitation tradeoff. In all but the most simple settings, the resulting inference is computationally intractable so that practical RL algorithms must resort to approximation. We demonstrate that the popular `RL as inference approximation can perform poorly in even very basic problems. However, we show that with a small modification the framework does yield algorithms that can provably perform well, and we show that the resulting algorithm is equivalent to the recently proposed K-learning, which we further connect with Thompson sampling.
We present ConFusion, an open-source package for online sensor fusion for robotic applications. ConFusion is a modular framework for fusing measurements from many heterogeneous sensors within a moving horizon estimator. ConFusion offers greater flexi bility in sensor fusion problem design than filtering-based systems and the ability to scale the online estimate quality with the available computing power. We demonstrate its performance in comparison to an iterated extended Kalman filter in visual-inertial tracking, and show its versatility through whole-body sensor fusion on a mobile manipulator.
Despite the extensive body of research that supports scientific inquiry and argumentation as cornerstones of physics learning, these strategies continue to be virtually absent in most classrooms, especially those that involve students who are learnin g English as a second language. This study presents results from an investigation of 3rd grade students discourse about how length and tension affect the sound produced by a string. These students came from a variety of language backgrounds, and all were learning English as a second language. Our results demonstrate varying levels, and uses, of experiential, imaginative, and mechanistic reasoning strategies. Using specific examples from students discourse, we will demonstrate some of the productive aspects of working within multiple language frameworks for making sense of physics. Conjectures will be made about how to utilize physics as a context for English Language Learners to further conceptual understanding, while developing their competence in the English language.
In order to plan a safe maneuver, self-driving vehicles need to understand the intent of other traffic participants. We define intent as a combination of discrete high-level behaviors as well as continuous trajectories describing future motion. In th is paper, we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. Our multi-task model achieves better accuracy than the respective separate modules while saving computation, which is critical to reducing reaction time in self-driving applications.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا