A novel time series analysis approach for prediction of dialysis in critically ill patients using echo-state networks

Background: Echo-state networks (ESN) are part of a group of reservoir computing methods and are basically a form of recurrent artificial neural networks (ANN). These methods can perform classification tasks on time series data. The recurrent ANN of an echo-state network has an 'echo-state' characteristic. This 'echo-state' functions as a fading memory: samples that have been introduced into the network in a further past, are faded away. The echostate approach for the training of recurrent neural networks was first described by Jaeger H. et al. In clinical medicine, until this moment, no original research articles have been published to examine the use of echo-state networks. Methods: This study examines the possibility of using an echo-state network for prediction of dialysis in the ICU. Therefore, diuresis values and creatinine levels of the first three days after ICU admission were collected from 830 patients admitted to the intensive care unit (ICU) between May 31th 2003 and November 17th 2007. The outcome parameter was the performance by the echo-state network in predicting the need for dialysis between day 5 and day 10 of ICU admission. Patients with an ICU length of stay < 10 days or patients that received dialysis in the first five days of ICU admission were excluded. Performance by the echo-state network was then compared by means of the area under the receiver operating characteristic curve (AUC) with results obtained by two other time series analysis methods by means of a support vector machine (SVM) and a naive Bayes algorithm (NB). Results: The AUC's in the three developed echo-state networks were 0.822, 0.818, and 0.817. These results were comparable to the results obtained by the SVM and the NB algorithm. Conclusions: This proof of concept study is the first to evaluate the performance of echo-state networks in an ICU environment. This echo-state network predicted the need for dialysis in ICU patients. The AUC's of the echo-state networks were good and comparable to the performance of other classification algorithms. Moreover, the echostate network was more easily configured than other time series modeling technologies

Echo State Networks: analysis, training and predictive control

The goal of this paper is to investigate the theoretical properties, the training algorithm, and the predictive control applications of Echo State Networks (ESNs), a particular kind of Recurrent Neural Networks. First, a condition guaranteeing incremetal global asymptotic stability is devised. Then, a modified training algorithm allowing for dimensionality reduction of ESNs is presented. Eventually, a model predictive controller is designed to solve the tracking problem, relying on ESNs as the model of the system. Numerical results concerning the predictive control of a nonlinear process for pH neutralization confirm the effectiveness of the proposed algorithms for the identification, dimensionality reduction, and the control design for ESNs.Comment: 6 pages,5 figures, submitted to European Control Conference (ECC

Online Language Learning to Perform and Describe Actions for Human-Robot Interaction

International audienceThe goal of this research is to provide a real-time and adaptive spoken langue interface between humans and a humanoid robot. The system should be able to learn new grammatical constructions in real-time, and then use them immediately following or in a later interactive session. In order to achieve this we use a recurrent neural network of 500 neurons-echo state network with leaky neurons [1]. The model processes sentences as grammatical constructions, in which the semantic words (nouns and verbs) are extracted and stored in working memory, and the grammatical words (prepositions, auxiliary verbs, etc.) are inputs to the network. The trained network outputs code the role (predicate, agent, object/location) that each semantic word takes. In the final output, the stored semantic words are then mapped onto their respective roles. The model thus learns the mappings between the grammatical structure of sentences and their meanings. The humanoid robot is an iCub [2] who interacts around a instrumented tactile table (ReacTable TM) on which objects can be manipulated by both human and robot. A sensory system has been developed to extract spatial relations. A speech recognition and text to speech off-the-shelf tool allows spoken communication. In parallel, the robot has a small set of actions (put(object, location), grasp(object), point(object)). These spatial relations, and action definitions form the meanings that are to be linked to sentences in the learned grammatical constructions. The target behavior of the system is to learn two conditions. In action performing (AP), the system should learn to generate the proper robot command, given a spoken input sentence. In scene description (SD), the system should learn to describe scenes given the extracted spatial relation. Training corpus for the neural model can be generated by the interaction with the user teaching the robot by describing spatial relations or actions, creating pairs. It could also be edited by hand to avoid speech recognition errors. These interactions between the different components of the system are shown in the Figure 1. The neural model processes grammatical constructions where semantic words (e.g. put, grasp, toy, left, right) are replaced by a common marker. This is done with only a predefined set of grammatical words (after, and, before, it, on, the, then, to, you). Therefore the model is able to deal with sentences that have the same constructions than previously seen sentences. In the AP condition, we demonstrate that the model can learn and generalize to complex sentences including "Before you put the toy on the left point the drums."; the robot will first point the drums and then put the toy on the left: showing here that the network is able to establish the proper chronological order of actions. Likewise, in the SD condition, the system can be exposed to a new scene and produce a description such as "To the left of the drums and to the right of the toy is the trumpet." In future research we can exploit this learning system in the context of human language development. In addition, the neural model could enable errors recovery from speech to text recognition. Index Terms: human-robot interaction, echo state network, online learning, iCub, language learning. References [1] H. Jaeger, "The "echo state" approach to analysing and training recurrent neural networks", Tech. Rep. GMD model has been developed with Oger toolbox: http://reservoir-computing.org/organic/engine. Figure 1: Communication between the speech recognition tool (that also controls the robotic platform) and the neural model

HUMAN-ROBOT INTERACTION: LANGUAGE ACQUISITION WITH NEURAL NETWORK

ABSTRACT The paper gives an overview about the process between two language processing methods towards Human-robot interaction. In this paper, Echo State Networks and Stochastic-learning grammar are explored in order to get an idea about generating human’s natural language and the possibilities of integrating these methods to make the communication process between robot to robot or robot to human to be more natural in dialogic syntactic language game. The methods integration could give several benefits such as improving the communicative efficiency and producing the more natural communication sentence.   ABSTRAK Tulisan ini memberikan penjabaran mengenai dua metode pemrosesan bahasa alami pada interaksi Manusia dan Robot. Echo State Networks adalah salah satu arsitektur dari Jaringan Syaraf Tiruan yang berdasarkan prinsip Supervised Learning untuk Recurrent Neural Network, dieksplorasi bersama Stochastic-learning Grammar yaitu salah satu framework tata bahasa dengan konsep probabilistik yang bertujuan untuk mendapatkan ide bagaimana proses bahasa alami dari manusia dan kemungkinannya mengintegrasikan dua metode tersebut untuk membuat proses komunikasi antara robot dengan robot atau robot dengan manusia menjadi lebih natural dalam dialogic syntactic language game. Metode integrasi dapat memberikan beberapa keuntungan seperti meningkatkan komunikasi yang efisien dan dapat membuat konstruksi kalimat saat komunikasi menjadi lebih natural. How To Cite : Fazrie, A.R. (2018). HUMAN-ROBOT INTERACTION: LANGUAGE ACQUISITION WITH NEURAL NETWORK. Jurnal Teknik Informatika, 11(1), 75-84.  doi 10.15408/jti.v11i1.6093 Permalink/DOI: http://dx.doi.org/10.15408/jti.v11i1.6093