scholarly journals An Intention Understanding Algorithm Based on Multimodal Information Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shaosong Dou ◽  
Zhiquan Feng ◽  
Jinglan Tian ◽  
Xue Fan ◽  
Ya Hou ◽  
...  
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
The Elderly ◽  
Bayesian Classifier ◽  
Service Robot ◽  
Object A ◽  
Multimodal Information Fusion ◽  
Multimodal Information ◽  
Intention Understanding ◽  
Scene Information

This paper proposes an intention understanding algorithm (KDI) based on an elderly service robot, which combines Neural Network with a seminaive Bayesian classifier to infer user’s intention. KDI algorithm uses CNN to analyze gesture and action information, and YOLOV3 is used for object detection to provide scene information. Then, we enter them into a seminaive Bayesian classifier and set key properties as super parent to enhance its contribution to an intent, realizing intention understanding based on prior knowledge. In addition, we introduce the actual distance between the users and objects and give each object a different purpose to implement intent understanding based on object-user distance. The two methods are combined to enhance the intention understanding. The main contributions of this paper are as follows: (1) an intention reasoning model (KDI) is proposed based on prior knowledge and distance, which combines Neural Network with seminaive Bayesian classifier. (2) A set of robot accompanying systems based on the robot is formed, which is applied in the elderly service scene.

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

scholarly journals Hobbit: Providing Fall Detection and Prevention for the Elderly in the Real World

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Markus Bajones ◽  
David Fischinger ◽  
Astrid Weiss ◽  
Daniel Wolf ◽  
Markus Vincze ◽  
...  
Keyword(s):  
Real World ◽  
Independent Living ◽  
Low Cost ◽  
Fall Detection ◽  
The Elderly ◽  
Field Trials ◽  
Lessons Learned ◽  
Human Robot Interaction ◽  
Service Robot ◽  
Emergency Situations

We present the robot developed within the Hobbit project, a socially assistive service robot aiming at the challenge of enabling prolonged independent living of elderly people in their own homes. We present the second prototype (Hobbit PT2) in terms of hardware and functionality improvements following first user studies. Our main contribution lies within the description of all components developed within the Hobbit project, leading to autonomous operation of 371 days during field trials in Austria, Greece, and Sweden. In these field trials, we studied how 18 elderly users (aged 75 years and older) lived with the autonomously interacting service robot over multiple weeks. To the best of our knowledge, this is the first time a multifunctional, low-cost service robot equipped with a manipulator was studied and evaluated for several weeks under real-world conditions. We show that Hobbit’s adaptive approach towards the user increasingly eased the interaction between the users and Hobbit. We provide lessons learned regarding the need for adaptive behavior coordination, support during emergency situations, and clear communication of robotic actions and their consequences for fellow researchers who are developing an autonomous, low-cost service robot designed to interact with their users in domestic contexts. Our trials show the necessity to move out into actual user homes, as only there can we encounter issues such as misinterpretation of actions during unscripted human-robot interaction.

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