scholarly journals Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1086
Author(s):  
Raoul Hoffmann ◽  
Hanna Brodowski ◽  
Axel Steinhage ◽  
Marcin Grzegorzek
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Capacitive Sensor ◽  
Legged Locomotion ◽  
Sensor Data ◽  
Neural Network Architecture ◽  
Gait Patterns ◽  
Before And After

Gait patterns are a result of the complex kinematics that enable human two-legged locomotion, and they can reveal a lot about a person’s state and health. Analysing them is useful for researchers to get new insights into the course of diseases, and for physicians to track the progress after healing from injuries. When a person walks and is interfered with in any way, the resulting disturbance can show up and be found in the gait patterns. This paper describes an experimental setup for capturing gait patterns with a capacitive sensor floor, which can detect the time and position of foot contacts on the floor. With this setup, a dataset was recorded where 42 participants walked over a sensor floor in different modes, inter alia, normal pace, closed eyes, and dual-task. A recurrent neural network based on Long Short-Term Memory units was trained and evaluated for the classification task of recognising the walking mode solely from the floor sensor data. Furthermore, participants were asked to do the Unilateral Heel-Rise Test, and their gait was recorded before and after doing the test. Another neural network instance was trained to predict the number of repetitions participants were able to do on the test. As the results of the classification tasks turned out to be promising, the combination of this sensor floor and the recurrent neural network architecture seems like a good system for further investigation leading to applications in health and care.

scholarly journals Artificial Recurrent Neural Network Architecture in Customer Consumption Prediction for Business Development

2020 ◽  
Vol 2 (2) ◽  
pp. 111-120
Author(s):  
Dr. Karrupusamy P.
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Association Rule ◽  
Network Architecture ◽  
Short Term Memory ◽  
Real Life ◽  
Consumption Pattern ◽  
Neural Network Architecture ◽  
Competitive Edge ◽  
Frame Work

The customer consumption pattern prediction has become one of a significant role in developing the business and taking it to a competitive edge. For forecasting the behaviors of the consumers the paper engages an artificial recurrent neural network architecture the long short-term memory an improvement of recurrent neural network. The mechanism laid out to predict the pattern of the consumption, uses the information’s about the consumption of products based on the age and the gender. The information essential are extracted and described with the prefix-span procedure based association rule. Utilizing the information about the day to day products purchase pattern as input a frame work to predict the customer daily essentials was designed, the designed frame was capable enough to learn the dissimilarities across the predicted and the original miscalculation rates. The frame work devised was tested using real life applications and the results observed demonstrated that the proposed LSTM based prediction with the prefix span association rule to acquire the day today consumption details is compatible for forecasting the customer consumption over time accurately.

scholarly journals Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network

2021 ◽  
Author(s):  
Zhan Li ◽  
Shuai Li
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Architecture ◽  
Redundancy Resolution ◽  
Neural Network Architecture ◽  
Kinematic Control ◽  
Physical Constraints ◽  
Motion Constraints ◽  
Joint Limits ◽  
First Time

AbstractRedundancy manipulators need favorable redundancy resolution to obtain suitable control actions to guarantee accurate kinematic control. Among numerous kinematic control applications, some specific tasks such as minimally invasive manipulation/surgery require the distal link of a manipulator to translate along such fixed point. Such a point is known as remote center of motion (RCM) to constrain motion planning and kinematic control of manipulators. Recurrent neural network (RNN) which possesses parallel processing ability, is a powerful alternative and has achieved success in conventional redundancy resolution and kinematic control with physical constraints of joint limits. However, up to now, there still is few related works on the RNNs for redundancy resolution and kinematic control of manipulators with RCM constraints considered yet. In this paper, for the first time, an RNN-based approach with a simplified neural network architecture is proposed to solve the redundancy resolution issue with RCM constraints, with a new and general dynamic optimization formulation containing the RCM constraints investigated. Theoretical results analyze and convergence properties of the proposed simplified RNN for redundancy resolution of manipulators with RCM constraints. Simulation results further demonstrate the efficiency of the proposed method in end-effector path tracking control under RCM constraints based on a redundant manipulator.

scholarly journals Application of deep learning methods to predict ionosphere parameters in real time

2020 ◽  
Vol 196 ◽  
pp. 02007
Author(s):  
Vladimir Mochalov ◽  
Anastasia Mochalova
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Short Term Memory ◽  
Neural Network Architecture ◽  
Short Term ◽  
Learning Methods ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.

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