Artificial Neural Network Based Step-Length Prediction Using Ultrasonic Sensors from Simulation to Implementation in Shoe-Type Measurement Device

2017 ◽  
Vol 21 (2) ◽  
pp. 321-329 ◽  
Author(s):  
Romy Budhi Widodo ◽  
◽  
Chikamune Wada ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Physical Therapist ◽  
Predictive Performance ◽  
Step Length ◽  
Ultrasonic Sensors ◽  
Measurement Device ◽  
Gait Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Step-length measurement as a spatial gait parameter is useful for the physician and physical therapist for determining the patient’s gait condition. We hypothesized that this could be determined using ultrasonic sensors mounted on a shoe-type measurement device. For that purpose, we have developed a shoe-type measurement device to measure gait parameters. Our system was found to effectively measure step-length and pressure distribution. However, we found that the presence of shoes leads to perishable and fragile conditions for the sensors. Therefore, we redesigned the number, angle, and range of the ultrasonic sensors mounted on the shoes in order to clarify and improve the step-length prediction. This paper discusses the improvement of a shoe-type measurement device from the implementation with real shoes and the step-length prediction using an artificial neural network (ANN). The results of the experiment show that the number, angle, and positioning of ultrasonic sensors affect their ability to capture the human step region, that is, 50×70 cm under the experimental condition of foot progression angle up to 30 degrees. The results of the predictive performance of step-length using the proposed ANN architecture demonstrate an improvement.

Role of Artificial Neural Network for Prediction of Gait Parameters and Patterns

2022 ◽  
pp. 427-439
Author(s):  
Kamalpreet Sandhu ◽  
Vikram Kumar Kamboj
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Human Locomotion ◽  
Neural System ◽  
Learning Ability ◽  
Ann Model ◽  
Gait Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Walking is very important exercise. Walking is characterized by gait. Gait defines the bipedal and forward propulsion of center of gravity of the human body. This chapter describes the role of artificial neural network (ANN) for prediction of gait parameters and patterns for human locomotion. The artificial neural network is a mathematical model. It is computational system inspired by the structure, processing method, and learning ability of a biological brain. According to bio-mechanics perspective, the neural system is utilized to check the non-direct connections between datasets. Also, ANN model in gait application is more desired than bio-mechanics strategies or statistical methods. It produces models of gait patterns, predicts horizontal ground reactions forces (GRF), vertical GRF, recognizes examples of stand, and predicts incline speed and distance of walking.

Role of Artificial Neural Network for Prediction of Gait Parameters and Patterns

2020 ◽  
pp. 124-135
Author(s):  
Kamalpreet Sandhu ◽  
Vikram Kumar Kamboj
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Human Locomotion ◽  
Neural System ◽  
Learning Ability ◽  
Ann Model ◽  
Gait Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Walking is very important exercise. Walking is characterized by gait. Gait defines the bipedal and forward propulsion of center of gravity of the human body. This chapter describes the role of artificial neural network (ANN) for prediction of gait parameters and patterns for human locomotion. The artificial neural network is a mathematical model. It is computational system inspired by the structure, processing method, and learning ability of a biological brain. According to bio-mechanics perspective, the neural system is utilized to check the non-direct connections between datasets. Also, ANN model in gait application is more desired than bio-mechanics strategies or statistical methods. It produces models of gait patterns, predicts horizontal ground reactions forces (GRF), vertical GRF, recognizes examples of stand, and predicts incline speed and distance of walking.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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