scholarly journals Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7628
Author(s):  
Yeon-Wook Kim ◽  
Kyung-Lim Joa ◽  
Han-Young Jeong ◽  
Sangmin Lee
Keyword(s):  
Time Series Data ◽  
Learning Algorithm ◽  
Sampling Rate ◽  
Model Performance ◽  
Series Data ◽  
Measurement Unit ◽  
Clinical Test ◽  
Ensemble Model ◽  
Balance Assessment ◽  
Optimal Ensemble

In this study, a wearable inertial measurement unit system was introduced to assess patients via the Berg balance scale (BBS), a clinical test for balance assessment. For this purpose, an automatic scoring algorithm was developed. The principal aim of this study is to improve the performance of the machine-learning-based method by introducing a deep-learning algorithm. A one-dimensional (1D) convolutional neural network (CNN) and a gated recurrent unit (GRU) that shows good performance in multivariate time-series data were used as model components to find the optimal ensemble model. Various structures were tested, and a stacking ensemble model with a simple meta-learner after two 1D-CNN heads and one GRU head showed the best performance. Additionally, model performance was enhanced by improving the dataset via preprocessing. The data were down sampled, an appropriate sampling rate was found, and the training and evaluation times of the model were improved. Using an augmentation process, the data imbalance problem was solved, and model accuracy was improved. The maximum accuracy of 14 BBS tasks using the model was 98.4%, which is superior to the results of previous studies.

scholarly journals Coordination Pattern of the Thigh, Pelvic, and Lumbar Movements during the Gait of Patients with Hip Osteoarthritis

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Takuya Ibara ◽  
Masaya Anan ◽  
Ryosuke Karashima ◽  
Kiyotaka Hada ◽  
Koichi Shinkoda ◽  
...  
Keyword(s):  
Time Series Data ◽  
Hip Osteoarthritis ◽  
Series Data ◽  
Measurement Unit ◽  
Control Group ◽  
Coordination Pattern ◽  
Rotational Movement ◽  
Vector Coding ◽  
Lateral Tilt ◽  
Angular Velocities

There are limited reports on segment movement and their coordination pattern during gait in patients with hip osteoarthritis. To avoid the excessive stress toward the hip and relevant joints, it is important to investigate the coordination pattern between these segment movements, focusing on the time series data. This study aimed to quantify the coordination pattern of lumbar, pelvic, and thigh movements during gait in patients with hip osteoarthritis and in a control group. An inertial measurement unit was used to measure the lumbar, pelvic, and thigh angular velocities during gait of 11 patients with hip osteoarthritis and 11 controls. The vector coding technique was applied, and the coupling angle and the appearance rate of coordination pattern in each direction were calculated and compared with the control group. Compared with the control group, with respect to the lumbar/pelvic segment movements, the patients with hip osteoarthritis spent more rates in anti-phase and lower rates in in-phase lateral tilt movement. With respect to the pelvic/thigh segment movements, the patients with hip osteoarthritis spent more rates within the proximal- and in-phases for lateral tilt movement. Furthermore, patients with osteoarthritis spent lower rates in the distal-phase for anterior/posterior tilt and rotational movement. Patients with hip osteoarthritis could not move their pelvic and thigh segments separately, which indicates the stiffness of the hip joint. The rotational movement and lateral tilt movements, especially, were limited, which is known as Duchenne limp. To maintain the gait ability, it seems important to pay attention to these directional movements.

scholarly journals Risk Monitoring and Quantitative Results of Various Attributes of Machine Learning Algorithms with a Time Series Data

2019 ◽  
Vol 8 (11) ◽  
pp. 4018-4022
Keyword(s):  
Machine Learning ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Machine Learning Algorithm ◽  
Risk Modelling ◽  
Risk Monitoring ◽  
Quantitative Results

The aim of this research is to do risk modelling after analysis of twitter posts based on certain sentiment analysis. In this research we analyze posts of several users or a particular user to check whether they can be cause of concern to the society or not. Every sentiment like happy, sad, anger and other emotions are going to provide scaling of severity in the conclusion of final table on which machine learning algorithm is applied. The data which is put under the machine learning algorithms are been monitored over a period of time and it is related to a particular topic in an area

scholarly journals Loss of Control Prediction for Motorcycles during Emergency Braking Maneuvers Using a Supervised Learning Algorithm

2020 ◽  
Vol 10 (5) ◽  
pp. 1754 ◽  
Author(s):  
Pedro Huertas-Leyva ◽  
Giovanni Savino ◽  
Niccolò Baldanzini ◽  
Marco Pierini
Keyword(s):  
Supervised Learning ◽  
Predictive Models ◽  
Time Series Data ◽  
Performance Indicator ◽  
Learning Algorithm ◽  
Front Wheel ◽  
Series Data ◽  
Loss Of Control ◽  
Skill Levels ◽  
Emergency Situations

The most common evasive maneuver among motorcycle riders and one of the most complicated to perform in emergency situations is braking. Because of the inherent instability of motorcycles, motorcycle crashes are frequently caused by loss of control performing braking as an evasive maneuver. Understanding the motion conditions that lead riders to start losing control is essential for defining countermeasures capable of minimizing the risk of this type of crashes. This paper provides predictive models to classify unsafe loss of control braking maneuvers on a straight line before becoming irreversibly unstable. We performed braking maneuver experiments in the field with motorcycle riders facing a simulated emergency scenario. The latter involved a mock-up intersection in which we generated conflict events between the motorcycle ridden by the participants and an oncoming car driven by trained research staff. The data collected comprises 165 braking trials (including 11 trials identified as loss of control) with 13 riders representing four categories of braking skill, ranging from beginner to expert. Three predictive models of loss of control events during braking trials, going from a basic model to a more advanced one, were defined using logistic regressions as supervised learning methods and using the area under the receiver operating characteristic (ROC) curve as a performance indicator. The predictor variables of the models were identified among the parameters of the vehicle kinematics. The best model predicted 100% of the loss of control and 100% of the full control cases. The basic and the more advanced supervised models were adapted for loss of control identification with time series data, and the results detecting in real-time the loss of control events showed excellent performance as well as with the supervised models. The study showed that expert riders may maintain stability under dynamic conditions that normally lead less skilled riders to a loss of control or falling events. The best decision thresholds of the most relevant kinematic parameters to predict loss of control have been defined. The thresholds of parameters that typically characterize the loss of control such as the yaw rate and front-wheel lock duration were dependent on the rider skill levels. The peak-to-root-mean-square ratio of roll acceleration was the most robust parameter for identifying loss of control among all skill levels.

scholarly journals Sample Entropy of Human Gait Center of Pressure Displacement: A Systematic Methodological Analysis

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 579 ◽  
Author(s):  
Samira Ahmadi ◽  
Nariman Sepehri ◽  
Christine Wu ◽  
Tony Szturm
Keyword(s):  
Time Series Data ◽  
Inertial Sensors ◽  
Center Of Pressure ◽  
Sampling Rate ◽  
Sample Entropy ◽  
Human Gait ◽  
Series Data ◽  
Low Pass ◽  
Data Points ◽  
Parameter Values

Sample entropy (SampEn) has been used to quantify the regularity or predictability of human gait signals. There are studies on the appropriate use of this measure for inter-stride spatio-temporal gait variables. However, the sensitivity of this measure to preprocessing of the signal and to variant values of template size (m), tolerance size (r), and sampling rate has not been studied when applied to “whole” gait signals. Whole gait signals are the entire time series data obtained from force or inertial sensors. This study systematically investigates the sensitivity of SampEn of the center of pressure displacement in the mediolateral direction (ML COP-D) to variant parameter values and two pre-processing methods. These two methods are filtering the high-frequency components and resampling the signals to have the same average number of data points per stride. The discriminatory ability of SampEn is studied by comparing treadmill walk only (WO) to dual-task (DT) condition. The results suggest that SampEn maintains the directional difference between two walking conditions across variant parameter values, showing a significant increase from WO to DT condition, especially when signals are low-pass filtered. Moreover, when gait speed is different between test conditions, signals should be low-pass filtered and resampled to have the same average number of data points per stride.

scholarly journals Next generation reservoir computing

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Daniel J. Gauthier ◽  
Erik Bollt ◽  
Aaron Griffith ◽  
Wendson A. S. Barbosa
Keyword(s):  
Time Series Data ◽  
Vector Autoregression ◽  
Learning Algorithm ◽  
Training Data ◽  
Series Data ◽  
Data Sets ◽  
Reservoir Computing ◽  
Next Generation ◽  
Training Time ◽  
Nonlinear Vector

AbstractReservoir computing is a best-in-class machine learning algorithm for processing information generated by dynamical systems using observed time-series data. Importantly, it requires very small training data sets, uses linear optimization, and thus requires minimal computing resources. However, the algorithm uses randomly sampled matrices to define the underlying recurrent neural network and has a multitude of metaparameters that must be optimized. Recent results demonstrate the equivalence of reservoir computing to nonlinear vector autoregression, which requires no random matrices, fewer metaparameters, and provides interpretable results. Here, we demonstrate that nonlinear vector autoregression excels at reservoir computing benchmark tasks and requires even shorter training data sets and training time, heralding the next generation of reservoir computing.

scholarly journals PENERAPAN ARTIFICIAL NEURAL NETWORK DENGAN OPTIMASI MODIFIED ARTIFICIAL BEE COLONY UNTUK MERAMALKAN HARGA BITCOIN TERHADAP RUPIAH

2020 ◽  
Vol 9 (2) ◽  
pp. 135-142
Author(s):  
Di Mokhammad Hakim Ilmawan ◽  
Budi Warsito ◽  
Sugito Sugito
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Bee Colony ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Series Data ◽  
Future Period ◽  
Bee Colony ◽  
Artificial Neural ◽  
Digital Assets

Bitcoin is one of digital assets that can be used to make a profit. One of the ways to use Bitcoin profitly is to trade Bitcoin. At trade activities, decisions making whether to buy or not are very crucial. If we can predict the price of Bitcoin in the future period, we can make a decisions whether to buy Bitcoin or not. Artificial Neural Network can be used to predict Bitcoin price data which is time series data. There are many learning algorithm in Artificial Neural Network, Modified Artificial Bee Colony is one of optimization algorithm that used to solve the optimal weight of Artificial Neural Network. In this study, the Bitcoin exchage rate against Rupiah starting September 1, 2017 to January 4, 2019 are used. Based on the training results obtained that MAPE value is 3,12% and the testing results obtained that MAPE value is 2,02%. This represent that the prediction results from Artificial Neural Network optimized by Modified Artificial Bee Colony algorithm are quite accurate because of small MAPE value.

Improvement of Variant Adaptable LSTM Trained With Metaheuristic Algorithms for Healthcare Analysis

2021 ◽  
pp. 1031-1051
Author(s):  
Tarik A. Rashid ◽  
Mohammad K. Hassan ◽  
Mokhtar Mohammadi ◽  
Kym Fraser
Keyword(s):  
Diabetes Mellitus ◽  
Neural Networks ◽  
Time Series Data ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Data Sets ◽  
Patients With Diabetes

Recently, the population of the world has increased along with health problems. Diabetes mellitus disease as an example causes issues to the health of many patients globally. The task of this chapter is to develop a dynamic and intelligent decision support system for patients with different diseases, and it aims at examining machine-learning techniques supported by optimization techniques. Artificial neural networks have been used in healthcare for several decades. Most research works utilize multilayer layer perceptron (MLP) trained with back propagation (BP) learning algorithm to achieve diabetes mellitus classification. Nonetheless, MLP has some drawbacks, such as, convergence, which can be slow; local minima can affect the training process. It is hard to scale and cannot be used with time series data sets. To overcome these drawbacks, long short-term memory (LSTM) is suggested, which is a more advanced form of recurrent neural networks. In this chapter, adaptable LSTM trained with two optimizing algorithms instead of the back propagation learning algorithm is presented. The optimization algorithms are biogeography-based optimization (BBO) and genetic algorithm (GA). Dataset is collected locally and another benchmark dataset is used as well. Finally, the datasets fed into adaptable models; LSTM with BBO (LSTMBBO) and LSTM with GA (LSTMGA) for classification purposes. The experimental and testing results are compared and they are promising. This system helps physicians and doctors to provide proper health treatment for patients with diabetes mellitus. Details of source code and implementation of our system can be obtained in the following link “https://github.com/hamakamal/LSTM.”

Fusion Process Neural Networks Classifier Oriented Time Series

2019 ◽  
Vol 16 (10) ◽  
pp. 4059-4063
Author(s):  
Ge Li ◽  
Hu Jing ◽  
Chen Guangsheng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Feature Extraction ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Series Data ◽  
Time Varying ◽  
Synthetic Control ◽  
Classification Feature

Based on the consideration of complementary advantages, different wavelet, fractal and statistical methods are integrated to complete the classification feature extraction of time series. Combined with the advantage of process neural networks that processing time-varying information, we propose a fusion classifier with process neural network oriented time series. Be taking advantage of the multi-fractal processing nonlinear feature of time series data classification, the strong adaptability of the wavelet technique for time series data and the effect of statistical features on the classification of time series data, we can achieve the classification feature extraction of time series. Additionally, using time-varying input characteristics of process neural networks, the pattern matching of timevarying input information and space-time aggregation operation is realized. The feature extraction of time series with the above three methods is fused to the distance calculation between time-varying inputs and cluster space in process neural networks. We provide the process neural network fusion to the learning algorithm and optimize the calculation process of the time series classifier. Finally, we report the performance of our classification method using Synthetic Control Charts data from the UCI dataset and illustrate the advantage and validity of the proposed method.

scholarly journals Deep Temporal Convolution Network for Time Series Classification

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 603
Author(s):  
Bee Hock David Koh ◽  
Chin Leng Peter Lim ◽  
Hasnae Rahimi ◽  
Wai Lok Woo ◽  
Bin Gao
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Multivariate Time Series ◽  
Classification Performance ◽  
Series Data ◽  
Temporal Context ◽  
Complex Data ◽  
Layer By Layer ◽  
The Right

A neural network that matches with a complex data function is likely to boost the classification performance as it is able to learn the useful aspect of the highly varying data. In this work, the temporal context of the time series data is chosen as the useful aspect of the data that is passed through the network for learning. By exploiting the compositional locality of the time series data at each level of the network, shift-invariant features can be extracted layer by layer at different time scales. The temporal context is made available to the deeper layers of the network by a set of data processing operations based on the concatenation operation. A matching learning algorithm for the revised network is described in this paper. It uses gradient routing in the backpropagation path. The framework as proposed in this work attains better generalization without overfitting the network to the data, as the weights can be pretrained appropriately. It can be used end-to-end with multivariate time series data in their raw form, without the need for manual feature crafting or data transformation. Data experiments with electroencephalogram signals and human activity signals show that with the right amount of concatenation in the deeper layers of the proposed network, it can improve the performance in signal classification.

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