scholarly journals Development of a methodological framework for a robust prediction of the main behaviours of dairy cows using a combination of machine learning algorithms on accelerometer data

2020 ◽  
Vol 169 ◽  
pp. 105179 ◽  
Author(s):  
L. Riaboff ◽  
S. Poggi ◽  
A. Madouasse ◽  
S. Couvreur ◽  
S. Aubin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dairy Cows ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Accelerometer Data ◽  
Methodological Framework ◽  
Robust Prediction

scholarly journals Improving Accelerometry-Based Measurement of Functional Use of the Upper Extremity After Stroke: Machine Learning Versus Counts Threshold Method

2020 ◽  
Vol 34 (12) ◽  
pp. 1078-1087
Author(s):  
Peter S. Lum ◽  
Liqi Shu ◽  
Elaine M. Bochniewicz ◽  
Tan Tran ◽  
Lin-Ching Chang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Upper Extremity ◽  
Functional Activity ◽  
Learning Algorithms ◽  
Ground Truth ◽  
Machine Learning Algorithms ◽  
Average Error ◽  
Test Duration ◽  
Accelerometer Data ◽  
Paretic Limb

Background Wrist-worn accelerometry provides objective monitoring of upper-extremity functional use, such as reaching tasks, but also detects nonfunctional movements, leading to ambiguity in monitoring results. Objective Compare machine learning algorithms with standard methods (counts ratio) to improve accuracy in detecting functional activity. Methods Healthy controls and individuals with stroke performed unstructured tasks in a simulated community environment (Test duration = 26 ± 8 minutes) while accelerometry and video were synchronously recorded. Human annotators scored each frame of the video as being functional or nonfunctional activity, providing ground truth. Several machine learning algorithms were developed to separate functional from nonfunctional activity in the accelerometer data. We also calculated the counts ratio, which uses a thresholding scheme to calculate the duration of activity in the paretic limb normalized by the less-affected limb. Results The counts ratio was not significantly correlated with ground truth and had large errors ( r = 0.48; P = .16; average error = 52.7%) because of high levels of nonfunctional movement in the paretic limb. Counts did not increase with increased functional movement. The best-performing intrasubject machine learning algorithm had an accuracy of 92.6% in the paretic limb of stroke patients, and the correlation with ground truth was r = 0.99 ( P < .001; average error = 3.9%). The best intersubject model had an accuracy of 74.2% and a correlation of r =0.81 ( P = .005; average error = 5.2%) with ground truth. Conclusions In our sample, the counts ratio did not accurately reflect functional activity. Machine learning algorithms were more accurate, and future work should focus on the development of a clinical tool.

scholarly journals Detection of Unilateral Arm Paresis after Stroke by Wearable Accelerometers and Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7784
Author(s):  
Johan Wasselius ◽  
Eric Lyckegård Finn ◽  
Emma Persson ◽  
Petter Ericson ◽  
Christina Brogårdh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Accelerometer Data ◽  
Learning Models ◽  
Stroke Patients ◽  
Convolutional Network ◽  
Stroke Treatment ◽  
Data Windows

Recent advances in stroke treatment have provided effective tools to successfully treat ischemic stroke, but still a majority of patients are not treated due to late arrival to hospital. With modern stroke treatment, earlier arrival would greatly improve the overall treatment results. This prospective study was performed to asses the capability of bilateral accelerometers worn in bracelets 24/7 to detect unilateral arm paralysis, a hallmark symptom of stroke, early enough to receive treatment. Classical machine learning algorithms as well as state-of-the-art deep neural networks were evaluated on detection times between 15 min and 120 min. Motion data were collected using triaxial accelerometer bracelets worn on both arms for 24 h. Eighty-four stroke patients with unilateral arm motor impairment and 101 healthy subjects participated in the study. Accelerometer data were divided into data windows of different lengths and analyzed using multiple machine learning algorithms. The results show that all algorithms performed well in separating the two groups early enough to be clinically relevant, based on wrist-worn accelerometers. The two evaluated deep learning models, fully convolutional network and InceptionTime, performed better than the classical machine learning models with an AUC score between 0.947–0.957 on 15 min data windows and up to 0.993–0.994 on 120 min data windows. Window lengths longer than 90 min only marginally improved performance. The difference in performance between the deep learning models and the classical models was statistically significant according to a non-parametric Friedman test followed by a post-hoc Nemenyi test. Introduction of wearable stroke detection devices may dramatically increase the portion of stroke patients eligible for revascularization and shorten the time to treatment. Since the treatment effect is highly time-dependent, early stroke detection may dramatically improve stroke outcomes.

scholarly journals Supplementary material to "A new methodological framework by geophysical sensors combinations associated with machine learning algorithms to understand soil attributes"

2021 ◽  
Author(s):  
Danilo César de Mello ◽  
Gustavo Vieira Veloso ◽  
Marcos Guedes de Lana ◽  
Fellipe Alcantara de Oliveira Mello ◽  
Raul Roberto Poppiel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Methodological Framework ◽  
Soil Attributes ◽  
Supplementary Material

scholarly journals Prediction of metabolic status of dairy cows in early lactation with on-farm cow data and machine learning algorithms

2019 ◽  
Vol 102 (11) ◽  
pp. 10186-10201 ◽  
Author(s):  
Wei Xu ◽  
Ariette T.M. van Knegsel ◽  
Jacques J.M. Vervoort ◽  
Rupert M. Bruckmaier ◽  
Renny J. van Hoeij ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dairy Cows ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Metabolic Status ◽  
Early Lactation ◽  
On Farm

Data Analytics to Predict, Detect, and Monitor Chronic Autoimmune Diseases Using Machine Learning Algorithms

2021 ◽  
pp. 150-182
Author(s):  
Jayashree M. Kudari
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Huge Data ◽  
Learning Techniques ◽  
Classification And Regression ◽  
Robust Prediction

Developments in machine learning techniques for classification and regression exposed the access of detecting sophisticated patterns from various domain-penetrating data. In biomedical applications, enormous amounts of medical data are produced and collected to predict disease type and stage of the disease. Detection and prediction of diseases, such as diabetes, lung cancer, brain cancer, heart disease, and liver diseases, requires huge tests and that increases the size of patient medical data. Robust prediction of a patient's disease from the huge data set is an important agenda in in this chapter. The challenge of applying a machine learning method is to select the best algorithm within the disease prediction framework. This chapter opts for robust machine learning algorithms for various diseases by using case studies. This usually analyzes each dimension of disease, independently checking the identified value between the limits to monitor the condition of the disease.

scholarly journals Applying Machine Learning Techniques in Older People Activity Recognition usingWearable and Mobile Devices

2019 ◽  
Author(s):  
Flavio Vinicius Vieira Santana ◽  
Bruno Henrique Rasteiro ◽  
Larissa Cardoso Zimmermann ◽  
Luciana De Nardin ◽  
Maria da Graça Campos Pimentel
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Accelerometer Data ◽  
Smartphone Apps ◽  
Online Recognition ◽  
Learning Techniques ◽  
Combined Use ◽  
Future Work

We investigate the potential of the combined use of smartwatch accelerometer data and smartphone apps for online older adultsactivity recognition.We selected machine learning algorithms which resulted in a posteriori recognition accuracy of 98.92%. Our smartphone app, with the selected machine learning algorithms, carried out online recognition from data captured on the smartwatch. These results allow us, as future work, assess the accuracy of online recognition when the system is used by older adults.

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