Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy

Tack, Christopher

doi:10.1016/j.msksp.2018.11.012

Cited by 90 publications

(56 citation statements)

References 33 publications

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“…Supervised learning is for performing prediction for labeled class data [18]. The model is trained and analyzed for essential features and then tested with unlabeled class data [13]. The model gains knowledge in training and applies the knowledge in the testing phase with real-world data [15].…”

Section: ) Supervised Learningmentioning

confidence: 99%

“…The model gains knowledge in training and applies the knowledge in the testing phase with real-world data [15]. Based on the data on hand, it is of 2 types: Classification and Regression, where the former is for discrete binary data and later is for continuous data [13].…”

Section: ) Supervised Learningmentioning

confidence: 99%

“…Machine learning is a process of constructing a model that learns from data and makes predictions without being programmed explicitly [13]. The process of prediction using machine learning involves mostly three stages [14].…”

Section: Introduction a Motivationmentioning

confidence: 99%

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Realizing a Stacking Generalization Model to Improve the Prediction Accuracy of Major Depressive Disorder in Adults

et al. 2020

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Major depressive disorder (MDD) is a persistent psychiatric mood disorder that is prevalent from a few weeks to a few months, even for years in the worst cases. It causes sadness, hopelessness in the individuals; sometimes, it forces them to hurt themselves. In severe cases, MDD can even lead to the death of the individual. It is challenging to diagnose MDD as it co-occurs with many other disorders (Co-Morbid) and many other reasons such as mobility, lack of motivation, and cost. The way to diagnose MDD is usually high ended that is challenging for the regular clinicians to diagnose. Therefore, to make their work more comfortable, and to predict MDD at the early stages, we have developed an ensemble-based machine learning model. The data collected has been cleaned with a preprocessing technique, and feature selection are performed using wrapper based methods; moreover, in the final step, a stacking based ensemble learning model is implemented to classify the MDD patients. Furthermore, KNN Imputation is implemented for preprocessing, Random Forest-Based Backward Elimination for feature selection and multi-layer perceptron, SVM and Random Forest as low-level learners in stacking generalization model. The results show that the prediction accuracy of the stacking generalization model is superior to the individual classifiers. INDEX TERMS K-nearest neighbors, major depressive disorder, multilayer perceptron, random forest, random forest-based feature elimination, stacking generalization and support vector machine.

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Section: ) Supervised Learningmentioning

confidence: 99%

Section: ) Supervised Learningmentioning

confidence: 99%

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Realizing a Stacking Generalization Model to Improve the Prediction Accuracy of Major Depressive Disorder in Adults

et al. 2020

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“…New ants in this system tend to choose the path with greater amount of pheromone. By passing time, all ants follow the positive feedback and choose the shortest path, which is signed by greatest amount of pheromone [86]. The applications of ant colony optimization in recent research have been declared as traveling salesman problem, scheduling, structural and concrete engineering, digital image processing, electrical engineering, clustering, routing optimization algorithm [41], data mining [32], robot path planning [87], and deep learning [39].…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

Literature Review on Big Data Analytics Methods

Vanani¹,

Majidian²

2020

Social Media and Machine Learning

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Companies and industries are faced with a huge amount of raw data, which have information and knowledge in their hidden layer. Also, the format, size, variety, and velocity of generated data bring complexity for industries to apply them in an efficient and effective way. So, complexity in data analysis and interpretation incline organizations to deploy advanced tools and techniques to overcome the difficulties of managing raw data. Big data analytics is the advanced method that has the capability for managing data. It deploys machine learning techniques and deep learning methods to benefit from gathered data. In this research, the methods of both ML and DL have been discussed, and an ML/DL deployment model for IOT data has been proposed.

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“…This can be done with discrete data, known as classification, or regression, where the prediction output is continuous data, such as joint angle. 27 Supervised learning is one of the two categories of ML algorithms and involves learning a model which best maps input features to labelled outputs. There are wide-varieties of supervised learning algorithms including models such as linear regression, polynomial regression, decision trees, and random forest regression.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor

Argent

Drummond

Remus

et al. 2019

Journal of Rehabilitation and Assistive Technologies Engineerin

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Introduction Joint angle measurement is an important objective marker in rehabilitation. Inertial measurement units may provide an accurate and reliable method of joint angle assessment. The objective of this study was to assess whether a single sensor with the application of machine learning algorithms could accurately measure hip and knee joint angle, and investigate the effect of inertial measurement unit orientation algorithms and person-specific variables on accuracy. Methods Fourteen healthy participants completed eight rehabilitation exercises with kinematic data captured by a 3D motion capture system, used as the reference standard, and a wearable inertial measurement unit. Joint angle was calculated from the single inertial measurement unit using four machine learning models, and was compared to the reference standard to evaluate accuracy. Results Average root-mean-squared error for the best performing algorithms across all exercises was 4.81° (SD = 1.89). The use of an inertial measurement unit orientation algorithm as a pre-processing step improved accuracy; however, the addition of person-specific variables increased error with average RMSE 4.99° (SD = 1.83°). Conclusions Hip and knee joint angle can be measured with a good degree of accuracy from a single inertial measurement unit using machine learning. This offers the ability to monitor and record dynamic joint angle with a single sensor outside of the clinic.

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Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy

Cited by 90 publications

References 33 publications

Realizing a Stacking Generalization Model to Improve the Prediction Accuracy of Major Depressive Disorder in Adults

Realizing a Stacking Generalization Model to Improve the Prediction Accuracy of Major Depressive Disorder in Adults

Literature Review on Big Data Analytics Methods

Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor

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