Effect of Machine Learning Techniques for Efficient Classification of EMG Patterns in Gait Disorders

Kumar, Manoj; Gautam, Pratiksha; Semwal, Vijay Bhaskar

doi:10.37391/ijeer.100211

Cited by 5 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It often incorrectly believes that there is a linear connection between the dependent and independent variables. Why we are not using decision tree because A little alteration to the data may result in a substantial alteration to the structure of the decision tree, resulting in instability; this is not the ideal approach for location prediction [7]. Since training the model takes more time.…”

Section: ░ 1 Introductionmentioning

confidence: 99%

Machine Learning Technique for Predicting Location

Arora

Agrawal

Patel

2023

IJEER

View full text Add to dashboard Cite

In the current era of internet and mobile phone usage, the prediction of a person's location at a specific moment has become a subject of great interest among researchers. As a result, there has been a growing focus on developing more effective techniques to accurately identify the precise location of a user at a given instant in time. The quality of GPS data plays a crucial role in obtaining high-quality results. Numerous algorithms are available that leverage user movement patterns and historical data for this purpose. This research presents a location prediction model that incorporates data from multiple users. To achieve the most accurate predictions, regression techniques are utilized for user trajectory prediction, and ensemble algorithmic procedures, such as the random forest approach, the Adaboost method, and the XGBoost method, are employed. The primary goal is to improve prediction accuracy. The improvement accuracy of proposed ensemble method is around 21.2%decrease in errors, which is much greater than earlier systems that are equivalent. Compared to previous comparable systems, the proposed system demonstrates an approximately 15% increase in accuracy when utilizing the ensemble methodology.

show abstract

Section: ░ 1 Introductionmentioning

confidence: 99%

Machine Learning Technique for Predicting Location

Arora

Agrawal

Patel

2023

IJEER

View full text Add to dashboard Cite

show abstract

“…The SEMG signal plays a vital role in both engineering and medical applications. SEMG is related to the muscle activation function by critically analyzing the electrical signal which is generated by the muscular contraction and flexion [7], [8]. The muscular contraction can be divided into two types such as voluntary and involuntary.…”

mentioning

confidence: 99%

SEMG Signals Identification Using DT And LR Classifier by Wavelet-Based Features

Narayan

Kumari

Ranjan

2022

IJEER

View full text Add to dashboard Cite

In the recent era of technology, biomedical signals have been attracted lots of attention regarding the development of rehabilitation robotic technology. The surface electromyography (SEMG) signals are the fabulous signals utilized in the field of robotics. In this context, SEMG signals have been acquired by twenty-five right-hand dominated healthy human subjects to discriminate the various hand gestures. The placement of SEMG electrodes has been done according to the predefined acupressure point of required hand movements. After the SEMG signal acquisition, pre-processing and noise rejection have been performed. The de-noising and four levels of SEMG signal decomposition have been accomplished by discrete wavelet transform (DWT). In this article, the third and fourth-level detail coefficients have been utilized for time-scale feature extractions. The performance of ten time-scale features has been evaluated and compared to each other with the three-fold cross-validation technique by using a Decision Tree (DT) and Linear Regression (LR) classifier. The results demonstrated that the DT classifier classification accuracy was found superior to the LR classifier. By using the DT classifier technique 96.3% accuracy has been achieved, with all combined features as a feature vector.

show abstract