Knee Vibratography: Arthritis Diagnosis through Non-Invasive Cloud Based Artificial Intelligence

J, Hemavathi; Kumar, T. Satish; Prasad, Ramesh

doi:10.1109/csitss.2017.8447763

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…But the method doesn't work with wrist affected by sclerosis diseases. Hemavati J et al in [18] compared four different methods related to the confusion matrix with, a true positive, true negative, false positive and false negative values with real-life patients data for diagnosis of arthritis with knee joints.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Medical Diagnostic Systems for Arthritis

2019

IJRTE

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Arthritis is a chronic, an autoimmune disease which may affect in many critical health problems in patients. From the past few years, the number of patients suffering from arthritis are swiftly escalating. Until now there is no accurate treatment discovered for this rare disease. The paper discusses the different machine learning techniques in early detection of Arthritis so that, early diagnosis can help the patients to detect/ cure the disease.

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Section: Literature Reviewmentioning

confidence: 99%

Intelligent Medical Diagnostic Systems for Arthritis

2019

IJRTE

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“…Neuromusculoskeletal disorders such as cerebral palsy and osteoarthritis are other infirmities that reduce the quality of life of people [3,4]. Clinically, such diseases are diagnosed using X-Ray [5] or Magnetic Resonance Imaging (MRI) techniques [6].…”

Section: Introductionmentioning

confidence: 99%

Human knee abnormality detection from imbalanced sEMG data

Vijayvargiya

Prakash

Kumar

et al. 2021

Biomedical Signal Processing and Control

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The classification of imbalanced datasets, especially in medicine, is a major problem in data mining. Such a problem is evident in analyzing normal and abnormal subjects about knee from data collected during walking. In this work, surface electromyography (sEMG) data were collected during walking from the lower limb of 22 individuals (11 with and 11 without knee abnormality). Subjects with a knee abnormality take longer to complete the walking task than healthy subjects. Therefore, the SEMG signal length of unhealthy subjects is longer than that of healthy subjects, resulting in a problem of imbalance in the collected sEMG signal data. Thus, the development of a classification model for such datasets is challenging due to the bias towards the majority class in the data. The collected sEMG signals are challenging due to the contribution of multiple motor units at a time and their dependency on neuromuscular activity, physiological and anatomical properties of the involved muscles. Hence, automated analysis of such sEMG signals is an arduous task. A multi-step classification scheme is proposed in this research to overcome this limitation. The Wavelet Denoising (WD) scheme is used to denoise the collected sEMG signals, followed by the extraction of eleven time-domain features. The oversampling techniques are then used to balance the data under analysis

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Knee Vibratography: Arthritis Diagnosis through Non-Invasive Cloud Based Artificial Intelligence

Cited by 2 publications

References 9 publications

Intelligent Medical Diagnostic Systems for Arthritis

Intelligent Medical Diagnostic Systems for Arthritis

Human knee abnormality detection from imbalanced sEMG data

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