Automated Home-Based Physiotherapy

Karthik, Singarao; Kumar, Sandeep; Prasad, Kantipudi Mvv; Mysurareddy, Kuchuru; C, John Moses; Seshu, B Dillep

doi:10.1109/dasa51403.2020.9317247

Cited by 7 publications

(1 citation statement)

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“…Similar approaches also derived in the study of [8]- [12], where different ML approaches are used to classify the audio spectrum data. It is also observed that out of different approaches, NN-based learning approaches have been widely studied in audio signal attributes to deal with various synthesis and processing parameters.…”

Section: Introductionmentioning

confidence: 99%

A computationally efficient learning model to classify audio signal attributes

Prasad¹,

Kumar²

2022

IJECE

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<p>The era of machine learning has opened up groundbreaking realities and opportunities in the field of medical diagnosis. However, it is also observed that faster and proper diagnosis of any diseases/medical conditions require proper analysis and classification of digital signal data. It indicates the proper identification of tumors in the brain. Brain magnetic resonance imaging (MRI) data has to be appropriately classified, and similarly, pulse signal analysis is required to evaluate the human heart operating condition. Several studies have used machine learning (ML) modeling to classify speech signals, but very few studies have explored the classification of audio signal attributes in the context of intelligent healthcare monitoring. The study thereby aims to introduce novel mathematical modeling to analyze and classify synthetic pulse audio signal attributes with cost-effective computation. The numerical modeling is composed of several functional blocks where deep neural network-based learning (DNNL) plays a crucial role during the training phase, and also it is further combined with a recurrent structure of long-short term memory (R-LSTM) feedback connections (FCs). The design approaches further experiment in a numerical computing environment in terms of accuracy and computational aspects. The classification outcome of the proposed approach shows that it attains approximately 85% accuracy, which is comparable to the baseline approaches and execution time.</p>

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Section: Introductionmentioning

confidence: 99%