In the modern era, a significant percentage of people around the world suffer from knee pain-related problems. 'Knee pain' can be alleviated by performing knee rehabilitation exercises in the correct posture on a regular basis. In our research, an attention mechanism-based CNN-TLSTM (Convolution Neural Network-tanh Long Sort-Term Memory) network has been proposed for assessing the knee pain level of a person. Here, electroencephalogram (EEG) signals of the frontal, parietal, and temporal lobes, electromyography (EMG) signals of the hamstring and quadriceps muscles, and knee bending angle have been used for knee pain detection. First, the CNN network has been utilized for automated feature extraction from the EEG, knee bending angle, and EMG data, and subsequently, the TLSTM network has been used as a classifier. The trained CNN-TLSTM model can classify the knee pain level of a person into five categories, namely no pain, low pain, medium pain, moderate pain, and high pain, with an overall accuracy of 95.88 %. In the hardware part, a prototype of an automated robotic knee rehabilitation system has been designed to help a person perform three rehabilitation exercises, i.e., sitting knee bending, straight leg rise, and active knee bending, according to his/her pain level, without the presence of any physiotherapist. The novelty of our research lies in (i) designing a novel deep learning-based classifier model for broadly classifying knee pain into five categories, (ii) introducing attention mechanism into the TLSTM network to boost its classification performance, and (iii) developing a user-friendly rehabilitation device for knee rehabilitation.

Prithwijit Mukherjee

Anisha Halder Roy

Biomedical Signal Processing and Control

生物医学信号处理和控制旨在提供一个跨学科的国际论坛，用于交换临床医学和生物科学中信号和图像的测量和分析研究的信息。重点是在临床诊断、患者监测和管理中使用方法和设备的实用、应用主导研究方面的贡献。生物医学信号处理和控制反映了这些方法在临床上使用和开发的主要领域工程和临床科学的接口。该期刊的范围被定义为包括相关的评论论文、技术说明、简短的通信和信件。教程论文和特刊也将出版。

The journal "Biomedical Signal Processing and Control" aims to provide an interdisciplinary international forum for the exchange of information on the measurement and analysis of signals and images in clinical medicine and biosciences. It focuses on practical and application-driven research contributions regarding methods and devices used in clinical diagnosis, patient monitoring, and management. "Biomedical Signal Processing and Control" reflects the main areas where these methods are applied and developed, particularly at the interface of engineering and clinical sciences. The scope of the journal includes relevant review papers, technical notes, brief communications, and letters, as well as tutorial papers and special issues.

《Biomedical Signal Processing and Control》期刊旨在提供一个跨学科的国际论坛，促进临床医学和生物科学领域中信号和图像测量与分析研究的信息交流。期刊重点关注在临床诊断、患者监测和管理中应用的方法和设备的实用性和应用导向的研究贡献。《Biomedical Signal Processing and Control》反映了这些方法在临床应用和开发中的主要领域，尤其是工程与临床科学的交汇点。期刊的范围包括相关的评论论文、技术说明、简短通信和信件，同时也会出版教程论文和特刊。

Category	Quartile
医学	2
医学, 工程生物医学	3

A deep learning-based comprehensive robotic system for lower limb rehabilitation

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Journal Info

Category	Quartile
ENGINEERING, BIOMEDICAL	1