Papers
7
Total Citations
141
H-Index
5
About
Lini Mathew is a prominent researcher specializing in biomedical signal processing, rehabilitation robotics, and human-machine interfaces, with a particular focus on surface electromyography (sEMG) and assistive technologies. Her work has made significant contributions to the field of EMG-based pattern recognition, exploring how machine learning techniques — including Support Vector Machines and wavelet transform methods — can decode human muscle signals to enable intuitive control of rehabilitation devices and robotic systems. Among her most influential contributions, Mathew's 2018 paper on novel sEMG feature extraction using machine learning approaches has garnered 40 citations, while her systematic review on robo-assisted lower limb rehabilitation for spastic patients has accumulated 34 citations, underscoring her dual expertise in both computational and clinical dimensions of rehabilitation engineering. Her earlier work on Discrete Wavelet Packet Transform-based elbow movement classification (30 citations) helped establish robust methodologies for precise motor intention detection. More recently, Mathew has expanded her research into multimodal biosignal interfaces, combining brain signals, eye signals, and RFID technology to develop sophisticated assistive devices. This evolution in her work reflects a broader commitment to improving quality of life for individuals with physical disabilities through innovative, technology-driven solutions.
Research Focus
Key Achievements
Top Papers
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- 6Robotic arm controlling using automated balancing platform4 citations · 2015
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