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S. Chatterji is a prominent researcher specializing in biomedical signal processing, rehabilitation robotics, and human-machine interfaces, with a particular focus on electromyography (EMG)-based control systems. Their work sits at the intersection of neuroscience, machine learning, and assistive technology, addressing critical challenges in restoring mobility and independence to individuals with neuromuscular conditions. Chatterji's most influential contribution, a 2012 review on EMG-based control of exoskeleton robots (62 citations), established a foundational framework for understanding how robotic exoskeletons can be leveraged for rehabilitation, strength augmentation, and limb substitution. Building on this, their 2013 review specifically targeting stroke rehabilitation (21 citations) highlighted the transformative potential of surface EMG in clinical settings. More recently, Chatterji has advanced the field through data-driven approaches, developing novel feature extraction techniques for sEMG signal classification using machine learning (40 citations) and ensemble algorithms combined with PCA and DWT for robotic control (12 citations). Collectively, their body of work has garnered nearly 140 citations, reflecting meaningful influence on the rehabilitation engineering community. Their research bridges theoretical signal analysis with practical robotic applications, offering valuable insights for engineers and clinicians working to develop next-generation assistive technologies.