2 citations · 4 across the 5 of their papers we have counts for
5 papers
Load-Aware Calibration of EMG-Driven Musculoskeletal Models for Accurate and Generalizable Joint Torque Estimation
Rajnish Kumar, Suriya Prakash Muthukrishnan, Lalan Kumar +1
Accurate EMG-driven musculoskeletal (MSK) modeling is critical for biomechanics, rehabilitation, and assistive technology. However, most models calibrate parameters under a single…
Deep Muscle EMG construction using A Physics-Integrated Deep Learning approach
Rajnish Kumar, Tapas Tripura, Souvik Chakraborty +1
Electromyography (EMG)--based computational musculoskeletal modeling is a non-invasive method for studying musculotendon function, human movement, and neuromuscular control, provid…
EEG-based 90-Degree Turn Intention Detection for Brain-Computer Interface
Pradyot Anand, Anant Jain, Suriya Prakash Muthukrishnan +3
Electroencephalography (EEG)--based turn intention prediction for lower limb movement is important to build an efficient brain-computer interface (BCI) system. This study investiga…
sEMG-Driven Physics-Informed Gated Recurrent Networks for Modeling Upper Limb Multi-Joint Movement Dynamics
Rajnish Kumar, Anand Gupta, Suriya Prakash Muthukrishnan +2
Exoskeletons and rehabilitation systems have the potential to improve human strength and recovery by using adaptive human-machine interfaces. Achieving precise and responsive contr…
Predicting Multi-Joint Kinematics of the Upper Limb from EMG Signals Across Varied Loads with a Physics-Informed Neural Network
Rajnish Kumar, Suriya Prakash Muthukrishnan, Lalan Kumar +1
In this research, we present an innovative method known as a physics-informed neural network (PINN) model to predict multi-joint kinematics using electromyography (EMG) signals rec…