Irandokht Khanjani

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About

Dr. Irandokht Khanjani is a researcher at the forefront of rehabilitation robotics and biomedical signal processing. Her primary research focuses on developing intelligent systems that bridge the gap between human physiology and robotic assistance, particularly for lower-limb rehabilitation. Her most cited work, "Estimate human-force from sEMG signals for a lower-limb rehabilitation robot" (2017, 11 citations), introduces a novel application of artificial neural networks (ANNs) to decode human intent. By analyzing surface electromyogram (sEMG) signals, Dr. Khanjani’s model estimates the forces a patient intends to generate, enabling a rehabilitation robot to respond intuitively and provide adaptive assistance. This contribution is critical for creating more responsive, patient-centered robotic therapy. Her research systematically explores optimal ANN parameter settings, addressing a key challenge in achieving high accuracy for real-time force estimation. Through this work, Dr. Khanjani is advancing the development of smarter, safer, and more effective assistive devices that can significantly improve the quality of life for individuals with mobility impairments.