Neha Gaud

About

Dr. Neha Gaud is a leading researcher in biomechanics and human motion analysis, with a core focus on gait recognition, wearable sensor technology, and deep learning applications for clinical diagnostics. Her work bridges the gap between engineering and healthcare, developing intelligent systems that interpret human movement for early disease detection. Dr. Gaud’s most impactful contribution is her pioneering use of Inertial Measurement Unit (IMU) sensors to identify joint patterns across different walking styles, a study that has garnered 182 citations. She further advanced the field by introducing the Extreme Learning Machine (ELM) algorithm for clinical gait classification, enabling early detection of neurological disorders—a work cited 93 times. Her recent innovations include a hybrid deep learning model for human activity recognition (HAR) using wearable sensors, optimized for edge computing, which promises real-time, low-latency health monitoring. Additionally, she developed a novel LSTM-based model and universal polynomial equation to quantify all sub-phases of human gait, achieving 42 citations. Dr. Gaud’s research is instrumental in transforming how clinicians diagnose and monitor mobility impairments, making her a vital figure in the intersection of AI, wearable tech, and rehabilitative medicine.

Research Focus

Key Achievements

Top Papers

1. 1
   Pattern identification of different human joints for different human walking styles using inertial measurement unit (IMU) sensor

   182 citations · 2021
2. 2
   Clinical Human Gait Classification: Extreme Learning Machine Approach

   93 citations · 2019
3. 3
   Development of the LSTM Model and Universal Polynomial Equation for All the Sub-Phases of Human Gait

   42 citations · 2023
4. 4
   Hybrid Deep Learning-Based Human Activity Recognition (HAR) Using Wearable Sensors: An Edge Computing Approach

   19 citations · 2024

Key Collaborators

Contact & Links