Caleb Sawade
Papers
1
Total Citations
4
H-Index
1
About
Caleb Sawade’s research lies at the intersection of biomechanics, wearable sensing, and machine learning, with a focus on advancing human movement analysis. His most-cited work, “Unilateral Inertial and Muscle Activity Sensor Fusion for Gait Cycle Progress Estimation” (2018), introduces a novel approach that fuses data from inertial measurement units (IMUs) and muscle activity sensors—captured from just one side of the lower body—to estimate gait cycle progress using feedforward neural networks. This contribution is significant because it reduces sensor complexity while maintaining accuracy, offering a practical pathway for real-world applications in rehabilitation, prosthetics, and sports science. With 4 citations, this paper has laid groundwork for efficient, unilateral gait monitoring. Sawade’s work exemplifies how sensor fusion and neural networks can transform raw biomechanical data into actionable insights, making him a notable figure in the growing field of wearable health technology.
Research Focus
Key Achievements
Top Papers
- 1