Jennifer Sloboda
Papers
2
Total Citations
41
H-Index
2
About
Jennifer Sloboda is a leading researcher in biomechatronics and neural control of movement, with a primary focus on estimating human joint dynamics for clinical and robotic applications. Her work bridges the gap between laboratory-based biomechanical analysis and real-world wearable technology. Sloboda’s major contributions center on developing non-invasive methods to predict ankle torques during locomotion using surface electromyography (EMG) and accelerometry. Her most-cited paper, “A Neural Network Estimation of Ankle Torques From Electromyography and Accelerometry” (2021, 31 citations), introduced a machine learning framework that enables accurate, real-time torque estimation without expensive motion-capture systems. This breakthrough has direct implications for patient rehabilitation, therapy planning, and the design of anticipatory control systems for wearable robotic devices like powered prostheses and exoskeletons. Her earlier work (2020, 10 citations) laid the groundwork by demonstrating that portable sensors could replace traditional lab setups, making clinical torque assessments more accessible. Sloboda’s research is notable for its practical impact: by enabling continuous, out-of-lab monitoring of joint mechanics, she is helping to democratize biomechanical assessment and advance the next generation of assistive technologies.
Research Focus
Key Achievements
Top Papers
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