Stella Ansah

Ph.D. candidate in Electrical & Computer Engineering specializing in wearable biosensor data analysis, digital biomarker development, and AI driven signal processing. Proven track record of building and publishing novel deep learning and algorithms for human motion recognition and physiological data (IMU, EDA, piezoresistive pressure sensors) processing. Experienced in sensor data pipelines, multimodal data integration, and translating clinical questions into validated digital endpoints.

Graduate Research Assistant – Wearable and Control Systems Lab at University of New Hampshire (2021-08 – Present) • Developed and validated digital biomarkers for gait, fall risk, and human motion using physiological sensor data streams including IMUs and piezoresistive pressure sensors.

• Built deep learning and classical ML pipelines (LSTM, CNN, SVM) in TensorFlow/Python achieving >95% classification accuracy in motion-state recognition across large, multi-session datasets.
• Designed novel algorithms for foot trajectory estimation, stride length computation, and auto-calibration under sensor drift, enabling accurate long term monitoring during activities of daily living.
• Developed explainable AI (XAI) models for fall-risk prediction from gait features derived from wearable sensor data, supporting clinical decision-making.
• Built a stress level detection system fusing heart rate, EDA, and skin temperature data(multimodal physiological biomarker) for personalized health applications.
• Engineered sensor data processing pipelines covering data collection, annotation, feature extraction, and model training and validation.
• Designed and 3D printed custom IMU enclosures, improving body segment tracking precision by >20% in human motion analysis studies.
• Published peer-reviewed papers in IEEE conferences and journals, as well as reviewed manuscript submissions for IEEE and Springer Nature journals and conferences.

Graduate Teaching

Assistant – Multi-Sensor Data Fusion at University of New Hampshire, ECE Dept. (2021-08 – Present) • Prepared and delivered course materials on Kalman filters, SVMs, fuzzy inference, and neural networks for sensor fusion, directly aligned with digital biomarker methodology.

• Mentored junior graduate students on research design and wearable sensor data analysis projects.

Technical

Collaborator at NSF I-Corps Hub Mid-Atlantic Region (2025-07 – 2025-12) • Applied hypothesis driven validation frameworks and conducted 30+ stakeholder interviews to assess market viability of a novel agritech solution.

Ph.D. in Electrical & Computer Engineering – University of New Hampshire (2021-08 – 2026-09)

B.S. in Electrical & Electronics Engineering – Kwame Nkrumah University of Science and Technology (2015-09 – 2019-06)