iPad-based Neuromotor Assessment & Training
Innovative iPad-based neuromotor assessment system for upper-limb motor skill learning and assessment. Our experimental protocol integrates iPad games, EEG devices, VICON 3D motion capture, and IMU sensors for comprehensive analysis. Gamified mHealth ecosystem to conduct remote rehabilitation studies.
Featured Research Projects
Motor skill learning in young children born preterm
Investigating mechanisms of motor skill learning in preterm children using game-based tasks and multimodal sensing.
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Game-based Mobile-Health Quantification of Upper-Limb Motor Performance in Children with Hemiparetic Cerebral Palsy
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mHealth ecosystem for assessing motor skills learning and decoding neuromotor control strategies
End-to-end platform spanning sensing, analytics, and adaptive training with micro-adaptation using AI. We developed SMT-Learner, a transformer-based deep learning model for movement trajectory learning to decode motor control strategies
Motor Imagery Driven Mobile BCI for Motor Training and Neurorehabilitation
We explore a mobile brain-computer interface (mBCI) integrating MI, AO, and ME with an adaptively controlled iPad game engine. We introduce a coherence-based perceptron to build Asynchronous Deep Coherence Neural Networks (AsyncDCNN) for neuromorphic computing. The design implements a low-latency closed-loop between a wireless EEG headset and the game engine. Pilot evaluation is planned at Marquette University’s NeuroRecovery Clinic.
Data Privacy (HIPAA-Compliant)
We follow HIPAA-compliant practices to protect participant data across the full research lifecycle.
- De-identification and role-based access controls
- Encrypted storage and secure transport (HTTPS/TLS)
- IRB-approved protocols and minimal PHI collection
Contact
Dr. Samuel Nemanich, PhD, MSCI
Interested to get involved?
We welcome research participants, students, clinicians, and collaborators.