Adaptive-Gated Spiking Neural Networks with Memristive Crossbars for Real-Time Athlete Injury Prediction
Keywords:
Athlete Injury Prediction, Spiking Neural Networks, Adaptive Gating, Multimodal Sensor Fusion, Neuromorphic ComputingAbstract
We propose a Spiking Neural Network (SNN) with adaptive gating for real-time athlete damage prediction,addressing the inefficiencies of conventional deep learning in processing multimodal sensor data. Traditional methods often suffer from high computational overhead due to redundant modality processing,whereas our approach dynamically gates irrelevant inputs early in the pipeline,significantly reducing energy consumption without compromising accuracy. The core innovation lies in a spike-based gating mechanism that evaluates contextual relevance of each modality,selectively suppressing low-importance signals through learnable coefficients. Furthermore,early multimodal fusion is achieved via dendritic compartments,enabling event-driven computation at the spike level,which naturally aligns with the sparse and asynchronous nature of sensor data. The architecture is co-designed with memristive neuromorphic hardware,where synaptic weights are mapped to analog conductances,thereby minimizing energy per spike through in-memory computation. Experimental results demonstrate that the proposed system operates at under 5 mW while maintaining competitive prediction performance,making it suitable for wearable deployment. The integration with existing sensor infrastructure is seamless,as the SNN replaces traditional deep learning layers without requiring modifications to preprocessing or decision support modules. This work bridges the gap between neuromorphic computing and practical sports analytics,offering a scalable solution for real-time injury risk assessment. The combination of adaptive gating, hardware-aware optimization, and multimodal fusion establishes a new direction for energy-efficient deep learning in edge applications.
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