Abstract
The increasing interest in Wi-Fi sensing stems from its potential to capture environmental data in a low-cost, non-intrusive way, making it ideal for applications like healthcare, space occupancy analysis, and gesture-based IoT control. However, a major limitation in this field is the common problem of overfitting, where models perform well on training data but fail to generalize to new data. To overcome this, we introduce a novel hybrid architecture that integrates Inception-BiLSTM with a Support Vector Machine (SVM), which we refer to as IBIS. Our IBIS approach is uniquely engineered to improve model generalization and create more robust classification boundaries. By applying this method to Doppler-derived data, we achieve a movement recognition accuracy of nearly 99%. Comprehensive performance metrics and confusion matrices confirm the significant effectiveness of our proposed solution.
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URL
https://arxiv.org/abs/2510.24936
