WaveFoG: Wavelet Gated Transformer for Parkinson's Freezing of Gait Detection Using Wearable Accelerometer Signals

Abstract

Freezing of gait (FoG) is one of the most debilitating episodic motor symptoms of Parkinson's disease (PD). FoG detection from wrist worn inertial signals poses challenging problems such as extreme data class imbalance (only 12-18% of signal windows are FoG), low frequency postural instabilities alongside diagnostically crucial 3-8 Hz rhythmic tremors, and the need for subject independent generalisation. To address these, this paper presents WaveFoG, a novel wavelet gated Transformer architecture that combines multi scale representations from discrete wavelet transform (DWT) with a dual branch encoder. A 1D CNN captures local gait texture, and a Transformer captures global temporal context. They are fused via a sigmoid gating fusion layer that modulates the encoder output based on the DWT sub-band descriptors. Trained with focal loss and evaluated on the public Kaggle TLVMC FoG Prediction dataset under subject independent grouped ten fold cross validation (SI-CV), WaveFoG achieves a mean F1-score of 0.875 +/- 0.017 and AUPRC of 0.833 +/- 0.020, exceeding single branch baselines (1D CNN, Bi-LSTM, and vanilla Transformer) by up to 5.4 pp on F1. Temporal saliency maps highlight temporal patterns that are consistent with reported characteristics of FoG onset. The source code of this project is available at: https://github.com/Shihabul-Shuvo/WaveFoG