InceptoFormer: A Multi-Signal Neural Framework for Parkinson's Disease Severity Evaluation from Gait

• URL: http://arxiv.org/abs/2508.04540v1
• Date: Wed, 06 Aug 2025 15:27:11 GMT
• Title: InceptoFormer: A Multi-Signal Neural Framework for Parkinson's Disease Severity Evaluation from Gait
• Authors: Safwen Naimi, Arij Said, Wassim Bouachir, Guillaume-Alexandre Bilodeau,
• Abstract summary: InceptoFormer is a multi-signal neural framework designed for Parkinson's Disease (PD) severity evaluation via gait dynamics analysis.<n>Our architecture introduces a 1D adaptation of the Inception model, which we refer to as Inception1D, along with a Transformer-based framework to stage PD severity according to the Hoehn and Yahr (H&Y) scale.<n>InceptoFormer achieves an accuracy of 96.6%, outperforming existing state-of-the-art methods in PD severity assessment.
• Score: 6.155129200870887
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present InceptoFormer, a multi-signal neural framework designed for Parkinson's Disease (PD) severity evaluation via gait dynamics analysis. Our architecture introduces a 1D adaptation of the Inception model, which we refer to as Inception1D, along with a Transformer-based framework to stage PD severity according to the Hoehn and Yahr (H&Y) scale. The Inception1D component captures multi-scale temporal features by employing parallel 1D convolutional filters with varying kernel sizes, thereby extracting features across multiple temporal scales. The transformer component efficiently models long-range dependencies within gait sequences, providing a comprehensive understanding of both local and global patterns. To address the issue of class imbalance in PD severity staging, we propose a data structuring and preprocessing strategy based on oversampling to enhance the representation of underrepresented severity levels. The overall design enables to capture fine-grained temporal variations and global dynamics in gait signal, significantly improving classification performance for PD severity evaluation. Through extensive experimentation, InceptoFormer achieves an accuracy of 96.6%, outperforming existing state-of-the-art methods in PD severity assessment. The source code for our implementation is publicly available at https://github.com/SafwenNaimi/InceptoFormer

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