MSSDA: Multi-Sub-Source Adaptation for Diabetic Foot Neuropathy Recognition

TL;DR

This paper introduces MSSDA, a novel domain adaptation method for diabetic foot neuropathy recognition using a new plantar pressure dataset, improving cross-domain accuracy and addressing data discrepancies.

Contribution

The paper presents a new plantar pressure dataset for DFN and proposes MSSDA, a multi-sub-source domain adaptation technique that enhances recognition accuracy across diverse data sources.

Findings

MSSDA improves domain adaptation performance on DFN datasets.

The new dataset provides continuous plantar pressure data for DFN research.

Results show effective reduction of domain discrepancies with MSSDA.

Abstract

Diabetic foot neuropathy (DFN) is a critical factor leading to diabetic foot ulcers, which is one of the most common and severe complications of diabetes mellitus (DM) and is associated with high risks of amputation and mortality. Despite its significance, existing datasets do not directly derive from plantar data and lack continuous, long-term foot-specific information. To advance DFN research, we have collected a novel dataset comprising continuous plantar pressure data to recognize diabetic foot neuropathy. This dataset includes data from 94 DM patients with DFN and 41 DM patients without DFN. Moreover, traditional methods divide datasets by individuals, potentially leading to significant domain discrepancies in some feature spaces due to the absence of mid-domain data. In this paper, we propose an effective domain adaptation method to address this proplem. We split the dataset based on convolutional feature statistics and select appropriate sub-source domains to enhance efficiency and avoid negative transfer. We then align the distributions of each source and target domain pair in specific feature spaces to minimize the domain gap. Comprehensive results validate the effectiveness of our method on both the newly proposed dataset for DFN recognition and an existing dataset.