Early detection of leprosy, a neglected tropical disease, is crucial to preventing irreversible nerve damage and disability. Analyzing temperature vari- ations in hands using infrared (IR) cameras offers a potential low-cost alternative to existing medical equipment for early detection of leprosy. This study explores the adaptation of hand landmark detec- tion models, commonly used for hand pose track- ing, to infer the hypothenar area, a critical region for leprosy diagnosis. The research addresses the challenge of limited ground-truth data for the hy- pothenar keypoint by developing annotated datasets and evaluating machine learning models like Lasso Regression and XGBoost. These models signif- icantly outperform the existing method of linear interpolation, demonstrating the feasibility of ac- curate hypothenar keypoint prediction even with limited training data. The findings contribute to the development of accessible, automated tools for early leprosy diagnosis, particularly in resource- constrained settings.

Hand landmark detection in infrared (IR) images is essential for early leprosy diagnosis in developing countries like Nepal, helping to prevent serious complications and disability. However, current hand landmark detection models, such as Google’s detection models comprised in the MediaPipe framework, often struggle with this task due to domain mismatch. While these models are trained on RGB images, the data for this research consists of greyscale IR images. This study addresses this challenge by exploring image transformation and colorization techniques to enhance MediaPipe's hand landmark recognition accuracy on IR images. Preprocessing was chosen over retraining the existing model due to limited computational resources and the lack of labeled target domain data, which makes the retraining infeasible.

Two preprocessing pipelines were developed to address different image characteristics: images with visible hand edges but varying colors of the hand, and images where hands blend in with the background, making the edges difficult to distinguish. The transformations include turning an image into its negative, colorization, contrast enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE), and masking to remove occlusion.

To evaluate the effectiveness of these techniques, accuracy has been calculated using Percentage of Correct Keypoints (PCK) metric and were compared against two baselines: a lower bound (MediaPipe performance on unchanged IR images) and an upper bound (MediaPipe performance on similar RGB images). Preliminary findings indicate that colorization significantly improves recognition for hands with sharp color transition, while contrast enhancement boosts edge definition for hands that blend into the background. By combining these approaches, the overall accuracy of hand landmark detection improved up to 25%, depending on the threshold value, particularly for the targeted open palm-up hand position.

These results demonstrate that preprocessing techniques can effectively reduce the input domain mismatch, enhancing automated leprosy diagnosis and supporting early detection efforts in low-resource settings.

In this work, we investigate how domain adaptation techniques can improve the performance of hand landmark detection models originally trained on RGB images when deployed on infrared (IR) data. Our motivation stems from a medical use case in Nepal, where clinicians require reliable temperature estimation at hand keypoints to detect early signs of leprosy. We evaluate three methods on a small IR dataset (80 labeled images & 5000 unlabeled frames): a shallow adaptation (AdaBN), a deep alignment approach (Deep CORAL), and a test-time subspace alignment method (SSA). Our experiments show that while AdaBN and SSA yield moderate improvements, Deep CORAL achieves stronger gains through targeted training of specific model components. The combination of these methods produces superior results, yielding an 11% improvement in percentage of correct keypoints (PCK@0.05) on our custom annotated IR dataset. These findings demonstrate that combining lightweight and deep domain adaptation approaches can effectively enhance IR hand landmark detection accuracy without requiring large labeled datasets, enabling practical deployment for clinical thermal imaging in resource-limited settings.

This study investigates sensor technologies for di- agnosing leprosy in Nepal, focussing on skin tem- perature in the hands using contact and non-contact sensors. Leprosy affects the peripheral nervous system, causing thermoregulatory dysfunction de- tectable via localized skin temperature changes. A systematised comparative review compares contact thermometry, infrared (IR) thermography, and IR- RGB thermography based on measurement quality, usability, and cost. Next to the systematised re- view, an experimental method is proposed to com- bine RGB and IR imaging to enhance the spatial accuracy of automatic region of interest (ROI) de- tection using MediaPipe Hand Landmarker. The study introduces a multimodal dataset of 45 sets of annotated IR-RGB images and validates a geomet- rical image registration model, achieving 93.2% keypoint detection accuracy—significantly outper- forming IR-only sensors. Results show IR-RGB thermography as a cost-effective, flexible, and ac- curate tool for early leprosy diagnosis in resource- limited settings.

Leprosy remains a significant health challenge in developing countries, where early diagnosis is crucial to prevent severe disabilities and social stigma. Recent studies have shown that infrared imaging can be used to detect abnormalities associated with leprosy by analyzing hand temperature variations. However, existing diagnostic methods relying on manual annotation of thermal images are timeconsuming, lack standardization, and require technical expertise. This research investigates methods for implementing real-time infrared video-based temperature analysis on mobile devices by focusing on hand landmark detection models, model optimization techniques, and evaluation metrics. A comprehensive literature review identified promising models such as MediaPipe Hands, OpenPose, and YOLO variants for hand landmark detection, along with optimization methods like pruning, quantization, and Neural Architecture Search (NAS) to adapt these models for mobile deployment. Furthermore, evaluation frameworks incorporating both performance and capability-oriented metrics were examined to ensure efficient and reliable deployment on resource-constrained devices. This study provides insights into developing a fully automated, mobile-based diagnostic tool for early leprosy detection, highlighting the challenges and opportunities in adapting visual AI models for infrared analysis. Future research should focus on empirical validation of optimized models on mobile platforms.