# Wearable-Based Real-time Freezing of Gait Detection in Parkinson's Disease Using Self-Supervised Learning

arXiv: https://arxiv.org/abs/2410.20715 · HTML: https://mlsanity.com/paper/2410.20715/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Builds on (most cited first, 1 of 1)

- [Improvement of Performance in Freezing of Gait detection in Parkinsons Disease using Transformer networks and a single waist worn triaxial accelerometer](https://mlsanity.com/paper/2404.03704/) (2024)

## Built on by (most cited first, 6 of 6)

- [Efficient Feature Extraction and Classification Architecture for MRI-Based Brain Tumor Detection and Localization](https://mlsanity.com/paper/2410.22619/) (2024)
- [Generalist vs Specialist Time Series Foundation Models: Investigating Potential Emergent Behaviors in Assessing Human Health Using PPG Signals](https://mlsanity.com/paper/2510.14254/) (2025)
- [Learning Clinical Representations Under Systematic Distribution Shift](https://mlsanity.com/paper/2603.07348/) (2026)
- [Discriminative Representation Learning for Clinical Prediction](https://mlsanity.com/paper/2603.20921/) (2026)
- [Learning Longitudinal Health Representations from EHR and Wearable Data](https://mlsanity.com/paper/2601.12227/) (2026)
- [Uncertainty-Aware Foundation Models for Clinical Data](https://mlsanity.com/paper/2604.04175/) (2026)

## Abstract (arXiv)

LIFT-PD is an innovative self-supervised learning framework developed for real-time detection of Freezing of Gait (FoG) in Parkinson's Disease (PD) patients, using a single triaxial accelerometer. It minimizes the reliance on large labeled datasets by applying a Differential Hopping Windowing Technique (DHWT) to address imbalanced data during training. Additionally, an Opportunistic Inference Module is used to reduce energy consumption by activating the model only during active movement periods. Extensive testing on publicly available datasets showed that LIFT-PD improved precision by 7.25% and accuracy by 4.4% compared to supervised models, while using 40% fewer labeled samples and reducing inference time by 67%. These findings make LIFT-PD a highly practical and energy-efficient solution for continuous, in-home monitoring of PD patients.