# Self-Supervised Learning and Opportunistic Inference for Continuous Monitoring of Freezing of Gait in Parkinson's Disease

arXiv: https://arxiv.org/abs/2410.21326 · HTML: https://mlsanity.com/paper/2410.21326/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 45 references that are in the map.
- First posted on arXiv 27 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Mobile Sensor Data Anonymization](https://mlsanity.com/paper/1810.11546/) (2018)
- [Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition](https://mlsanity.com/paper/2109.02054/) (2021)
- [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, 5 of 5)

- [Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting](https://mlsanity.com/paper/2411.10703/) (2024)
- [Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors](https://mlsanity.com/paper/2411.11764/) (2024)
- [Efficient Feature Extraction and Classification Architecture for MRI-Based Brain Tumor Detection and Localization](https://mlsanity.com/paper/2410.22619/) (2024)
- [Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals](https://mlsanity.com/paper/2504.06987/) (2025)
- [Label-Efficient Bilateral Attention for Parkinson's Disease Screening from Wrist-Worn IMU Signals](https://mlsanity.com/paper/2604.18372/) (2026)

## Abstract (arXiv)

Parkinson's disease (PD) is a progressive neurological disorder that impacts the quality of life significantly, making in-home monitoring of motor symptoms such as Freezing of Gait (FoG) critical. However, existing symptom monitoring technologies are power-hungry, rely on extensive amounts of labeled data, and operate in controlled settings. These shortcomings limit real-world deployment of the technology. This work presents LIFT-PD, a computationally-efficient self-supervised learning framework for real-time FoG detection. Our method combines self-supervised pre-training on unlabeled data with a novel differential hopping windowing technique to learn from limited labeled instances. An opportunistic model activation module further minimizes power consumption by selectively activating the deep learning module only during active periods. Extensive experimental results show that LIFT-PD achieves a 7.25% increase in precision and 4.4% improvement in accuracy compared to supervised models while using as low as 40% of the labeled training data used for supervised learning. Additionally, the model activation module reduces inference time by up to 67% compared to continuous inference. LIFT-PD paves the way for practical, energy-efficient, and unobtrusive in-home monitoring of PD patients with minimal labeling requirements.