# Improvement of Performance in Freezing of Gait detection in Parkinsons Disease using Transformer networks and a single waist worn triaxial accelerometer

arXiv: https://arxiv.org/abs/2404.03704 · HTML: https://mlsanity.com/paper/2404.03704/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 70 references that are in the map.
- First posted on arXiv 4 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Escaping the Big Data Paradigm with Compact Transformers](https://mlsanity.com/paper/2104.05704/) (2021)

## Built on by (most cited first, 4 of 4)

- [Wearable-Based Real-time Freezing of Gait Detection in Parkinson's Disease Using Self-Supervised Learning](https://mlsanity.com/paper/2410.20715/) (2024)
- [Self-Supervised Learning and Opportunistic Inference for Continuous Monitoring of Freezing of Gait in Parkinson's Disease](https://mlsanity.com/paper/2410.21326/) (2024)
- [StrengthSense: A Dataset of IMU Signals Capturing Everyday Strength-Demanding Activities](https://mlsanity.com/paper/2511.02027/) (2025)
- [Towards Context-Aware Clinical Motion Understanding in Daily Living at Home: Freezing of Gait Detection with Egocentric Vision](https://mlsanity.com/paper/2608.13283/) (2026)

## Abstract (arXiv)

Freezing of gait (FOG) is one of the most incapacitating symptoms in Parkinsons disease, affecting more than 50 percent of patients in advanced stages of the disease. The presence of FOG may lead to falls and a loss of independence with a consequent reduction in the quality of life. Wearable technology and artificial intelligence have been used for automatic FOG detection to optimize monitoring. However, differences between laboratory and daily-life conditions present challenges for the implementation of reliable detection systems. Consequently, improvement of FOG detection methods remains important to provide accurate monitoring mechanisms intended for free-living and real-time use. This paper presents advances in automatic FOG detection using a single body-worn triaxial accelerometer and a novel classification algorithm based on Transformers and convolutional networks. This study was performed with data from 21 patients who manifested FOG episodes while performing activities of daily living in a home setting. Results indicate that the proposed FOG-Transformer can bring a significant improvement in FOG detection using leave-one-subject-out cross-validation (LOSO CV). These results bring opportunities for the implementation of accurate monitoring systems for use in ambulatory or home settings.