Unlocking the Magic of Motion

Gait imbalances are a common cause of injury in elderly people, which can lead to instability and increase the risk of falling. The recent introduction of deep learning (DL)-based gait assessment methods has affected fall prevention systems in elderly and Parkinson's Disease (PD) patients. In this scoping review, we curate and provide a summary of these studies. We discuss the effectiveness, feasibility, and necessity of DL-based methods for predicting sudden imbalances in gait, including muscle strain, Freezing of Gait (FoG), etc. We also discuss the challenges and limitations in translating DL models to both clinical and daily activities, including variations in movement patterns, sensor availability, and the need for robust generalization. We highlight the limitations of the existing studies and future trends that need to be addressed by the research community.

GaitSetPy is a modular, extensible Python toolkit designed to advance research and development in gait analysis and recognition. Addressing the growing need for reproducible, flexible, and scalable software in human movement studies, GaitSetPy provides a unified framework for loading diverse gait datasets, preprocessing time series data, extracting a wide range of gait features, performing exploratory data analysis, and applying state-of-the-art machine learning models. The architecture is built around abstract base classes and singleton managers, enabling seamless integration of new datasets, feature extractors, preprocessors, analyzers, and classifiers through a plugin-based system. GaitSetPy supports both modern class-based and legacy function-based APIs, ensuring backward compatibility while promoting best practices in software design. The toolkit includes ready-to-use workflows for popular datasets such as Daphnet, MobiFall, PhysioNet, HAR-UP, and Arduous, and offers utilities for statistical, time, and frequency domain feature extraction, as well as visualization and evaluation tools. By fostering modularity, extensibility, and ease of use, GaitSetPy aims to accelerate innovation and reproducibility in gait research. The package is open source and available at https://github.com/Alohomora-Labs/gaitSetPy