MaD GUI: An Open-Source Python Package for Annotation and Analysis of Time-Series Data

Ollenschläger, Malte; Küderle, Arne; Mehringer, Wolfgang; Seifer, Ann-Kristin; Winkler, Jürgen; Gaßner, Heiko; Kluge, Felix; Eskofier, Bjoern M.

doi:10.3390/s22155849

Cited by 11 publications

(6 citation statements)

References 46 publications

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“…This makes it possible to build (internal) toolboxes and workflows on top of our work. For example, we successfully integrated the gaitmap algorithms into a custom plugin for the MaD -GUI [65] to allow clinicians at the University Hospital Erlangen to analyze data from routine clinical gait recordings.…”

Section: Resultsmentioning

confidence: 99%

Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking

Küderle,

Ullrich,

Roth

et al. 2024

IEEE Open J. Eng. Med. Biol.

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Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. Conclusion: The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.

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Section: Resultsmentioning

confidence: 99%

Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking

Küderle,

Ullrich,

Roth

et al. 2024

IEEE Open J. Eng. Med. Biol.

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“…First, we used the foot-worn IMU data to segment individual strides considered as regions of interest. Strides were segmented by a dynamic time-warping algorithm [ 33 ] followed by a manual quality control using the MaD GUI [ 34 ]. Turning strides were excluded from the data set.…”

Section: Methodsmentioning

confidence: 99%

EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors

Seifer

Dorschky

Küderle

et al. 2023

Sensors

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Wearable sensors are able to monitor physical health in a home environment and detect changes in gait patterns over time. To ensure long-term user engagement, wearable sensors need to be seamlessly integrated into the user’s daily life, such as hearing aids or earbuds. Therefore, we present EarGait, an open-source Python toolbox for gait analysis using inertial sensors integrated into hearing aids. This work contributes a validation for gait event detection algorithms and the estimation of temporal parameters using ear-worn sensors. We perform a comparative analysis of two algorithms based on acceleration data and propose a modified version of one of the algorithms. We conducted a study with healthy young and elderly participants to record walking data using the hearing aid’s integrated sensors and an optical motion capture system as a reference. All algorithms were able to detect gait events (initial and terminal contacts), and the improved algorithm performed best, detecting 99.8% of initial contacts and obtaining a mean stride time error of 12 ± 32 ms. The existing algorithms faced challenges in determining the laterality of gait events. To address this limitation, we propose modifications that enhance the determination of the step laterality (ipsi- or contralateral), resulting in a 50% reduction in stride time error. Moreover, the improved version is shown to be robust to different study populations and sampling frequencies but is sensitive to walking speed. This work establishes a solid foundation for a comprehensive gait analysis system integrated into hearing aids that will facilitate continuous and long-term home monitoring.

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“…Labels for stance not having an immediately following swing label were discarded, and vice versa for swing labels. Turning strides were manually excluded from the analysis in this study using a graphical user interface [ 21 ] with a custom plugin, as the HMM was developed and validated exclusively for straight strides.…”

Section: Methodsmentioning

confidence: 99%

Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning

Ollenschläger,

Höfner,

Ullrich

et al. 2023

Orphanet J Rare Dis

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Background Hereditary spastic paraplegias (HSPs) cause characteristic gait impairment leading to an increased risk of stumbling or even falling. Biomechanically, gait deficits are characterized by reduced ranges of motion in lower body joints, limiting foot clearance and ankle range of motion. To date, there is no standardized approach to continuously and objectively track the degree of dysfunction in foot elevation since established clinical rating scales require an experienced investigator and are considered to be rather subjective. Therefore, digital disease-specific biomarkers for foot elevation are needed. Methods This study investigated the performance of machine learning classifiers for the automated detection and classification of reduced foot dorsiflexion and clearance using wearable sensors. Wearable inertial sensors were used to record gait patterns of 50 patients during standardized 4 $$\times$$ × 10 m walking tests at the hospital. Three movement disorder specialists independently annotated symptom severity. The majority vote of these annotations and the wearable sensor data were used to train and evaluate machine learning classifiers in a nested cross-validation scheme. Results The results showed that automated detection of reduced range of motion and foot clearance was possible with an accuracy of 87%. This accuracy is in the range of individual annotators, reaching an average accuracy of 88% compared to the ground truth majority vote. For classifying symptom severity, the algorithm reached an accuracy of 74%. Conclusion Here, we show that the present wearable gait analysis system is able to objectively assess foot elevation patterns in HSP. Future studies will aim to improve the granularity for continuous tracking of disease severity and monitoring therapy response of HSP patients in a real-world environment.

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MaD GUI: An Open-Source Python Package for Annotation and Analysis of Time-Series Data

Cited by 11 publications

References 46 publications

Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking

Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking

EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors

Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning

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