MultiGait (Multimorbidity Gait) is a Python implementation of a gait analysis pipeline for real-world assessment, primarily developed for wrist-worn inertial measurement units (IMUs). This pipeline integrates signal-processing algorithms for gait detection, initial contact detection, stride-length estimation, cadence, and walking speed providing a streamlined workflow for mobility research in people with multiple long-term conditions (multimorbidity). While the pipeline is wrist-focused, fine-tuned versions for lower-back devices are also provided for use in custom workflows.
The individual algorithms included in this library have been developed and validated in multimorbidity cohorts [1]. Validation of the full wrist-worn device pipeline is provided in [2], which presents the recommended pipeline configuration and reports the associated errors and performance metrics.
Planned future releases include:
- Novel algorithm implementations.
- Validated wear-time detection algorithms.
Note on biomechanical definitions : The biomechanical logic and gait event definitions implemented in MultiGait are based on the specifications defined within Mobilise-D.
Software developed by Dr Dimitrios Megaritis. Scientific authorship is listed in CITATION.cff.
Table of Contents
- Installation
- Usage
- Citation
- Validation reference for the underlying algorithms
- Validation reference for the complete wrist-device pipeline
- Funding and Support
- License
From PyPI (recommended)
python3 -m pip install multigaitFrom GitHub:
pip install git+https://github.com/DMegaritis/multigait.gitOr clone the repository and install locally:
git clone https://github.com/DMegaritis/multigait.git
cd multigait
pip install .The package is designed to be used in two main modes:
High-level pipelines allow loading raw IMU data and obtaining gait outcomes end-to-end. Example:
from multigait.pipeline import MultiGaitPipelineMultimorbidityImpaired
pipeline = MultiGaitPipelineMultimorbidityImpaired()
pipeline.safe_run(long_trial)Note: We offer the best performing pipeline for older adults with multimorbidity presented and validated in [2].
Additional note: While this library has been primarily developed as a complete gait pipeline for wrist-worn devices, it also includes support for lower-back algorithms. Fine-tuned versions for lower-back devices are provided and can be used in custom pipelines.
You can also use individual algorithms separately to build custom workflows. Example modules include:
- Gait Sequence Detection (GSD)
- Initial Contact Detection (ICD)
- Stride Length Estimation (SL)
- Cadence (CAD; requires prior GSD and ICD)
- Walking Speed (Ws; requires prior GSD, CAD, and SL)
For usage examples and input/output formats, see the examples in this repository or in DMegaritis/multimobility_wrist.
The following table summarizes all Digital Mobility Outcomes (DMOs) extracted by MultiGait, including walking bout types and the statistics calculated:
| Walking Bout | DMO | Statistic/Measure |
|---|---|---|
| All WBs | Number of walking bouts | Sum |
| All WBs | Total Walking Duration (min) | Sum |
| All WBs | Initial Contacts (n_raw) | Sum |
| All WBs | WB Duration (s) | Mean |
| All WBs | Max WB Duration (s) | 90th percentile |
| All WBs | WB Duration bout to bout variability (s) | CV |
| All WBs | Cadence (steps/min) | Mean |
| All WBs | Stride Duration (s) | Mean |
| All WBs | Cadence bout to bout variability (steps/min) | CV |
| All WBs | Stride Duration bout to bout variability (s) | CV |
| All WBs | Cadence within bout variability (CV) | Mean |
| All WBs | Stride Duration within bout variability (CV) | Mean |
| All WBs | Cadence within bout variability (RMSSD) | Mean |
| All WBs | Stride Duration within bout variability (RMSSD) | Mean |
| 10–30s WBs | Number of walking bouts | Sum |
| 10–30s WBs | Walking Speed (m/s) | Mean |
| 10–30s WBs | Stride Length (m) | Mean |
| 10–30s WBs | Walking Speed within bout variability (CV) (m/s) | Mean |
| 10–30s WBs | Stride Length within bout variability (CV) (m) | Mean |
| 10–30s WBs | Walking Speed within bout variability (RMSSD) (m/s) | Mean |
| 10–30s WBs | Stride Length within bout variability (RMSSD) (m) | Mean |
| >10s WBs | Number of walking bouts | Sum |
| >10s WBs | Max Walking Speed (m/s) | 90th percentile |
| >30s WBs | Number of walking bouts | Sum |
| >30s WBs | Walking Speed (m/s) | Mean |
| >30s WBs | Stride Length (m) | Mean |
| >30s WBs | Cadence (steps/min) | Mean |
| >30s WBs | Stride Duration (s) | Mean |
| >30s WBs | Max Walking Speed (m/s) | 90th percentile |
| >30s WBs | Max Cadence (steps/min) | 90th percentile |
| >30s WBs | Walking Speed bout to bout variability (m/s) | CV |
| >30s WBs | Stride Length bout to bout variability (m) | CV |
| >30s WBs | Cadence within bout variability (CV) (steps/min) | Mean |
| >30s WBs | Stride Duration within bout variability (CV) (s) | Mean |
| >30s WBs | Walking Speed within bout variability (CV) (m/s) | Mean |
| >30s WBs | Stride Length within bout variability (CV) (m) | Mean |
| >30s WBs | Cadence within bout variability (RMSSD) (steps/min) | Mean |
| >30s WBs | Stride Duration within bout variability (RMSSD) (s) | Mean |
| >30s WBs | Walking Speed within bout variability (RMSSD) (m/s) | Mean |
| >30s WBs | Stride Length within bout variability (RMSSD) (m) | Mean |
| >60s WBs | Number of walking bouts | Sum |
| All WBs | Alpha | - |
CV: Coefficient of Variation; RMSSD: Root Mean Square of Successive Differences
If you use MultiGait in your research, please cite:
@software{megaritis2025wristmobility,
author = {Megaritis, Dimitrios and Alcock, Lisa and Scott, Kirsty and Hiden, Hugo and Cereatti, Andrea and Vogiatzis, Ioannis and Del Din, Silvia},
title = {MultiGait: Real-World Gait Pipeline for Wrist-Worn Devices for Multimorbid Populations},
year = {2025},
publisher = {Zenodo},
doi = {https://doi.org/10.5281/zenodo.17903930},
url = {https://zenodo.org/records/17903930}
}[1] Megaritis D, Alcock L, Scott K, Hiden H, Cereatti A, Vogiatzis I, Del Din S. Real-World Wrist-Derived Digital Mobility Outcomes in People with Multiple Long-Term Conditions: A Comparison of Algorithms. Bioengineering (Basel). 2025 Oct 15;12(10):1108. doi: 10.3390/bioengineering12101108. PMID: 41155107; PMCID: PMC12561645.
[2] Megaritis D, Alcock L, Scott K, Hiden H, Vogiatzis I, Del Din S. Validation of an Algorithmic Pipeline for Wrist-Worn Devices to Estimate Walking Speed in People with Multiple Long-Term Conditions. Sensors (Basel). 2026 Aug 5;26(15):4946. doi: 10.3390/s26154946. PMID: 42590721; PMCID: PMC13468760.
This work was supported by the Medical Research Council (MRC) Gap Fund award (UKRI/MR/B000091/1).
The MultiGait library is licensed under the Apache License 2.0. It is free to use for any purpose, including commercial use, but all distributions must include the license text.
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