From 263f7f67df5fa9be40bc877bdd070788a12f8a5e Mon Sep 17 00:00:00 2001 From: Julius Welzel Date: Tue, 8 Oct 2024 13:35:52 +0200 Subject: [PATCH] [RMV] paper directory for JOSS --- paper/example_data.png | Bin 173182 -> 0 bytes paper/fig_gs_example.png | Bin 35379 -> 0 bytes paper/make_figures.py | 76 ------- paper/paper.html | 429 --------------------------------------- paper/paper.md | 81 -------- paper/references.bib | 318 ----------------------------- stepup_amsterdam_meeting | 1 + 7 files changed, 1 insertion(+), 904 deletions(-) delete mode 100644 paper/example_data.png delete mode 100644 paper/fig_gs_example.png delete mode 100644 paper/make_figures.py delete mode 100644 paper/paper.html delete mode 100644 paper/paper.md delete mode 100644 paper/references.bib create mode 160000 stepup_amsterdam_meeting diff --git a/paper/example_data.png b/paper/example_data.png deleted file mode 100644 index 373ef1f9c6a91f7bda035e55896818823cec5e10..0000000000000000000000000000000000000000 GIT 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KielMAT: NeuroGeriatric Motion Toolbox - An Open-Source Python Toolbox for Analyzing Neurological Motion Data from Various Recording Modalities

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Authors
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Julius Welzel

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Clint Hansen

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Walter Maetzler

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Robbin Romijnders

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Published
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February 8, 2024

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Summary

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The NeuroGeriatric Motion Toolbox (KielMAT) is an open-source Python-based toolbox designed for processing human motion data. KielMAT provides a comprehensive suite of algorithms for motion data processing in neuroscience and biomechanics, including gait sequence detection, initial contact detection, pyhisical activity monitoring, etc. These algorithms assist in assessing human motion data and identifying abnormalities, facilitating diagnosis and treatment planning for individuals with mobility impairments. The modular design of KielMAT allows for flexibility and scalability, making it suitable for a wide range of applications in human motion analysis research.

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Statement of need

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The analysis of human movement is critical for neurological assessment (Micó-Amigo et al. 2023). Human motion characterization is crucial for overall well-being, encompassing our physical, mental, and social dimensions. The increasing prevalence of mobility-limiting diseases, such as Parkinson’s disease (PD), poses a serious burden on healthcare systems (Mahlknecht et al. 2013). Wearable devices such as inertial measurement units (IMUs) allow for long-term monitoring of disease progression and could, therefore, be used to track changes in gait (Mazzà et al. 2021). While many studies have focused on a single IMU worn on the lower back, they often used not freely available software to extract data (Micó-Amigo et al. 2023). The development of easy-to-use and open-source code implementations is imperative for large-scale data extraction in research and clinical settings. KielMAT addresses this gap by providing diverse algorithms for human motion data analysis, catering to motion researchers and clinicians and promoting the utilization of open-source software. KielMAT encompasses a broad range of validated algorithms such as gait sequence detection, initial contact detection, and physical activity monitoring. While not exhaustive, ongoing efforts aim to include additional validated algorithms, such as sit-to-stand, stand-to-sit, etc.

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Provided Functionality

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KielMAT offers practical examples demonstrating the application of currently implemented algorithms, focusing on gait sequence detection and initial contact detection. The toolbox utilizes IMU sensor data from clinical cohorts, such as those with congestive heart failure (CHF) (Micó-Amigo et al. 2023). Participants undergo real-world assessments, engaging in daily activities and specific tasks, including outdoor walking, navigating slopes and stairs, and moving between rooms.

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The data were processed using our gait sequence detection module, based on the Paraschiv-Ionescu algorithmParaschiv-Ionescu, Soltani, and Aminian (2020), to identify gait sequences within the time series. Subsequently, our initial contact detection module , also based on the Paraschiv-Ionescu algorithm Paraschiv-Ionescu, Soltani, and Aminian (2020), was applied to identify initial contacts within the detected gait sequences. Utilizing lower back acceleration data from the Mobilise-D dataset, we demonstrate the accurate identification of gait events such as gait onset, gait duration, and initial contacts. KielMAT offers practical examples demonstrating the application of currently implemented algorithms, focusing on gait sequence detection and initial contact detection. This example illustrates the practical application of our toolbox in analyzing human gait patterns which is shown in figure 1.

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Figure 1: Acceleration Data and Detected Gait Events using KielMAT Modules

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In figure 1, we present a detailed analysis of acceleration data from the lower back, highlighting the key gait events detected by our modules. The green vertical line indicates the onset of a gait sequence, while the shaded gray region represents the duration of the gait sequence. Additionally, blue dashed lines denote the detected initial contacts within the gait sequence.

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Installation and usage

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The KielMAT package is implemented in Python and is freely available under a Non-Profit Open Software License version 3.0. The stable version of the package can be installed from PyPI.org using pip install kielmat. Our documentation provides detailed instructions on installation and some tutorial notebooks.

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Acknowledgements

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- - -

References

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-Mahlknecht, Philipp, Stefan Kiechl, Bastiaan R Bloem, Johann Willeit, Christoph Scherfler, Arno Gasperi, Gregorio Rungger, Werner Poewe, and Klaus Seppi. 2013. “Prevalence and Burden of Gait Disorders in Elderly Men and Women Aged 60–97 Years: A Population-Based Study.” PloS One 8 (7): e69627. -
-
-Mazzà, Claudia, Lisa Alcock, Kamiar Aminian, Clemens Becker, Stefano Bertuletti, Tecla Bonci, Philip Brown, et al. 2021. “Technical Validation of Real-World Monitoring of Gait: A Multicentric Observational Study.” BMJ Open 11 (12): e050785. -
-
-Micó-Amigo, M. Encarna, Tecla Bonci, Anisoara Paraschiv-Ionescu, Martin Ullrich, Cameron Kirk, Abolfazl Soltani, Arne Küderle, et al. 2023. “Assessing Real-World Gait with Digital Technology? Validation, Insights and Recommendations from the Mobilise-D Consortium.” Journal of NeuroEngineering and Rehabilitation 20 (1): 78. https://doi.org/10.1186/s12984-023-01198-5. -
-
-Paraschiv-Ionescu, Anisoara, Christopher J Newman, Lena Carcreff, Corinna N Gerber, Stephane Armand, and Kamiar Aminian. 2019. “Locomotion and Cadence Detection Using a Single Trunk-Fixed Accelerometer: Validity for Children with Cerebral Palsy in Daily Life-Like Conditions.” Journal of Neuroengineering and Rehabilitation 16 (1): 1–11. -
-
-Paraschiv-Ionescu, Anisoara, Abolfazl Soltani, and Kamiar Aminian. 2020. “Real-World Speed Estimation Using Single Trunk IMU: Methodological Challenges for Impaired Gait Patterns.” In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 4596–99. IEEE. -
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Footnotes

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- - - - \ No newline at end of file diff --git a/paper/paper.md b/paper/paper.md deleted file mode 100644 index 4e073e7a..00000000 --- a/paper/paper.md +++ /dev/null @@ -1,81 +0,0 @@ ---- -title: "KielMAT: Kiel Motion Analysis Toolbox - An Open-Source Python Toolbox for Analyzing Neurological Motion Data from Various Recording Modalities" -tags: - - Python - - Motion capture - - Neurology - - Accelerometer - - Gyroscope -authors: - - name: Masoud Abedinifar^[co-first author] - orcid: 0000-0002-4050-9835 - affiliation: 1 - - name: Julius Welzel^[co-first author] - orcid: 0000-0001-8958-0934 - affiliation: 1 - - name: Clint Hansen - orcid: 0000-0003-4813-3868 - affiliation: 1 - - name: Walter Maetzler - orcid: 0000-0002-5945-4694 - affiliation: 1 - - name: Robbin Romijnders^[corresponding author] - orcid: 0000-0002-2507-0924 - affiliation: 1 -affiliations: - - name: Neurogeriatrics, Department of Neurology, University Hospital Schleswig-Holstein (USKH), Kiel Germany - index: 1 -date: 08 Feb 2024 -bibliography: references.bib -output: - quarto::pdf_document: - latex_engine: xelatex - keep_tex: true ---- - - - -
- -# Summary -The Kiel Motion Analysis Toolbox (KielMAT) is an open-source Python-based toolbox designed for processing human motion data, following open-science practices. KielMAT offers a range of algorithms for the processing of motion data in neuroscience and biomechanics and currently includes implementations for gait sequence detection, initial contact detection, physical activity monitoring, sit to stand and stand to sit detection algorithms. These algorithms aid in identifying patterns in human motion data on different time scales. The KielMAT is versatile in accepting motion data from various recording modalities, including IMUs that provide acceleration data from specific body locations such as the pelvis or wrist. This flexibility allows researchers to analyze data captured using different hardware setups, ensuring broad applicability across studies. Some of the toolbox algorithms have been developed and validated in clinical cohorts, allowing extracted patters to be used in a clinical context. The modular design of KielMAT allows the toolbox to be easily extended to incorporate relevant algorithms which will be developed in the research community. The toolbox is designed to be user-friendly and is accompanied by a comprehensive documentation and practical examples, while the underlying data structures build on the Motion BIDS specification [@jeung:2024]. The KielMAT toolbox is intended to be used by researchers and clinicians to analyze human motion data from various recording modalities and to promote the utilization of open-source software in the field of human motion analysis. - -# Statement of need -Physical mobility is an essential aspect of health, as impairment in mobility is associated with reduced quality of life, falls, hospitalization, mortality, and other adverse events in many chronic conditions. Traditional mobility measures include patient-reported outcomes, objective clinical assessments, and subjective clinical assessments. These measures are linked to the perception and capacity aspects of health, which often fail to show relevant effects on daily function at an individual level [@maetzler:2021]. Perception involves surveys and patient-reported outcomes that capture how individuals feel about their own functional abilities, while capacity refers to clinical assessments of an individual's ability to perform various tasks. To complement both patient-reported (perception) and clinical (capacity) assessment approaches, digital health technology (DHT) introduces a new paradigm for assessing daily function. By using wearable devices, DHT provides objective insights into an individual's functional performance, directly linking it to the International Classification of Functioning, Disability and Health (ICF) framework [@ICF:2001; @ustun:2003] for assessing how people perform in everyday life activities. [@warmerdam:2020; @fasano:2020; @maetzler:2021; @hansen:2018; @buckley:2019; @celik:2021]. DHT allows an objective impression of how patients function in everyday life and their ability to routinely perform everyday activities [@hansen:2018; @buckley:2019; @celik:2021]. Nonetheless, due to several persisting challenges in this field, current tools and techniques are still in their infancy [@micoamigo:2023]. Many studies often used proprietary software to clinically relevant features of mobility. The development of easy-to-use and open-source software is imperative for transparent features extraction in research and clinical settings. KielMAT addresses this gap by providing software for human mobility analysis, to be used by motion researchers and clinicians, while promoting open-source practices. The conceptual framework builds on Findable, Accessible, Interoperable and Reusable (FAIR) data principles to encourage the use of open source software as well as facilitate data sharing and reproducibility in the field of human motion analysis [@wilkinson:2016]. The KielMAT comprises several modules which are implemented and validated with different dataset and each serving distinct purposes in human motion analysis: - -1. Gait Sequence Detection (GSD): Identifies walking bouts to analyze gait patterns and abnormalities, crucial for neurological and biomechanical assessments. - -2. Initial Contact Detection (ICD): Pinpoints the moment of initial foot contact during walking, aiding in understanding gait dynamics and stability. - -3. Physical Activity Monitoring (PAM): Determines the intensity level of physical activities based on accelerometer signals. - -These modules are pivotal because they enable researchers and clinicians to extract meaningful insights from motion data captured in various environments and conditions. These modules are designed to process data from wearable devices, which offer distinct advantages over vision-based approaches. wearable devices such as IMUs provide continuous monitoring capabilities, enabling users to wear them throughout the day in diverse settings without logistical constraints posed by camera-based systems. - -# State of the field -With the growing availability of digital health data, open-source implementations of relevant algorithms are increasingly becoming available. From the Mobilise-D consortium, the recommended algorithms for assessing real-world gait were released, but these algorithms were developed in MATLAB, that is not free to use [@mobilised:2019; @mobilised:2023]. Likewise, an algorithm for the estimation of gait quality was released, but it is also only available in MATLAB [@gaitqualitycomposite:2016; @MATLAB:2022]. Alternatively, open-source, Python packages are available, for example to detect gait and extract gait features from a low back-worn inertial measurement unit (IMU) [@czech:2019], or from two feet-worn IMUs [@kuederle:2024]. These advancements facilitate broader accessibility and usability across research and clinical applications. Additionally, innovative approaches like Mobile GaitLab focus on video input for predicting key gait parameters such as walking speed, cadence, knee flexion angle at maximum extension, and the Gait Deviation Index, leveraging open-source principles and designed to be accessible to non-computer science specialists [@kidzinski:2020; @mobile-gaitlab:2020]. Moreover, tools such as Sit2Stand and Sports2D contribute to this landscape by offering user-friendly platforms for assessing physical function through automated analysis of movements like sit-to-stand transitions and joint angles from smartphone videos (Sports2D) [@Boswell:2023; @Pagnon:2023]. KielMAT builds forth on these toolboxes by providing a module software package that goes beyond the analysis of merely gait, and extends these analyses by additionally allowing for the physical activity monitoring [@van:2013] and other daily life-relevant movements, such as sit-to-stand and stand-to-sit transitions [@pham:2017] as well as turns [@pham:2018]. - -# Provided Functionality -KielMAT offers a comprehensive suite of algorithms for motion data processing in neuroscience and biomechanics. Currently, the toolbox includes implementations for gait sequence detection (GSD) and initial contact detection (ICD), whereas algorithms for postural transition analysis [@pham:2017] and turns [@pham:2018] are under current development. KielMAT is built on principles from the Brain Imaging Data Structure (BIDS) [@gorgolewski:2016] and for the motion analysis data are organized similar to the Motion-BIDS specifications [@jeung:2024]. - -## Dataclass -Supporting the data curation as specified in BIDS, data are organized in recordings, where recordings can be simultaneously collected with different tracking systems (e.g., an camera-based optical motion capture system and a set of IMUs). A tracking system is defined as a group of motion channels that share hardware properties (the recording device) and software properties (the recording duration and number of samples). Loading of a recording returns a `KielMATRecording` object, that holds both `data` and `channels`. Here, `data` are the actual time series data, where `channels` provide information (meta-data) on the time series type, component, the sampling frequency, and the units in which the time series (channel) are recorded. - -## Modules -The data can be passed to algorithms that are organized in different modules, such as GSD and ICD. For example, the accelerometer data from a lower back-worn IMU can be passed to the gait sequence detection algorithm [@paraschiv:2019;@paraschiv:2020]. Next, the data can be passed to the initial contact detection algorithm [@paraschiv:2019] to returns the timings of initial contacts within each gait sequence (Figure [1](example_data.png)). - -![](example_data.png) -
-Figure 1: A representative snippet of acceleration data from a low back-worn with the detected gait sequences (pink-shaded background) and the detected initial contacts (red triangles). -
- -# Installation and usage -The KielMAT package is implemented in Python (>=3.10) and is freely available under a Non-Profit Open Software License version 3.0. The stable version of the package can be installed from PyPI.org using `pip install kielmat`. Users and developers can also install the toolbox from source from GitHub. The documentation of the toolbox provides detailed instructions on [installation](https://neurogeriatricskiel.github.io/KielMAT/#installation), [conceptual framework](https://neurogeriatricskiel.github.io/KielMAT/#data-classes-conceptual-framework) and [tutorial notebooks](https://neurogeriatricskiel.github.io/KielMAT/examples/) for basic usage and specific algorithms. Data used in the examples have been collected in accordance with the Declaration of Helsinki. - -# How to contribute -KielMAT is a community effort, and any contribution is welcomed. The project is hosted on [https://github.com/neurogeriatricskiel/KielMAT](https://github.com/neurogeriatricskiel/KielMAT). In case you want to add new algorithms, it is suggested to fork the project and, after finalizing the changes, to [create a pull request from a fork](https://docs.github.com/de/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork). - -# Acknowledgements -The authors would like to thank every person who provided data which has been used in the development and validation of the algorithms in the KielMAT toolbox. -The authors declare no competing interests. - -# References diff --git a/paper/references.bib b/paper/references.bib deleted file mode 100644 index 4e115fb8..00000000 --- a/paper/references.bib +++ /dev/null @@ -1,318 +0,0 @@ -@book{ICF:2001, - address = {Geneva}, - title = {International classification of functioning, disability and health: {ICF}}, - isbn = {978-92-4-154542-6}, - shorttitle = {International classification of functioning, disability and health}, - publisher = {World Health Organization}, - editor = {{World Health Organization}}, - year = {2001}, -} - -@article{Boswell:2023, - author = {Boswell, Melissa A and Kidzi{\'n}ski, {\L}ukasz and Hicks, Jennifer L and Uhlrich, Scott D and Falisse, Antoine and Delp, Scott L}, - title = {Smartphone videos of the sit-to-stand test predict osteoarthritis and health outcomes in a nationwide study}, - journal = {npj Digital Medicine}, - volume = {6}, - number = {1}, - pages = {32}, - year = {2023}, - publisher={Nature Publishing Group UK London}, - doi= {10.1038/s41746-023-00775-1} -} - -@article{buckley:2019, - author = {Buckley, Christopher and Alcock, Lisa and McArdle, Ríona and Rehman, Rana Zia Ur and Del Din, Silvia and Mazzà, Claudia and Yarnall, Alison J. and Rochester, Lynn}, - title = {The {Role} of {Movement} {Analysis} in {Diagnosing} and {Monitoring} {Neurodegenerative} {Conditions}: {Insights} from {Gait} and {Postural} {Control}}, - journal = {Brain Sciences}, - volume = {9}, - number = {2}, - pages = {34}, - year = {2019}, - doi = {10.3390/brainsci9020034} -} - -@article{celik:2021, - author = {Celik, Y. and Stuart, S. and Woo, W. L. and Godfrey, A.}, - title = {Gait analysis in neurological populations: {Progression} in the use of wearables}, - journal = {Medical Engineering \& Physics}, - volume = {87}, - pages = {9--29}, - year = {2021}, - doi = {10.1016/j.medengphy.2020.11.005} -} - -@article{czech:2019, - author = {Matthew D. Czech and Shyamal Patel}, - title = {GaitPy: An Open-Source Python Package for Gait Analysis Using an Accelerometer on the Lower Back}, - journal = {Journal of Open Source Software}, - volume = {4}, - number = {43}, - pages = {1778}, - year = {2019}, - publisher = {The Open Journal}, - doi = {10.21105/joss.01778} -} - -@article{fasano:2020, - author = {Fasano, Alfonso and Mancini, Martina}, - title = {Wearable-based mobility monitoring: the long road ahead}, - journal = {The Lancet Neurology}, - volume = {19}, - number = {5}, - pages = {378--379}, - year = {2020}, - doi = {10.1016/S1474-4422(20)30033-8} -} - -@article{gorgolewski:2016, - author = {Gorgolewski, Krzysztof J. and Auer, Tibor and Calhoun, Vince D. and Craddock, R. Cameron and Das, Samir and Duff, Eugene P. and Flandin, Guillaume and Ghosh, Satrajit S. and Glatard, Tristan and Halchenko, Yaroslav O. and Handwerker, Daniel A. and Hanke, Michael and Keator, David and Li, Xiangrui and Michael, Zachary and Maumet, Camille and Nichols, B. Nolan and Nichols, Thomas E. and Pellman, John and Poline, Jean-Baptiste and Rokem, Ariel and Schaefer, Gunnar and Sochat, Vanessa and Triplett, William and Turner, Jessica A. and Varoquaux, Gaël and Poldrack, Russell A.}, - title = {The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments}, - journal = {Scientific Data}, - volume = {3}, - number = {1}, - pages = {160044}, - year = {2016}, - doi = {10.1038/sdata.2016.44} -} - -@article{hansen:2018, - author = {Hansen, Clint and Sanchez-Ferro, Alvaro and Maetzler, Walter}, - title = {How {Mobile} {Health} {Technology} and {Electronic} {Health} {Records} {Will} {Change} {Care} of {Patients} with {Parkinson}'s {Disease}}, - journal = {Journal of Parkinson's Disease}, - volume = {8}, - number = {s1}, - pages = {S41--S45}, - year = {2018}, - doi = {10.3233/JPD-181498} -} - -@article{jeung:2024, - title = {Motion-{{BIDS}}: An Extension to the Brain Imaging Data Structure to Organize Motion Data for Reproducible Research}, - shorttitle = {Motion-{{BIDS}}}, - author = {Jeung, Sein and Cockx, Helena and Appelhoff, Stefan and Berg, Timotheus and Gramann, Klaus and Grothkopp, S{\"o}ren and Warmerdam, Elke and Hansen, Clint and Oostenveld, Robert and Welzel, Julius}, - year = {2024}, - month = jul, - journal = {Scientific Data}, - volume = {11}, - number = {1}, - pages = {716}, - publisher = {Nature Publishing Group}, - issn = {2052-4463}, - doi = {10.1038/s41597-024-03559-8}, -} - -@article{kidzinski:2020, - author = {Kidzi{\'n}ski, {\L}ukasz and Yang, Bryan and Hicks, Jennifer L and Rajagopal, Apoorva and Delp, Scott L and Schwartz, Michael H}, - title = {Deep neural networks enable quantitative movement analysis using single-camera videos}, - journal = {Nature communications}, - volume = {11}, - number = {1}, - pages = {4054}, - year = {2020}, - publisher={Nature Publishing Group UK London}, - doi = {10.1038/s41467-020-17807-z} -} - -@article{kuederle:2024, - author = {Küderle, Arne and Ullrich, Martin and Roth, Nils and Ollenschläger, Malte and Ibrahim, Alzhraa A. and Moradi, Hamid and Richer, Robert and Seifer, Ann-Kristin and Zürl, Matthias and Sîmpetru, Raul C. and Herzer, Liv and Prossel, Dominik and Kluge, Felix and Eskofier, Bjoern M.}, - title={Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking}, - journal = {IEEE Open Journal of Engineering in Medicine and Biology}, - volume={5}, - number={}, - pages={163-172}, - year={2024}, - doi={10.1109/OJEMB.2024.3356791} -} - -@article{maetzler:2021, - author = {Maetzler, Walter and Rochester, Lynn and Bhidayasiri, Roongroj and Espay, Alberto J. and Sánchez-Ferro, Alvaro and van Uem, Janet M.T.}, - title = {Modernizing {Daily} {Function} {Assessment} in {Parkinson}'s {Disease} {Using} {Capacity}, {Perception}, and {Performance} {Measures}}, - journal = {Movement Disorders}, - volume = {36}, - number = {1}, - pages = {76--82}, - year = {2021}, - doi = {10.1002/mds.28377} -} - -@article{mahlknecht:2013, - author={Mahlknecht, Philipp and Kiechl, Stefan and Bloem, Bastiaan R and Willeit, Johann and Scherfler, Christoph and Gasperi, Arno and Rungger, Gregorio and Poewe, Werner and Seppi, Klaus}, - title={Prevalence and burden of gait disorders in elderly men and women aged 60--97 years: a population-based study}, - journal={PloS one}, - volume={8}, - number={7}, - pages={e69627}, - year={2013}, - publisher={Public Library of Science San Francisco, USA}, - doi = {10.1371/journal.pone.0069627} -} - -@article{mazza:2021, - author={Mazz{\`a}, Claudia and Alcock, Lisa and Aminian, Kamiar and Becker, Clemens and Bertuletti, Stefano and Bonci, Tecla and Brown, Philip and Brozgol, Marina and Buckley, Ellen and Carsin, Anne-Elie and others}, - title={Technical validation of real-world monitoring of gait: a multicentric observational study}, - journal={BMJ open}, - volume={11}, - number={12}, - pages={e050785}, - year={2021}, - publisher={British Medical Journal Publishing Group}, - doi = {10.1136/bmjopen-2021-050785} -} - -@article{micoamigo:2023, - author = {Micó-Amigo, M. Encarna and Bonci, Tecla and Paraschiv-Ionescu, Anisoara and Ullrich, Martin and Kirk, Cameron and Soltani, Abolfazl and Küderle, Arne and Gazit, Eran and Salis, Francesca and Alcock, Lisa and Aminian, Kamiar and Becker, Clemens and Bertuletti, Stefano and Brown, Philip and Buckley, Ellen and Cantu, Alma and Carsin, Anne-Elie and Caruso, Marco and Caulfield, Brian and Cereatti, Andrea and Chiari, Lorenzo and D’Ascanio, Ilaria and Eskofier, Bjoern and Fernstad, Sara and Froehlich, Marcel and Garcia-Aymerich, Judith and Hansen, Clint and Hausdorff, Jeffrey M. and Hiden, Hugo and Hume, Emily and Keogh, Alison and Kluge, Felix and Koch, Sarah and Maetzler, Walter and Megaritis, Dimitrios and Mueller, Arne and Niessen, Martijn and Palmerini, Luca and Schwickert, Lars and Scott, Kirsty and Sharrack, Basil and Sillén, Henrik and Singleton, David and Vereijken, Beatrix and Vogiatzis, Ioannis and Yarnall, Alison J. and Rochester, Lynn and Mazzà, Claudia and Del Din, Silvia and {for the Mobilise-D consortium}}, - title = {Assessing real-world gait with digital technology? {Validation}, insights and recommendations from the {Mobilise}-{D} consortium}, - journal = {Journal of NeuroEngineering and Rehabilitation}, - volume = {20}, - number = {1}, - pages = {78}, - year = {2023}, - doi = {10.1186/s12984-023-01198-5} -} - -@article{paraschiv:2019, - author={Paraschiv-Ionescu, Anisoara and Newman, Christopher J and Carcreff, Lena and Gerber, Corinna N and Armand, Stephane and Aminian, Kamiar}, - title={Locomotion and cadence detection using a single trunk-fixed accelerometer: validity for children with cerebral palsy in daily life-like conditions}, - journal={Journal of neuroengineering and rehabilitation}, - volume={16}, - number={1}, - pages={1--11}, - year={2019}, - publisher={BioMed Central}, - doi = {10.1186/s12984-019-0494-z} -} - -@inproceedings{paraschiv:2020, - author={Paraschiv-Ionescu, Anisoara and Soltani, Abolfazl and Aminian, Kamiar}, - title={Real-world speed estimation using single trunk IMU: methodological challenges for impaired gait patterns}, - booktitle={2020 42nd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)}, - pages={4596--4599}, - year={2020}, - organization={IEEE}, - doi={10.1109/EMBC44109.2020.9176281} -} - -@article{pham:2017, - author = {Pham, Minh H. and Elshehabi, Morad and Haertner, Linda and Heger, Tanja and Hobert, Markus A. and Faber, Gert S. and Salkovic, Dina and Ferreira, Joaquim J. and Berg, Daniela and Sanchez-Ferro, Álvaro and van Dieën, Jaap H. and Maetzler, Walter}, - title = {Algorithm for {Turning} {Detection} and {Analysis} {Validated} under {Home}-{Like} {Conditions} in {Patients} with {Parkinson}'s {Disease} and {Older} {Adults} using a 6 {Degree}-of-{Freedom} {Inertial} {Measurement} {Unit} at the {Lower} {Back}}, - journal = {Frontiers in Neurology}, - volume = {8}, - pages = {135}, - year = {2017}, - doi = {10.3389/fneur.2017.00135} -} - -@article{pham:2018, - author = {Pham, Minh H. and Warmerdam, Elke and Elshehabi, Morad and Schlenstedt, Christian and Bergeest, Lu-Marie and Heller, Maren and Haertner, Linda and Ferreira, Joaquim J. and Berg, Daniela and Schmidt, Gerhard and Hansen, Clint and Maetzler, Walter}, - title = {Validation of a {Lower} {Back} "{Wearable}"-{Based} {Sit}-to-{Stand} and {Stand}-to-{Sit} {Algorithm} for {Patients} {With} {Parkinson}'s {Disease} and {Older} {Adults} in a {Home}-{Like} {Environment}}, - journal = {Frontiers in Neurology}, - volume = {9}, - pages = {652}, - year = {2018}, - doi = {10.3389/fneur.2018.00652} -} - -@article{ustun:2003, - title={The International Classification of Functioning, Disability and Health: a new tool for understanding disability and health}, - author={{\"U}stun, T Bedirhan and Chatterji, Somnath and Bickenbach, Jerome and Kostanjsek, Nenad and Schneider, Margie}, - journal={Disability and rehabilitation}, - volume={25}, - number={11-12}, - pages={565--571}, - year={2003}, - publisher={Taylor \& Francis}, - doi = {10.1080/0963828031000137063} -} - -@article{van:2013, - title={Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity}, - author={Van Hees, Vincent T and Gorzelniak, Lukas and Dean Le{\'o}n, Emmanuel Carlos and Eder, Martin and Pias, Marcelo and Taherian, Salman and Ekelund, Ulf and Renstr{\"o}m, Frida and Franks, Paul W and Horsch, Alexander and others}, - journal={PloS one}, - volume={8}, - number={4}, - pages={e61691}, - year={2013}, - publisher={Public Library of Science San Francisco, USA}, - doi = {10.1371/journal.pone.0061691} -} - -@article{warmerdam:2020, - author = {Warmerdam, Elke and Hausdorff, Jeffrey M and Atrsaei, Arash and Zhou, Yuhan and Mirelman, Anat and Aminian, Kamiar and Espay, Alberto J and Hansen, Clint and Evers, Luc J W and Keller, Andreas and Lamoth, Claudine and Pilotto, Andrea and Rochester, Lynn and Schmidt, Gerhard and Bloem, Bastiaan R and Maetzler, Walter}, - title = {Long-term unsupervised mobility assessment in movement disorders}, - journal = {The Lancet Neurology}, - volume = {19}, - number = {5}, - pages = {462--470}, - year = {2020}, - doi = {10.1016/S1474-4422(19)30397-7} -} - -@article{wilkinson:2016, - author={Wilkinson, Mark D and Dumontier, Michel and Aalbersberg, IJsbrand Jan and Appleton, Gabrielle and Axton, Myles and Baak, Arie and Blomberg, Niklas and Boiten, Jan-Willem and da Silva Santos, Luiz Bonino and Bourne, Philip E and others}, - title={The FAIR Guiding Principles for scientific data management and stewardship}, - journal={Scientific data}, - volume={3}, - number={1}, - pages={1--9}, - year={2016}, - publisher={Nature Publishing Group}, - doi = {10.1038/sdata.2016.18} -} - -% ---------- - -@misc{gaitqualitycomposite:2016, - author = {van Schooten, Kimberley S. AND Pijnappels, Mirjam AND Rispens, Sietse M. AND Elders, Petra J. M. AND Lips, Paul AND Daffertshofer, Andreas AND Beek, Peter J. AND van Dieën, Jaap H.}, - title = {Estimate Gait Quality Composite}, - year = {2016}, - publisher = {GitHub}, - journal = {GitHub repository}, - url = {https://github.com/KimvanS/EstimateGaitQualityComposite} -} - -@misc{mobile-gaitlab:2020, - author = {Kidzi{\'n}ski, {\L}ukasz and Yang, Bryan and Hicks, Jennifer L and Rajagopal, Apoorva and Delp, Scott L and Schwartz, Michael H}, - title = {mobile-gaitlab}, - year = {2020}, - publisher = {GitHub}, - journal = {GitHub repository}, - url = {https://github.com/stanfordnmbl/mobile-gaitlab} -} - -@misc{mobilised:2019, - author = {}, - title = {{Mobilise-D} consortium}, - year = {2019}, - url = {https://mobilise-d.eu/}, -} - -@misc{mobilised:2023, - author = {Micó-Amigo, M. Encarna and Bonci, Tecla and Paraschiv-Ionescu, Anisoara and Ullrich, Martin and Kirk, Cameron and Soltani, Abolfazl and Küderle, Arne and Gazit, Eran and Salis, Francesca and Alcock, Lisa and Aminian, Kamiar and Becker, Clemens and Bertuletti, Stefano and Brown, Philip and Buckley, Ellen and Cantu, Alma and Carsin, Anne-Elie and Caruso, Marco and Caulfield, Brian and Cereatti, Andrea and Chiari, Lorenzo and D’Ascanio, Ilaria and Eskofier, Bjoern and Fernstad, Sara and Froehlich, Marcel and Garcia-Aymerich, Judith and Hansen, Clint and Hausdorff, Jeffrey M. and Hiden, Hugo and Hume, Emily and Keogh, Alison and Kluge, Felix and Koch, Sarah and Maetzler, Walter and Megaritis, Dimitrios and Mueller, Arne and Niessen, Martijn and Palmerini, Luca and Schwickert, Lars and Scott, Kirsty and Sharrack, Basil and Sillén, Henrik and Singleton, David and Vereijken, Beatrix and Vogiatzis, Ioannis and Yarnall, Alison J. and Rochester, Lynn and Mazzà, Claudia and Del Din, Silvia and {for the Mobilise-D consortium}}, - title = {Mobilise-D Technical Validation Study Recommended Algorithms}, - year = {2023}, - publisher = {GitHub}, - journal = {GitHub repository}, - url = {https://github.com/mobilise-d/Mobilise-D-TVS-Recommended-Algorithms} -} - -@misc{Pagnon:2023, - author = {Pagnon, D.}, - title = {Sports2D - Angles from video}, - year = {2023}, - publisher = {GitHub}, - journal = {GitHub repository}, - url = {https://github.com/davidpagnon/Sports2D}, - doi= {10.5281/zenodo.7903963} -} - -% ---------- - -@software{MATLAB:2022, - year = {2022}, - author = {{The MathWorks Inc.}}, - title = {MATLAB version: 9.13.0 (R2022b)}, - publisher = {The MathWorks Inc.}, - address = {Natick, Massachusetts, United States}, - url = {https://www.mathworks.com} -} - diff --git a/stepup_amsterdam_meeting b/stepup_amsterdam_meeting new file mode 160000 index 00000000..0ff26a6c --- /dev/null +++ b/stepup_amsterdam_meeting @@ -0,0 +1 @@ +Subproject commit 0ff26a6c0172aa3acc57bd3c6beef2821cb944a5

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None: - # Load recording - rec = mobilised.load_recording( - FILE_PATH, tracking_systems=TRACKSYS, tracked_points=TRACKED_POINTS - ) - - # Extract sensor data and sample rate - acc_data = rec.data[TRACKSYS][ - [f"{TRACKED_POINTS[TRACKSYS][0]}_ACCEL_{x}" for x in ["x", "y", "z"]] - ] - gyr_data = rec.data[TRACKSYS][ - [f"{TRACKED_POINTS[TRACKSYS][0]}_GYRO_{x}" for x in ["x", "y", "z"]] - ] - fs = ( - rec.channels[TRACKSYS][ - rec.channels[TRACKSYS]["name"] == f"{TRACKED_POINTS[TRACKSYS][0]}_ACCEL_x" - ]["sampling_frequency"] - .values[0] - .astype(float) - ) - - # Physical Activity Monitoring - pam = PhysicalActivityMonitoring() - # pam = pam.detect(data=acc_data, sampling_freq_Hz=fs) - - # Detect gait sequences - gsd = ParaschivIonescuGaitSequenceDetection( - tracking_systems=TRACKSYS, tracked_points=TRACKED_POINTS[TRACKSYS][0] - ) - gsd = gsd.detect(acc_data, sampling_freq_Hz=fs) - - # Detect initial contacts - icd = ParaschivIonescuInitialContactDetection( - tracking_systems=TRACKSYS, tracked_points=TRACKED_POINTS[TRACKSYS][0] - ) - icd = icd.detect(acc_data, gait_sequences=gsd.gait_sequences_, sampling_freq_Hz=fs) - - # Plot - iplot = True - if iplot: - fig, ax = plt.subplots(figsize=(6 * 1 / 2.54, 3.5 * 1 / 2.54)) - ax.plot(np.arange(acc_data.shape[0]) / fs, acc_data) - for _, (onset, duration) in gsd.gait_sequences_[ - ["onset", "duration"] - ].iterrows(): - ax.axvspan(onset, onset + duration, color="tab:pink", ec="none", alpha=0.1) - ax.set_xlim((1318.0, 1342.0)) - ax.set_xlabel("") - ax.set_ylabel("") - ax.set_xticks([]) - ax.set_yticks([]) - plt.tight_layout() - plt.savefig("fig_gs_example.png", dpi=300) - return - - -if __name__ == "__main__": - main() diff --git a/paper/paper.html b/paper/paper.html deleted file mode 100644 index ba6e19c8..00000000 --- a/paper/paper.html +++ /dev/null @@ -1,429 +0,0 @@ - - - - - - - - - - -KielMAT: NeuroGeriatric Motion Toolbox - An Open-Source Python Toolbox for Analyzing Neurological Motion Data from Various Recording Modalities - - - - - - - - - - - - - - - - - - - -