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Development main #98

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Jul 19, 2024
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2658099
fix spelling mistake in documentation
JuliusWelzel May 28, 2024
92e2efd
handling different versions in APDM mobility lab importer.
masoudabedinifar May 30, 2024
b2f4452
update on monitor labels (V5) based on feedback from APDM mobilitylab…
masoudabedinifar Jun 27, 2024
98ea2db
Bump tornado from 6.4 to 6.4.1
dependabot[bot] Jul 2, 2024
2b821cc
Bump scikit-learn from 1.4.2 to 1.5.0
dependabot[bot] Jul 2, 2024
adc9258
Declaration of Helsinki
masoudabedinifar Jul 17, 2024
8f60c7b
data used for each module
masoudabedinifar Jul 17, 2024
75636bf
Contributing
masoudabedinifar Jul 17, 2024
a62e731
summary revision
masoudabedinifar Jul 17, 2024
66176ff
Statement of need revision
masoudabedinifar Jul 17, 2024
d8f471f
State of the Field revision & remove unsued files
masoudabedinifar Jul 17, 2024
32d62a3
name changed to KMAT
masoudabedinifar Jul 17, 2024
2a11cda
name changed to Kiel Motion Analysis Toolbox (KMAT)
masoudabedinifar Jul 17, 2024
b151fe5
name changed to KMAT
masoudabedinifar Jul 17, 2024
90604e8
name changed to kamt
masoudabedinifar Jul 17, 2024
ca7785d
name chanegd to kmat
masoudabedinifar Jul 17, 2024
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Revert "name chanegd to kmat"
masoudabedinifar Jul 17, 2024
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Revert "name changed to kamt"
masoudabedinifar Jul 17, 2024
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Revert "name changed to KMAT"
masoudabedinifar Jul 17, 2024
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Revert "name changed to Kiel Motion Analysis Toolbox (KMAT)"
masoudabedinifar Jul 17, 2024
97a79fd
Revert "name changed to KMAT"
masoudabedinifar Jul 17, 2024
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merge conflict solved.
masoudabedinifar Jul 17, 2024
183a234
spelling correction
masoudabedinifar Jul 17, 2024
0fd4bc5
renaming to Kiel Motion Analysis Toolbox
masoudabedinifar Jul 17, 2024
a01523a
renamed to kielmat
masoudabedinifar Jul 17, 2024
4e793bb
renamed to KielMotionAnalysisToolbox
masoudabedinifar Jul 17, 2024
5ace5f4
Merge pull request #95 from neurogeriatricskiel/sensepark-data-importer
JuliusWelzel Jul 17, 2024
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Merge pull request #93 from neurogeriatricskiel/dependabot/pip/scikit…
JuliusWelzel Jul 17, 2024
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Merge pull request #92 from neurogeriatricskiel/dependabot/pip/tornad…
JuliusWelzel Jul 17, 2024
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update gitignore for example datasets
JuliusWelzel Jul 18, 2024
a980386
Merge branch 'fetch-keepControl-data' into development-main
JuliusWelzel Jul 18, 2024
12ca069
[FIX] run examples with online avaliable datasets
JuliusWelzel Jul 18, 2024
4b6d054
[ADD] new logo
JuliusWelzel Jul 19, 2024
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[ADD] new logo to docs
JuliusWelzel Jul 19, 2024
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Merge branch 'development-main' of https://github.com/neurogeriatrics…
JuliusWelzel Jul 19, 2024
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Merge pull request #97 from neurogeriatricskiel/rename_toolbox
JuliusWelzel Jul 19, 2024
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summary revision
masoudabedinifar committed Jul 17, 2024
commit a62e73108f44192dade141ce92d335053f51e768
2 changes: 1 addition & 1 deletion paper/paper.md
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# Summary
The NeuroGeriatrics Motion Toolbox (NGMT) is an open-source Python-based toolbox designed for processing human motion data, following open-science practices. NGMT 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. 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 NGMT 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:2023]. The NGMT 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.
The NeuroGeriatrics Motion Toolbox (NGMT) is an open-source Python-based toolbox designed for processing human motion data, following open-science practices. NGMT 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 NGMT 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 NGMT 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:2023]. The NGMT 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, since impairment of 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 associated with the perception and capacity aspects of health that frequently fail to show any relevant effect on daily function at an individual level [@maetzler:2021]. To complement both patient-reported (perception) and clinical (capacity) assessment approaches, digital health technology (DHT), including body-worn or wearable devices, offers a new dimension of measuring daily function, that is, performance [@warmerdam:2020; @fasano:2020; @maetzler: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. The NeuroGeriatrics Motion Toolbox (NGMT) 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 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.