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+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6842 +10.21105/joss.06842 + +KielMAT: Kiel Motion Analysis Toolbox - An Open-Source +Python Toolbox for Analyzing Neurological Motion Data from Various +Recording Modalities + + + +https://orcid.org/0000-0002-4050-9835 + +Abedinifar +Masoud + + + + +https://orcid.org/0000-0001-8958-0934 + +Welzel +Julius + + + + +https://orcid.org/0000-0003-4813-3868 + +Hansen +Clint + + + + +https://orcid.org/0000-0002-5945-4694 + +Maetzler +Walter + + + + +https://orcid.org/0000-0002-2507-0924 + +Romijnders +Robbin + + +* + + + +Neurogeriatrics, Department of Neurology, University +Hospital Schleswig-Holstein (USKH), Kiel Germany + + + + +* E-mail: + + +8 +2 +2024 + +9 +102 +6842 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +Motion capture +Neurology +Accelerometer +Gyroscope + + + + + + + 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 + et al., 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 + et al., 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 + (Üstun + et al., 2003; + World + Health Organization, 2001) for assessing how people perform + in everyday life activities. + (Buckley + et al., 2019; + Celik + et al., 2021; + Fasano + & Mancini, 2020; + Hansen + et al., 2018; + Maetzler + et al., 2021; + Warmerdam + et al., 2020). DHT allows an objective impression of how + patients function in everyday life and their ability to routinely + perform everyday activities + (Buckley + et al., 2019; + Celik + et al., 2021; + Hansen + et al., 2018). Nonetheless, due to several persisting + challenges in this field, current tools and techniques are still in + their infancy + (Micó-Amigo + et al., 2023a). 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 + et al., 2016). The KielMAT comprises several modules which + are implemented and validated with different dataset and each + serving distinct purposes in human motion analysis:

+ + +

Gait Sequence Detection (GSD): Identifies walking bouts to + analyze gait patterns and abnormalities, crucial for + neurological and biomechanical assessments.

+
+ +

Initial Contact Detection (ICD): Pinpoints the moment of + initial foot contact during walking, aiding in understanding + gait dynamics and stability.

+
+ +

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 + (Micó-Amigo + et al., 2023b; + Mobilise-D + Consortium, 2019). Likewise, an algorithm for the + estimation of gait quality was released, but it is also only + available in MATLAB + (Schooten, + 2016; + The + MathWorks Inc., 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 + & Patel, 2019), or from two feet-worn IMUs + (Küderle + et al., 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 + (Kidziński + et al., 2020a, + 2020b). + 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 + et al., 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 + Hees et al., 2013) and other daily life-relevant movements, + such as sit-to-stand and stand-to-sit transitions + (Pham + et al., 2017) as well as turns + (Pham + et al., 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 + et al., 2017) and turns + (Pham + et al., 2018) are under current development. KielMAT is built + on principles from the Brain Imaging Data Structure (BIDS) + (Gorgolewski + et al., 2016) and for the motion analysis data are organized + similar to the Motion-BIDS specifications + (Jeung + et al., 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-Ionescu + et al., 2020, + 2019). + Next, the data can be passed to the initial contact detection + algorithm + (Paraschiv-Ionescu + et al., 2019) to returns the timings of initial contacts + within each gait sequence (Figure + 1).

+ + +

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, + conceptual + framework and + tutorial + notebooks 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. + 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.

+
+ + 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.

+
+
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Validation, insights and recommendations from the Mobilise-D consortium + Journal of NeuroEngineering and Rehabilitation + 2023 + 20 + 1 + 10.1186/s12984-023-01198-5 + 78 + + + + + + + Paraschiv-IonescuAnisoara + NewmanChristopher J + CarcreffLena + GerberCorinna N + ArmandStephane + AminianKamiar + + 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 + BioMed Central + 2019 + 16 + 1 + 10.1186/s12984-019-0494-z + 1 + 11 + + + + + + Paraschiv-IonescuAnisoara + SoltaniAbolfazl + AminianKamiar + + Real-world speed estimation using single trunk IMU: Methodological challenges for impaired gait patterns + 2020 42nd annual international conference of the IEEE engineering in medicine & biology society (EMBC) + IEEE + 2020 + 10.1109/EMBC44109.2020.9176281 + 4596 + 4599 + + + + + + PhamMinh H. + ElshehabiMorad + HaertnerLinda + HegerTanja + HobertMarkus A. + FaberGert S. + SalkovicDina + FerreiraJoaquim J. + BergDaniela + Sanchez-FerroÁlvaro + DieënJaap H. van + MaetzlerWalter + + 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 + Frontiers in Neurology + 2017 + 8 + 10.3389/fneur.2017.00135 + 135 + + + + + + + PhamMinh H. + WarmerdamElke + ElshehabiMorad + SchlenstedtChristian + BergeestLu-Marie + HellerMaren + HaertnerLinda + FerreiraJoaquim J. + BergDaniela + SchmidtGerhard + HansenClint + MaetzlerWalter + + 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 + Frontiers in Neurology + 2018 + 9 + 10.3389/fneur.2018.00652 + 652 + + + + + + + ÜstunT Bedirhan + ChatterjiSomnath + BickenbachJerome + KostanjsekNenad + SchneiderMargie + + The international classification of functioning, disability and health: A new tool for understanding disability and health + Disability and rehabilitation + Taylor & Francis + 2003 + 25 + 11-12 + 10.1080/0963828031000137063 + 565 + 571 + + + + + + Van HeesVincent T + GorzelniakLukas + Dean LeónEmmanuel Carlos + EderMartin + PiasMarcelo + TaherianSalman + EkelundUlf + RenströmFrida + FranksPaul W + HorschAlexander + others + + Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity + PloS one + Public Library of Science San Francisco, USA + 2013 + 8 + 4 + 10.1371/journal.pone.0061691 + e61691 + + + + + + + WarmerdamElke + HausdorffJeffrey M + AtrsaeiArash + ZhouYuhan + MirelmanAnat + AminianKamiar + EspayAlberto J + HansenClint + EversLuc J W + KellerAndreas + LamothClaudine + PilottoAndrea + RochesterLynn + SchmidtGerhard + BloemBastiaan R + MaetzlerWalter + + Long-term unsupervised mobility assessment in movement disorders + The Lancet Neurology + 2020 + 19 + 5 + 10.1016/S1474-4422(19)30397-7 + 462 + 470 + + + + + + WilkinsonMark D + DumontierMichel + AalbersbergIJsbrand Jan + AppletonGabrielle + AxtonMyles + BaakArie + BlombergNiklas + BoitenJan-Willem + Silva SantosLuiz Bonino da + BournePhilip E + others + + The FAIR guiding principles for scientific data management and stewardship + Scientific data + Nature Publishing Group + 2016 + 3 + 1 + 10.1038/sdata.2016.18 + 1 + 9 + + + + + + SchootenMirjam AND Rispens vanKimberley S. AND Pijnappels + + Estimate gait quality composite + GitHub repository + GitHub + 2016 + https://github.com/KimvanS/EstimateGaitQualityComposite + + + + + + KidzińskiŁukasz + YangBryan + HicksJennifer L + RajagopalApoorva + DelpScott L + SchwartzMichael H + + Mobile-gaitlab + GitHub repository + GitHub + 2020 + https://github.com/stanfordnmbl/mobile-gaitlab + + + + + Mobilise-D consortium + 2019 + https://mobilise-d.eu/ + + + + + + Micó-AmigoM. Encarna + BonciTecla + Paraschiv-IonescuAnisoara + UllrichMartin + KirkCameron + SoltaniAbolfazl + KüderleArne + GazitEran + SalisFrancesca + AlcockLisa + AminianKamiar + BeckerClemens + BertulettiStefano + BrownPhilip + BuckleyEllen + CantuAlma + CarsinAnne-Elie + CarusoMarco + CaulfieldBrian + CereattiAndrea + ChiariLorenzo + D’AscanioIlaria + EskofierBjoern + FernstadSara + FroehlichMarcel + Garcia-AymerichJudith + HansenClint + HausdorffJeffrey M. + HidenHugo + HumeEmily + KeoghAlison + KlugeFelix + KochSarah + MaetzlerWalter + MegaritisDimitrios + MuellerArne + NiessenMartijn + PalmeriniLuca + SchwickertLars + ScottKirsty + SharrackBasil + SillénHenrik + SingletonDavid + VereijkenBeatrix + VogiatzisIoannis + YarnallAlison J. + RochesterLynn + MazzàClaudia + Del DinSilvia + for the Mobilise-D consortium + + Mobilise-d technical validation study recommended algorithms + GitHub repository + GitHub + 2023 + https://github.com/mobilise-d/Mobilise-D-TVS-Recommended-Algorithms + + + + + + PagnonD. + + Sports2D - angles from video + GitHub repository + GitHub + 2023 + https://github.com/davidpagnon/Sports2D + 10.5281/zenodo.7903963 + + + + + + The MathWorks Inc. + + MATLAB version: 9.13.0 (R2022b) + The MathWorks Inc. + Natick, Massachusetts, United States + 2022 + https://www.mathworks.com + + + + +

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