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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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