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Zhou L, Schneider J, Arnrich B, Konigorski S. Analyzing population-level trials as N-of-1 trials: An application to gait. Contemp Clin Trials Commun 2024; 38:101282. [PMID: 38533473 PMCID: PMC10964044 DOI: 10.1016/j.conctc.2024.101282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 02/08/2024] [Accepted: 02/28/2024] [Indexed: 03/28/2024] Open
Abstract
Studying individual causal effects of health interventions is important whenever intervention effects are heterogeneous between study participants. Conducting N-of-1 trials, which are single-person randomized controlled trials, is the gold standard for their analysis. As an alternative method, we propose to re-analyze existing population-level studies as N-of-1 trials, and use gait as a use case for illustration. Gait data were collected from 16 young and healthy participants under fatigued and non-fatigued, as well as under single-task (only walking) and dual-task (walking while performing a cognitive task) conditions. As a reference to the N-of-1 trials approach, we first computed standard population-level ANOVA models to evaluate differences in gait parameters (stride length and stride time) across conditions. Then, we estimated the effect of the interventions on gait parameters on the individual level through Bayesian repeated-measures models, viewing each participant as their own trial, and compared the results. The results illustrated that while few overall population-level effects were visible, individual-level analyses revealed differences between participants. Baseline values of the gait parameters varied largely among all participants, and the effects of fatigue and cognitive task were also heterogeneous, with some individuals showing effects in opposite directions. These differences between population-level and individual-level analyses were more pronounced for the fatigue intervention compared to the cognitive task intervention. Following our empirical analysis, we discuss re-analyzing population studies through the lens of N-of-1 trials more generally and highlight important considerations and requirements. Our work encourages future studies to investigate individual effects using population-level data.
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Affiliation(s)
- Lin Zhou
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - Juliana Schneider
- Digital Health & Machine Learning, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - Bert Arnrich
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - Stefan Konigorski
- Digital Health & Machine Learning, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
- Department of Statistics, Harvard University, Cambridge, USA
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, NY, USA
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Küderle A, Ullrich M, Roth N, Ollenschläger M, Ibrahim AA, Moradi H, Richer R, Seifer AK, Zürl M, Sîmpetru RC, Herzer L, Prossel D, Kluge F, Eskofier BM. Gaitmap-An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:163-172. [PMID: 38487091 PMCID: PMC10939318 DOI: 10.1109/ojemb.2024.3356791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/15/2023] [Accepted: 01/17/2024] [Indexed: 03/17/2024] Open
Abstract
Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. Conclusion: The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.
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Affiliation(s)
- Arne Küderle
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Martin Ullrich
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Nils Roth
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Malte Ollenschläger
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Alzhraa A. Ibrahim
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
- Department of Molecular NeurologyFAU Erlangen91054ErlangenGermany
- Computer Science Department, Faculty of Computers and InformationAssiut UniversityAssiut Governorate71515Egypt
| | - Hamid Moradi
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Robert Richer
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Ann-Kristin Seifer
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Matthias Zürl
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Raul C. Sîmpetru
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Liv Herzer
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Dominik Prossel
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Felix Kluge
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
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García-de-Villa S, Neira GGV, Álvarez MN, Huertas-Hoyas E, Ruiz LR, Del-Ama AJ, Sánchez MCR, Jiménez AR. A database with frailty, functional and inertial gait metrics for the research of fall causes in older adults. Sci Data 2023; 10:566. [PMID: 37626053 PMCID: PMC10457385 DOI: 10.1038/s41597-023-02428-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 07/28/2023] [Indexed: 08/27/2023] Open
Abstract
The GSTRIDE database contains information of the health status assessment of 163 elderly adults. We provide socio-demographic data, functional and frailty variables, and the outcomes from tests commonly performed for the evaluation of elder people. The database contains gait parameters estimated from the measurements of an Inertial Measurement Unit (IMU) placed on the foot of volunteers. These parameters include the total walking distance, the number of strides and multiple spatio-temporal gait parameters, such as stride length, stride time, speed, foot angles and clearance, among others. The main processed database is stored, apart from MS Excel, in CSV format to ensure their usability. The database is complemented with the raw IMU recordings in TXT format, in order to let researchers test other algorithms of gait analysis. We include the Python programming codes as a base to reproduce or modify them. The database stores data to study the frailty-related parameters that distinguish faller and non-faller populations, and analyze the gait-related parameters in the frail subjects, which are essential topics for the elderly.
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Grants
- FUNDACIÓN MAPFRE "Ayudas a la investigación de Ignacio H. de Larramendi, 2020"
- Spanish Ministry of Science, Grant No. MICROCEBUS RTI2018-095168-B-C55 (MCIU/AEI/FEDER, UE) and NEXTPERCEPTION European Union project funded by ECSEL Joint Undertaking (JU), under grant agreement No.876487 (ECSEL-2019-2-RIA), which receives support from the European Union’s Horizon 2020 research and innovation programm and Finland, Spain (MCIN/AEI PCI2020-112040), Italy, Germany, Czech Republic, Belgium, Netherlands.
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Affiliation(s)
- Sara García-de-Villa
- Spanish National Research Council, Centre for Automation and Robotics (CSIC-UPM), 28500, Arganda del Rey, Spain.
- Universidad Rey Juan Carlos, Department of Signal Theory and Communications, Madrid, 28942, Spain.
| | | | - Marta Neira Álvarez
- Foundation for Research and Biomedical Innovation of the Infanta Sofía Hospital (HUIS), Department of Geriatrics, Madrid, 28702, Spain
| | - Elisabet Huertas-Hoyas
- Universidad Rey Juan Carlos, Department of Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine, Madrid, 28922, Spain
| | - Luisa Ruiz Ruiz
- Spanish National Research Council, Centre for Automation and Robotics (CSIC-UPM), 28500, Arganda del Rey, Spain
- Universidad de Alcalá, PhD Student, Alcalá de Henares, 28801, Madrid, Spain
| | - Antonio J Del-Ama
- Universidad Rey Juan Carlos, Electronic Technology Area, Madrid, 28933, Spain
| | | | - Antonio R Jiménez
- Spanish National Research Council, Centre for Automation and Robotics (CSIC-UPM), 28500, Arganda del Rey, Spain
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Zhou L, Fischer E, Brahms CM, Granacher U, Arnrich B. DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units. Sci Data 2023; 10:543. [PMID: 37604913 PMCID: PMC10442385 DOI: 10.1038/s41597-023-02391-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 07/17/2023] [Indexed: 08/23/2023] Open
Abstract
In recent years, there has been a growing interest in developing and evaluating gait analysis algorithms based on inertial measurement unit (IMU) data, which has important implications, including sports, assessment of diseases, and rehabilitation. Multi-tasking and physical fatigue are two relevant aspects of daily life gait monitoring, but there is a lack of publicly available datasets to support the development and testing of methods using a mobile IMU setup. We present a dataset consisting of 6-minute walks under single- (only walking) and dual-task (walking while performing a cognitive task) conditions in unfatigued and fatigued states from sixteen healthy adults. Especially, nine IMUs were placed on the head, chest, lower back, wrists, legs, and feet to record under each of the above-mentioned conditions. The dataset also includes a rich set of spatio-temporal gait parameters that capture the aspects of pace, symmetry, and variability, as well as additional study-related information to support further analysis. This dataset can serve as a foundation for future research on gait monitoring in free-living environments.
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Affiliation(s)
- Lin Zhou
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Germany.
| | - Eric Fischer
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Germany
| | - Clemens Markus Brahms
- Division of Training and Movement Sciences, University of Potsdam, 14469, Potsdam, Germany
- Department of Sport and Sport Science, Exercise and Human Movement Science, University of Freiburg, 79102, Freiburg, Germany
| | - Urs Granacher
- Department of Sport and Sport Science, Exercise and Human Movement Science, University of Freiburg, 79102, Freiburg, Germany
| | - Bert Arnrich
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Germany.
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Verbiest JR, Bonnechère B, Saeys W, Van de Walle P, Truijen S, Meyns P. Gait Stride Length Estimation Using Embedded Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:7166. [PMID: 37631706 PMCID: PMC10459491 DOI: 10.3390/s23167166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/12/2023] [Accepted: 07/17/2023] [Indexed: 08/27/2023]
Abstract
INTRODUCTION Spatiotemporal gait parameters, e.g., gait stride length, are measurements that are classically derived from instrumented gait analysis. Today, different solutions are available for gait assessment outside the laboratory, specifically for spatiotemporal gait parameters. Such solutions are wearable devices that comprise an inertial measurement unit (IMU) sensor and a microcontroller (MCU). However, these existing wearable devices are resource-constrained. They contain a processing unit with limited processing and memory capabilities which limit the use of machine learning to estimate spatiotemporal gait parameters directly on the device. The solution for this limitation is embedded machine learning or tiny machine learning (tinyML). This study aims to create a machine-learning model for gait stride length estimation deployable on a microcontroller. MATERIALS AND METHOD Starting from a dataset consisting of 4467 gait strides from 15 healthy people, measured by IMU sensor, and using state-of-the-art machine learning frameworks and machine learning operations (MLOps) tools, a multilayer 1D convolutional float32 and int8 model for gait stride length estimation was developed. RESULTS The developed float32 model demonstrated a mean accuracy and precision of 0.23 ± 4.3 cm, and the int8 model demonstrated a mean accuracy and precision of 0.07 ± 4.3 cm. The memory usage for the float32 model was 284.5 kB flash and 31.9 kB RAM. The int8 model memory usage was 91.6 kB flash and 13.6 kB RAM. Both models were able to be deployed on a Cortex-M4F 64 MHz microcontroller with 1 MB flash memory and 256 kB RAM. CONCLUSIONS This study shows that estimating gait stride length directly on a microcontroller is feasible and demonstrates the potential of embedded machine learning, or tinyML, in designing wearable sensor devices for gait analysis.
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Affiliation(s)
- Joeri R. Verbiest
- Department of Sciences and Technology, Karel de Grote (KdG) University of Applied Sciences and Arts, 2660 Antwerp, Belgium
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium
| | - Bruno Bonnechère
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium
- Technology-Supported and Data-Driven Rehabilitation, Data Science Institute, Hasselt University, 3590 Diepenbeek, Belgium
| | - Wim Saeys
- Department of Rehabilitation Sciences and Physiotherapy, MOVANT, Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, 2610 Antwerp, Belgium
| | - Patricia Van de Walle
- Department of Rehabilitation Sciences and Physiotherapy, MOVANT, Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, 2610 Antwerp, Belgium
- Clinical Gait Analysis Laboratory Antwerp, Heder, Ekeren, 2180 Antwerp, Belgium
| | - Steven Truijen
- Department of Rehabilitation Sciences and Physiotherapy, MOVANT, Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, 2610 Antwerp, Belgium
| | - Pieter Meyns
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium
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Vertical Jump Data from Inertial and Optical Motion Tracking Systems. DATA 2022. [DOI: 10.3390/data7080116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Motion capture (MOCAP) is a widely used technique to record human, animal, and object movement for various applications such as animation, biomechanical assessment, and control systems. Different systems have been proposed based on diverse technologies, such as visible light cameras, infrared cameras with passive or active markers, inertial systems, or goniometer-based systems. Each system has pros and cons that make it usable in different scenarios. This paper presents a dataset that combines Optical Motion and Inertial Systems, capturing a well-known sports movement as the vertical jump. As a reference system, the optical motion capture consists of six Flex 3 Optitrack cameras with 100 FPS. On the other hand, we developed an inertial system consisting of seven custom-made devices based on the IMU MPU-9250, which includes a three-axis magnetometer, accelerometer and gyroscope, and an embedded Digital Motion Processor (DMP) attached to a microcontroller mounted on a Teensy 3.2 with an ARM Cortex-M4 processor with wireless operation using Bluetooth. The purpose of taking IMU data with a low-cost and customized system is the deployment of applications that can be performed with similar hardware and can be adjusted to different areas. The developed measurement system is flexible, and the acquisition format and enclosure can be customized. The proposed dataset comprises eight jumps recorded from four healthy humans using both systems. Experimental results on the dataset show two usage examples for measuring joint angles and COM position. The proposed dataset is publicly available online and can be used in comparative algorithms, biomechanical studies, skeleton reconstruction, sensor fusion techniques, or machine learning models.
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Measurements of User and Sensor Data from the Internet of Things (IoT) Devices. DATA 2022. [DOI: 10.3390/data7050052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The evolution of modern cyber-physical systems and the tremendous growth in the number of interconnected Internet of Things (IoT) devices are already paving new ways for the development of improved data collection and processing methods [...]
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