Comparison of lower limb calibration methods for movement analysis with inertial measurement unit (IMU)

Pacher, L.; Châtellier, Christian; Vauzelle, Rodolphe; Fradet, Laetitia

doi:10.1080/10255842.2020.1813432

Cited by 4 publications

(7 citation statements)

References 8 publications

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“…Another crucial aspect of MAS is the sensor calibration procedure [ 27 , 29 , 55 ]. Di Raimondo et al [ 27 ] describe three possible types of IMU calibration: manual, static, and functional calibration.…”

Section: Discussionmentioning

confidence: 99%

SensAA—Design and Verification of a Cloud-Based Wearable Biomechanical Data Acquisition System

David,

Schick,

Rapp

et al. 2024

Sensors

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Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data were uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9° to 8°, which is below that of most comparable systems. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics.

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Section: Discussionmentioning

confidence: 99%

SensAA—Design and Verification of a Cloud-Based Wearable Biomechanical Data Acquisition System

David,

Schick,

Rapp

et al. 2024

Sensors

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show abstract

“…This position allowed a recording with all the markers to reconstruct marker trajectories when there were marker trajectory gaps by rigid transformation 21 . The proper propostion of the subject was carefully controlled 1) before starting the acquisition since the task was to be used for IMU-to-segment calibration 16,22,23 . The verbal instruction was: "Spread your arms slightly and stay in a static position for 10 seconds without moving. "…”

Section: Methodsmentioning

confidence: 99%

“…Because of the length of all the verbal instructions, these are detailed in Supplementary Table 1. These trials can be used to perform IMU-to-segment calibration 16,22,23 .…”

Section: Methodsmentioning

confidence: 99%

“…IMUs position and orientation were tracked using the marker clusters to perform calibrations. Indeed, many studies have shown the importance of performing a IMU-to-segment calibration 16,22,23 to ensure that IMUs are properly aligned with the anatomical axes. This calibration largely influences kinematics computation 22 .…”

Section: Methodsmentioning

confidence: 99%

“…This calibration largely influences kinematics computation 22 . Several IMU-to-segment approaches exist; the most common being the functional calibration which consists of aligning the sensors and segments frames based on specific 2D-movements realized by the subject 23 . Thus, the proposed dataset 9 is composed of several functional tasks performed in different anatomical planes to allow IMU-to-segment calibration (e.g.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A dataset of asymptomatic human gait and movements obtained from markers, IMUs, insoles and force plates

et al. 2023

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Human motion capture and analysis could be made easier through the use of wearable devices such as inertial sensors and/or pressure insoles. However, many steps are still needed to reach the performance of optoelectronic systems to compute kinematic parameters. The proposed dataset has been established on 10 asymptomatic adults. Participants were asked to walk at different speeds on a 10-meters walkway in a laboratory and to perform different movements such as squats or knee flexion/extension tasks. Three-dimensional trajectories of 69 reflective markers placed according to a conventional full body markerset, acceleration and angular velocity signals of 8 inertial sensors, pressure signals of 2 insoles, 3D ground reaction forces and moments obtained from 3 force plates were simultaneously recorded. Eight calculated virtual markers related to joint centers were also added to the dataset. This dataset contains a total of 337 trials including static and dynamic tasks for each participant. Its purpose is to enable comparisons between various motion capture systems and stimulate the development of new methods for gait analysis.

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SensAA – Design and Verification of a Cloud-based Wearable Biomechanical Data Acquisition System

David,

Schick,

Rapp

et al. 2024

Preprint

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Exoskeletons for the purpose of rehabilitation become more and more popular. These devices are often designed for clinical therapy and provide assistance for the user. Exoskeletons for assistive purposes are still subject to research. Hence, only little requirements for the mechanics, actuation or power supply are known for this use-case. One way to specify these requirements is the calculation of expected load collectives during everyday life of the target group. The calculation of these load collectives requires long term gait observation of the subjects. In this paper, we propose a data acquisition system focused on collecting lower limb biomechanical measurements in everyday life. The system is designed to be cost efficient and user-optimized, using off-the-shelf components and a Cloud Server System for centralized data storage.

show abstract

Comparison of lower limb calibration methods for movement analysis with inertial measurement unit (IMU)

Cited by 4 publications

References 8 publications

SensAA—Design and Verification of a Cloud-Based Wearable Biomechanical Data Acquisition System

SensAA—Design and Verification of a Cloud-Based Wearable Biomechanical Data Acquisition System

A dataset of asymptomatic human gait and movements obtained from markers, IMUs, insoles and force plates

SensAA – Design and Verification of a Cloud-based Wearable Biomechanical Data Acquisition System

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