Open-source software library for real-time inertial measurement unit data-based inverse kinematics using OpenSim

Background Inertial measurements (IMUs) facilitate the measurement of human motion outside the motion laboratory. A commonly used open-source software for musculoskeletal simulation and analysis of human motion, OpenSim, includes a tool to enable kinematics analysis of IMU data. However, it only enables offline analysis, i.e., analysis after the data has been collected. Extending OpenSim’s functionality to allow real-time kinematics analysis would allow real-time feedback for the subject during the measurement session and has uses in e.g., rehabilitation, robotics, and ergonomics. Methods We developed an open-source software library for real-time inverse kinematics (IK) analysis of IMU data using OpenSim. The software library reads data from IMUs and uses multithreading for concurrent calculation of IK. Its operation delays and throughputs were measured with a varying number of IMUs and parallel computing IK threads using two different musculoskeletal models, one a lower-body and torso model and the other a full-body model. We published the code under an open-source license on GitHub. Results A standard desktop computer calculated full-body inverse kinematics from treadmill walking at 1.5 m/s with data from 12 IMUs in real-time with a mean delay below 55 ms and reached a throughput of more than 90 samples per second. A laptop computer had similar delays and reached a throughput above 60 samples per second with treadmill walking. Minimal walking kinematics, motion of lower extremities and torso, were calculated from treadmill walking data in real-time with a throughput of 130 samples per second on the laptop and 180 samples per second on the desktop computer, with approximately half the delay of full-body kinematics. Conclusions The software library enabled real-time inverse kinematical analysis with different numbers of IMUs and customizable musculoskeletal models. The performance results show that subject-specific full-body motion analysis is feasible in real-time, while a laptop computer and IMUs allowed the use of the method outside the motion laboratory

Estimation methods for musculoskeletal movement and loading using biomechanical models and portable modalities: bringing modern musculoskeletal analysis methods outside the motion laboratory

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PARITALON SUUNNITTELU JA RAKENTAMINEN JOENSUUN LÄHIALUEELLE

Opinnäytetyön tavoitteena oli tehdä suunnitelmat ja kustannuslaskelmat paritaloprojektia varten. Ajatus projektista lähti keskustelusta paritalon rakentamisesta. Toimeksiantajaa työllä ei ollut, joten se tehtiin omien toiveiden ja tavoitteiden pohjalta. Opinnäytetyössä tehtiin suunnitelmat puu- ja harkkorakenteiselle talolle. Sisämitoiltaan ne pidettiin samankokoisina ja pohjaratkaisu oli sama, jotta niitä voitaisiin helposti vertailla keskenään. Kustannusten vertailupohjan saamiseksi suoritettiin kysely, josta saatiin hyviä tuloksia esimerkiksi sähkökulutuksesta ja asuntojen ostohinnoista. Kustannusten osalta tutkittiin materiaalien merkitystä kokonaishintaan. Kustannuksista tehtiin molempien talotyyppien osalta kaksi erilaista laskelmaa, jossa toisessa laskelmassa on mukana ammattilaisen tekemä työ ja toisessa työn osuus on rajattu vain ammattilaista vaativiin töihin. Suunnitelmien ja kustannuslaskelmien tulokseksi tuli kuva tämänhetkisestä rakentamisen hintatasosta. Pohdittavaksi vielä projektin toteutuksen osalta jää kuitenkin, millaiseen rakennetyyppiin on valmis rahallisesti panostamaan.The purpose of this thesis was to make plans and cost calculations for a semi-detached house project. The idea for a project started in a superficial small talk about building a semi-detached house. As the project did not have a commissioner, the thesis was carried out by our own requirements and needs. The thesis includes plans for a wood and an ashlar house. In order to be able to compare them, the inside measures and layouts are similar in both cases. The thesis studies the expenses on the basis of materials to the total price. There are two kinds of calculations for both construction types. The one calculation includes professional work and the other one includes only the work requiring professionals. The comparison provides a lot of information for building and living costs. The results of plans and cost calculations show a clear picture regarding actual building costs. When considering a building project one must be aware of the financial requirements regarding each building type

Open-Source Software Library for Real-Time Inertial Measurement Unit Data-Based Inverse Kinematics using OpenSim

Abstract Background: An open-source software library for multithreaded real-time inverse kinematical (IK) analysis of inertial measurement unit (IMU) data using OpenSim was developed. Its operation delays and throughputs were measured with a varying number of IMUs and parallel computing IK threads using two different musculoskeletal models, one a lower-body and torso model and the other a full-body model. Results: Full-body inverse kinematics with data from 12 IMUs could be calculated in real-time with a mean delay below 100 ms and at more than 900 samples per second. Live visualization of IK is an option but results in limited IK throughput. The effect of this limitation was assessed by comparing the range of motion (ROM) of each joint from visualized real-time IK to the ROM from offline IK at IMU sampling frequency, resulting in mean ROM differences below 0.3 degrees. Conclusions: The software library enables real-time inverse kinematical analysis with different numbers of IMUs and customizable musculoskeletal models, making it possible to do subject-specific full-body motion analysis outside the motion laboratory in real-time.</jats:p

Open-Source Software Library For Real-Time Inertial Measurement Unit Data-Based Inverse Kinematics Using OpenSim

Abstract An open-source software library for multithreaded real-time inverse kinematical (IK) analysis of inertial measurement unit (IMU) data using OpenSim was developed. Its operation delays and throughputs were measured with a varying number of IMUs and parallel computing IK threads using two different musculoskeletal models, one a lower-body and torso model and the other a full-body model. Full-body inverse kinematics with data from 12 IMUs could be calculated in real-time with a mean delay below 100 ms and at more than 900 samples per second. Live visualization of IK is an option but results in limited IK throughput. The effect of this limitation was assessed by comparing the range of motion (ROM) of each joint from visualized real-time IK to the ROM from offline IK at IMU sampling frequency, resulting in mean ROM differences below 0.3 degrees. The software library enables real-time inverse kinematical analysis with different numbers of IMUs and customizable musculoskeletal models, making it possible to do subject-specific full-body motion analysis outside the motion laboratory in real-time.</jats:p

Dataset of knee joint contact force peaks and corresponding subject characteristics from 4 open datasets

This dataset contains data from overground walking trials of 166 subjects with several trials per subject (approximately 2900 trials total). DATA ORIGINS &amp; LICENSE INFORMATION The data comes from four existing open datasets collected by others: Schreiber &amp; Moissenet, A multimodal dataset of human gait at different walking speeds established on injury-free adult participants article: https://www.nature.com/articles/s41597-019-0124-4 dataset: https://figshare.com/articles/dataset/A_multimodal_dataset_of_human_gait_at_different_walking_speeds/7734767 Fukuchi et al., A public dataset of overground and treadmill walking kinematics and kinetics in healthy individuals article: https://peerj.com/articles/4640/ dataset: https://figshare.com/articles/dataset/A_public_data_set_of_overground_and_treadmill_walking_kinematics_and_kinetics_of_healthy_individuals/5722711 Horst et al., A public dataset of overground walking kinetics and full-body kinematics in healthy adult individuals article: https://www.nature.com/articles/s41598-019-38748-8 dataset: https://data.mendeley.com/datasets/svx74xcrjr/3 Camargo et al., A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions article: https://www.sciencedirect.com/science/article/pii/S0021929021001007 dataset (3 links): https://data.mendeley.com/datasets/fcgm3chfff/1 https://data.mendeley.com/datasets/k9kvm5tn3f/1 https://data.mendeley.com/datasets/jj3r5f9pnf/1 In this dataset, those datasets are referred to as the Schreiber, Fukuchi, Horst, and Camargo datasets, respectively. The Schreiber, Fukuchi, Horst, and Camargo datasets are licensed under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). We have modified the datasets by analyzing the data with musculoskeletal simulations &amp; analysis software (OpenSim). In this dataset, we publish modified data as well as some of the original data. STRUCTURE OF THE DATASET The dataset contains two kinds of text files: those starting with &quot;predictors_&quot; and those starting with &quot;response_&quot;. Predictors comprise 12 text files, each describing the input (predictor) variables we used to train artifical neural networks to predict knee joint loading peaks. Responses similarly comprise 12 text files, each describing the response (outcome) variables that we trained and evaluated the network on. The file names are of the form &quot;predictors_X&quot; for predictors and &quot;response_X&quot; for responses, where X describes which response (outcome) variable is predicted with them. X can be: - loading_response_both: the maximum of the first peak of stance for the sum of the loading of the medial and lateral compartments - loading_response_lateral: the maximum of the first peak of stance for the loading of the lateral compartment - loading_response_medial: the maximum of the first peak of stance for the loading of the medial compartment - terminal_extension_both: the maximum of the second peak of stance for the sum of the loading of the medial and lateral compartments - terminal_extension_lateral: the maximum of the second peak of stance for the loading of the lateral compartment - terminal_extension_medial: the maximum of the second peak of stance for the loading of the medial compartment - max_peak_both: the maximum of the entire stance phase for the sum of the loading of the medial and lateral compartments - max_peak_lateral: the maximum of the entire stance phase for the loading of the lateral compartment - max_peak_medial: the maximum of the entire stance phase for the loading of the medial compartment - MFR_common: the medial force ratio for the entire stance phase - MFR_LR: the medial force ratio for the first peak of stance - MFR_TE: the medial force ratio for the second peak of stance The predictor text files are organized as comma-separated values. Each row corresponds to one walking trial. A single subject typically has several trials. The column labels are DATASET_INDEX,SUBJECT_INDEX,KNEE_ADDUCTION,MASS,HEIGHT,BMI,WALKING_SPEED,HEEL_STRIKE_VELOCITY,AGE,GENDER. DATASET_INDEX describes which original dataset the trial is from, where {1=Schreiber, 2=Fukuchi, 3=Horst, 4=Camargo} SUBJECT_INDEX is the index of the subject in the original dataset. If you use this column, you will have to rewrite these to avoid duplicates (e.g., several datasets probably have subject &quot;3&quot;). KNEE_ADDUCTION is the knee adduction-abduction angle (positive for adduction, negative for abduction) of the subject in static pose, estimated from motion capture markers. MASS is the mass of the subject in kilograms HEIGHT is the height of the subject in millimeters BMI is the body mass index of the subject WALKING_SPEED is the mean walking speed of the subject during the trial HEEL_STRIKE_VELOCITY is the mean of the velocities of the subject&#39;s pelvis markers at the instant of heel strike AGE is the age of the subject in years GENDER is an integer/boolean where {1=male, 0=female} The response text files contain one floating-point value per row, describing the knee joint contact force peak for the trial in newtons (or the medial force ratio). Each row corresponds to one walking trial. The rows in predictor and response text files match each other (e.g., row 7 describes the same trial in both predictors_max_peak_medial.txt and response_max_peak_medial.txt). See our journal article &quot;Prediction of Knee Joint Compartmental Loading Maxima Utilizing Simple Subject Characteristics and Neural Networks&quot; (https://doi.org/10.1007/s10439-023-03278-y) for more information. Questions &amp; other contacts: [email protected]

Prediction of Knee Joint Compartmental Loading Maxima Utilizing Simple Subject Characteristics and Neural Networks

Joint loading may affect the development of osteoarthritis, but patient-specific load estimation requires cumbersome motion laboratory equipment. This reliance could be eliminated using artificial neural networks (ANNs) to predict loading from simple input predictors. We used subject-specific musculoskeletal simulations to estimate knee joint contact forces for 290 subjects during over 5000 stance phases of walking and then extracted compartmental and total joint loading maxima from the first and second peaks of the stance phase. We then trained ANN models to predict the loading maxima from predictors that can be measured without motion laboratory equipment (subject mass, height, age, gender, knee abduction-adduction angle, and walking speed). When compared to the target data, our trained models had NRMSEs (RMSEs normalized to the mean of the response variable) between 0.14 and 0.42 and Pearson correlation coefficients between 0.42 and 0.84. The loading maxima were predicted most accurately using the models trained with all predictors. We demonstrated that prediction of knee joint loading maxima may be possible without laboratory-measured motion capture data. This is a promising step in facilitating knee joint loading predictions in simple environments, such as a physician's appointment. In future, the rapid measurement and analysis setup could be utilized to guide patients in rehabilitation to slow development of joint disorders, such as osteoarthritis.</p

A Perturbed Postural Balance Test Using an Instrumented Treadmill – Precision and Accuracy of Belt Movement and Test-Retest Reliability of Balance Measures

A perturbed postural balance test can be used to investigate balance control under mechanical disturbances. The test is typically performed using purpose-built movable force plates. As instrumented treadmills become increasingly common in biomechanics laboratories and in clinical settings, these devices could be potentially used to assess and train balance control. The purpose of the study was to investigate how an instrumented treadmill applies to perturbed postural balance test. This was investigated by assessing the precision and reliability of the treadmill belt movement and the test-retest reliability of perturbed postural balance test over 5 days. Postural balance variables were calculated from the center of pressure trajectory and included peak displacement, time to peak displacement, and recovery displacement. Additionally, the study investigated short-term learning effects over the 5 days. Eight healthy participants (aged 24–43 years) were assessed for 5 consecutive days with four different perturbation protocols. Center of pressure (COP) data were collected using the force plates of the treadmill while participant and belt movements were measured with an optical motion capture system. The results show that the treadmill can reliably deliver the intended perturbations with &amp;lt;1% deviation in total displacement and with minimal variability between days and participants (typical errors 0.06–2.71%). However, the treadmill was not able to reach the programmed 4 m/s2 acceleration, reaching only about 75% of it. Test–retest reliability of the selected postural balance variables ranged from poor to good (ICC 0.156–0.752) with typical errors between 4.3 and 28.2%. Learning effects were detected based on linear or quadratic trends (p &amp;lt; 0.05) in peak displacement of the slow forward and fast backward protocols and in time to peak displacement in slow and fast backward protocols. The participants altered the initial location of the COP relative to the foot depending on the direction of the perturbation. In conclusion, the precision and accuracy of belt movement were found to be excellent. Test-retest reliability of the balance test utilizing an instrumented treadmill ranged from poor to good which is, in line with previous investigations using purpose-built devices for perturbed postural balance assessment.</jats:p

IMU data from walking trials on a treadmill

This dataset contains inertial measurement unit data from 10 treadmill walking trials. The subject is of legal age and wore a total of 12 Xsens MTw Awinda IMUs. More details in our publication (https://doi.org/10.7717/peerj.15097). Includes a readme file with details