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Lee DH, Cao D, Moon Y, Chen C, Liu NK, Xu XM, Wu W. Enhancement of motor functional recovery in thoracic spinal cord injury: voluntary wheel running versus forced treadmill exercise. Neural Regen Res 2025; 20:836-844. [PMID: 38886956 DOI: 10.4103/nrr.nrr-d-23-01585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/19/2024] [Indexed: 06/20/2024] Open
Abstract
JOURNAL/nrgr/04.03/01300535-202503000-00028/figure1/v/2024-06-17T092413Z/r/image-tiff Spinal cord injury necessitates effective rehabilitation strategies, with exercise therapies showing promise in promoting recovery. This study investigated the impact of rehabilitation exercise on functional recovery and morphological changes following thoracic contusive spinal cord injury. After a 7-day recovery period after spinal cord injury, mice were assigned to either a trained group (10 weeks of voluntary running wheel or forced treadmill exercise) or an untrained group. Bi-weekly assessments revealed that the exercise-trained group, particularly the voluntary wheel exercise subgroup, displayed significantly improved locomotor recovery, more plasticity of dopaminergic and serotonin modulation compared with the untrained group. Additionally, exercise interventions led to gait pattern restoration and enhanced transcranial magnetic motor-evoked potentials. Despite consistent injury areas across groups, exercise training promoted terminal innervation of descending axons. In summary, voluntary wheel exercise shows promise for enhancing outcomes after thoracic contusive spinal cord injury, emphasizing the role of exercise modality in promoting recovery and morphological changes in spinal cord injuries. Our findings will influence future strategies for rehabilitation exercises, restoring functional movement after spinal cord injury.
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Affiliation(s)
- Do-Hun Lee
- Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Dan Cao
- Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Younghye Moon
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Chen Chen
- Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Nai-Kui Liu
- Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Xiao-Ming Xu
- Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Wei Wu
- Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
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Long T, Outerleys J, Yeung T, Fernandez J, Bouxsein ML, Davis IS, Bredella MA, Besier TF. Predicting ankle and knee sagittal kinematics and kinetics using an ankle-mounted inertial sensor. Comput Methods Biomech Biomed Engin 2024; 27:1057-1070. [PMID: 37516980 DOI: 10.1080/10255842.2023.2224912] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/11/2023] [Accepted: 06/07/2023] [Indexed: 08/01/2023]
Abstract
The purpose of this study was to develop a machine learning model to reconstruct time series kinematic and kinetic profiles of the ankle and knee joint across six different tasks using an ankle-mounted IMU. Four male collegiate basketball players performed repeated tasks, including walking, jogging, running, sidestep cutting, max-height jumping, and stop-jumping, resulting in a total of 102 movements. Ankle and knee flexion-extension angles and moments were estimated using motion capture and inverse dynamics and considered 'actual data' for the purpose of model fitting. Synchronous acceleration and angular velocity data were collected from right ankle-mounted IMUs. A time-series feature extraction model was used to determine a set of features used as input to a random forest regression model to predict the ankle and knee kinematics and kinetics. Five-fold cross-validation was performed to verify the model accuracy, and statistical parametric mapping was used to determine the difference between the predicted and experimental time series. The random forest regression model predicted the time-series profiles of the ankle and knee flexion-extension angles and moments with high accuracy (Kinematics: R2 ranged from 0.782 to 0.962, RMSE ranged from 2.19° to 11.58°; Kinetics: R2 ranged from 0.711 to 0.966, RMSE ranged from 0.10 Nm/kg to 0.41 Nm/kg). There were differences between predicted and actual time series for the knee flexion-extension moment during stop-jumping and walking. An appropriately trained feature-based regression model can predict time series knee and ankle joint angles and moments across a wide range of tasks using a single ankle-mounted IMU.
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Affiliation(s)
- Ting Long
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Jereme Outerleys
- Spaulding National Running Center, Harvard Medical School, Cambridge, MA, USA
| | - Ted Yeung
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Justin Fernandez
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - Mary L Bouxsein
- Beth Israel Deaconess Medical Center, Harvard Medical School, Cambridge, MA, USA
| | - Irene S Davis
- Spaulding National Running Center, Harvard Medical School, Cambridge, MA, USA
| | - Miriam A Bredella
- Massachusetts General Hospital and Harvard Medical School, Cambridge, MA, USA
| | - Thor F Besier
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
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Evans S. Sacroiliac Joint Dysfunction in Endurance Runners Using Wearable Technology as a Clinical Monitoring Tool: Systematic Review. JMIR BIOMEDICAL ENGINEERING 2024; 9:e46067. [PMID: 38875697 PMCID: PMC11148519 DOI: 10.2196/46067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 10/02/2023] [Accepted: 10/30/2023] [Indexed: 06/16/2024] Open
Abstract
BACKGROUND In recent years, researchers have delved into the relationship between the anatomy and biomechanics of sacroiliac joint (SIJ) pain and dysfunction in endurance runners to elucidate the connection between lower back pain and the SIJ. However, the majority of SIJ pain and dysfunction cases are diagnosed and managed through a traditional athlete-clinician arrangement, where the athlete must attend regular in-person clinical appointments with various allied health professionals. Wearable sensors (wearables) are increasingly serving as a clinical diagnostic tool to monitor an athlete's day-to-day activities remotely, thus eliminating the necessity for in-person appointments. Nevertheless, the extent to which wearables are used in a remote setting to manage SIJ dysfunction in endurance runners remains uncertain. OBJECTIVE This study aims to conduct a systematic review of the literature to enhance our understanding regarding the use of wearables in both in-person and remote settings for biomechanical-based rehabilitation in SIJ dysfunction among endurance runners. In addressing this issue, the overarching goal was to explore how wearables can contribute to the clinical diagnosis (before, during, and after) of SIJ dysfunction. METHODS Three online databases, including PubMed, Scopus, and Google Scholar, were searched using various combinations of keywords. Initially, a total of 4097 articles were identified. After removing duplicates and screening articles based on inclusion and exclusion criteria, 45 articles were analyzed. Subsequently, 21 articles were included in this study. The quality of the investigation was assessed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) evidence-based minimum set of items for reporting in systematic reviews. RESULTS Among the 21 studies included in this review, more than half of the investigations were literature reviews focusing on wearable sensors in the diagnosis and treatment of SIJ pain, wearable movement sensors for rehabilitation, or a combination of both for SIJ gait analysis in an intelligent health care setting. As many as 4 (19%) studies were case reports, and only 1 study could be classified as fully experimental. One paper was classified as being at the "pre" stage of SIJ dysfunction, while 6 (29%) were identified as being at the "at" stage of classification. Significantly fewer studies attempted to capture or classify actual SIJ injuries, and no study directly addressed the injury recovery stage. CONCLUSIONS SIJ dysfunction remains underdiagnosed and undertreated in endurance runners. Moreover, there is a lack of clear diagnostic or treatment pathways using wearables remotely, despite the availability of validated technology. Further research of higher quality is recommended to investigate SIJ dysfunction in endurance runners and explore the use of wearables for rehabilitation in remote settings.
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Affiliation(s)
- Stuart Evans
- School of Education, La Trobe University, Melbourne, Australia
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Madden TS, Hawkins DA. Increasing Step Rate Reduces Peak and Cumulative Insole Force in Collegiate Runners. Med Sci Sports Exerc 2024; 56:982-989. [PMID: 37486767 DOI: 10.1249/mss.0000000000003261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
PURPOSE The primary goal of this study was to examine changes in peak insole force and cumulative weighted peak force (CWPF)/km with increased step rate in collegiate runners. The secondary goal was to determine whether sacral acceleration correlates with insole force when increasing step rate. METHODS Twelve collegiate distance runners ran 1000 m outdoors at 3.83 m·s -1 at preferred and 10% increased step rates while insole force and sacral acceleration were recorded. Cumulative weighted peak force/km was calculated from insole force based on cumulative damage models. The effects of step rate on peak insole force and CWPF·km -1 were tested using paired t tests or Wilcoxon tests. Correlation coefficients between peak axial (approximately vertical) sacral acceleration times body mass and peak insole force were calculated on cohort and individual levels. RESULTS Peak insole force and CWPF·km -1 decreased ( P < 0.001) with increased step rate. Peak axial sacral acceleration did not correlate with peak insole force on the cohort level ( r = 0.35, P = 0.109) but did within individuals (mean, r = 0.69-0.78; P < 0.05). CONCLUSIONS Increasing step rate may reduce peak vGRF and CWPF·km -1 in collegiate runners. Therefore, clinicians should consider step rate interventions to reduce peak and cumulative vGRF in this population. Individual-specific calibrations may be required to assess changes in peak vGRF in response to increasing step rate using wearable accelerometers.
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Affiliation(s)
- Thomas S Madden
- Department of Mechanical Engineering, Montana State University, Bozeman, MT
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Pareja-Cano Á, Arjona JM, Caulfield B, Cuesta-Vargas A. Parameterization of Biomechanical Variables through Inertial Measurement Units (IMUs) in Occasional Healthy Runners. SENSORS (BASEL, SWITZERLAND) 2024; 24:2191. [PMID: 38610402 PMCID: PMC11014260 DOI: 10.3390/s24072191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 03/20/2024] [Accepted: 03/27/2024] [Indexed: 04/14/2024]
Abstract
Running is one of the most popular sports practiced today and biomechanical variables are fundamental to understanding it. The main objectives of this study are to describe kinetic, kinematic, and spatiotemporal variables measured using four inertial measurement units (IMUs) in runners during treadmill running, investigate the relationships between these variables, and describe differences associated with different data sampling and averaging strategies. A total of 22 healthy recreational runners (M age = 28 ± 5.57 yrs) participated in treadmill measurements, running at their preferred speed (M = 10.1 ± 1.9 km/h) with a set-up of four IMUs placed on tibias and the lumbar area. Raw data was processed and analysed over selections spanning 30 s, 30 steps and 1 step. Very strong positive associations were obtained between the same family variables in all selections. The temporal variables were inversely associated with the step rate variable in the selection of 30 s and 30 steps of data. There were moderate associations between kinetic (forces) and kinematic (displacement) variables. There were no significant differences between the biomechanics variables in any selection. Our results suggest that a 4-IMU set-up, as presented in this study, is a viable approach for parameterization of the biomechanical variables in running, and also that there are no significant differences in the biomechanical variables studied independently, if we select data from 30 s, 30 steps or 1 step for processing and analysis. These results can assist in the methodological aspects of protocol design in future running research.
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Affiliation(s)
- Álvaro Pareja-Cano
- Grupo Clinimetría en Fisioterapia (CTS 631), Department of Physiotherapy, Faculty of Health Sciences, University of Málaga, 29071 Málaga, Spain; (Á.P.-C.); (J.M.A.)
- Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA Plataforma Bionand) Grupo Clinimetria (F-14), 29590 Málaga, Spain
| | - José María Arjona
- Grupo Clinimetría en Fisioterapia (CTS 631), Department of Physiotherapy, Faculty of Health Sciences, University of Málaga, 29071 Málaga, Spain; (Á.P.-C.); (J.M.A.)
- Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA Plataforma Bionand) Grupo Clinimetria (F-14), 29590 Málaga, Spain
- Faculty of Sciences and Technology, University Isabel I, 09003 Burgos, Spain
| | - Brian Caulfield
- School of Public Health, Physiotherapy and Sports, University College Dublin, D04 C1P1 Dublin, Ireland;
- Insight Centre, University College Dublin, D04 N2E5 Dublin, Ireland
| | - Antonio Cuesta-Vargas
- Grupo Clinimetría en Fisioterapia (CTS 631), Department of Physiotherapy, Faculty of Health Sciences, University of Málaga, 29071 Málaga, Spain; (Á.P.-C.); (J.M.A.)
- Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA Plataforma Bionand) Grupo Clinimetria (F-14), 29590 Málaga, Spain
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Krishnakumar S, van Beijnum BJF, Baten CTM, Veltink PH, Buurke JH. Estimation of Kinetics Using IMUs to Monitor and Aid in Clinical Decision-Making during ACL Rehabilitation: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:2163. [PMID: 38610374 PMCID: PMC11014074 DOI: 10.3390/s24072163] [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: 01/26/2024] [Revised: 03/18/2024] [Accepted: 03/23/2024] [Indexed: 04/14/2024]
Abstract
After an ACL injury, rehabilitation consists of multiple phases, and progress between these phases is guided by subjective visual assessments of activities such as running, hopping, jump landing, etc. Estimation of objective kinetic measures like knee joint moments and GRF during assessment can help physiotherapists gain insights on knee loading and tailor rehabilitation protocols. Conventional methods deployed to estimate kinetics require complex, expensive systems and are limited to laboratory settings. Alternatively, multiple algorithms have been proposed in the literature to estimate kinetics from kinematics measured using only IMUs. However, the knowledge about their accuracy and generalizability for patient populations is still limited. Therefore, this article aims to identify the available algorithms for the estimation of kinetic parameters using kinematics measured only from IMUs and to evaluate their applicability in ACL rehabilitation through a comprehensive systematic review. The papers identified through the search were categorized based on the modelling techniques and kinetic parameters of interest, and subsequently compared based on the accuracies achieved and applicability for ACL patients during rehabilitation. IMUs have exhibited potential in estimating kinetic parameters with good accuracy, particularly for sagittal movements in healthy cohorts. However, several shortcomings were identified and future directions for improvement have been proposed, including extension of proposed algorithms to accommodate multiplanar movements and validation of the proposed techniques in diverse patient populations and in particular the ACL population.
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Affiliation(s)
- Sanchana Krishnakumar
- Department of Biomedical Signals and System, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands; (B.-J.F.v.B.); (P.H.V.); (J.H.B.)
| | - Bert-Jan F. van Beijnum
- Department of Biomedical Signals and System, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands; (B.-J.F.v.B.); (P.H.V.); (J.H.B.)
| | - Chris T. M. Baten
- Roessingh Research and Development, Roessinghsbleekweg 33B, 7522 AH Enschede, The Netherlands;
| | - Peter H. Veltink
- Department of Biomedical Signals and System, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands; (B.-J.F.v.B.); (P.H.V.); (J.H.B.)
| | - Jaap H. Buurke
- Department of Biomedical Signals and System, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands; (B.-J.F.v.B.); (P.H.V.); (J.H.B.)
- Roessingh Research and Development, Roessinghsbleekweg 33B, 7522 AH Enschede, The Netherlands;
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Florenciano Restoy JL, Solé-Casals J, Borràs-Boix X. Effect of Foot Orthoses on Angular Velocity of Feet. SENSORS (BASEL, SWITZERLAND) 2023; 23:8917. [PMID: 37960617 PMCID: PMC10650853 DOI: 10.3390/s23218917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/24/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023]
Abstract
There is some uncertainty regarding how foot orthoses (FO) affect the biomechanics of the lower extremities during running in non-injured individuals. This study aims to describe the behavior of the angular velocity of the foot in the stride cycle measured with a low-sampling-rate IMU device commonly used by podiatrists. Specific objectives were to determine if there are differences in angular velocity between the right and left foot and to determine the effect of foot orthoses (FO) on the 3D angular velocity of the foot during running. The sample was composed of 40 male adults (age: 43.0 ± 13.8 years, weight: 72.0 ± 5.5 kg, and height: 175.5 ± 7.0 cm), who were healthy and without any locomotor system alterations at the time of the test. All subjects use FO on a regular basis. The results show that there are significant differences in the transverse plane between feet, with greater differences in the right foot. Significant differences between FO and non-FO conditions were observed in the frontal and transverse planes on the left foot and in the sagittal and transverse planes on the right foot. FO decreases the velocity of the foot in dorsi-plantar flexion and abduction and increases the velocity in inversion. The kinematic changes in foot velocity occur between 30% and 60% of the complete cycle, and the FO reduces the velocity in abduction and dorsi-plantar flexion and increases the velocity in inversion-eversion, which facilitates the transition to the oscillating leg and with it the displacement of the center of mass. Quantifying possible asymmetries and assessing the effect of foot orthoses may aid in improving running mechanics and preventing injuries in individuals.
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Affiliation(s)
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic—Central University of Catalonia, 08500 Vic, Spain
| | - Xantal Borràs-Boix
- Sport Exercise and Human Movement, University of Vic—Central University of Catalonia, 08500 Vic, Spain
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Kiernan D, Ng B, Hawkins DA. Acceleration-Based Estimation of Vertical Ground Reaction Forces during Running: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns. SENSORS (BASEL, SWITZERLAND) 2023; 23:8719. [PMID: 37960420 PMCID: PMC10648662 DOI: 10.3390/s23218719] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023]
Abstract
Twenty-seven methods of estimating vertical ground reaction force first peak, loading rate, second peak, average, and/or time series from a single wearable accelerometer worn on the shank or approximate center of mass during running were compared. Force estimation errors were quantified for 74 participants across different running surfaces, speeds, and foot strike angles and biases, repeatability coefficients, and limits of agreement were modeled with linear mixed effects to quantify the accuracy, reliability, and precision. Several methods accurately and reliably estimated the first peak and loading rate, however, none could do so precisely (the limits of agreement exceeded ±65% of target values). Thus, we do not recommend first peak or loading rate estimation from accelerometers with the methods currently available. In contrast, the second peak, average, and time series could all be estimated accurately, reliably, and precisely with several different methods. Of these, we recommend the 'Pogson' methods due to their accuracy, reliability, and precision as well as their stability across surfaces, speeds, and foot strike angles.
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Affiliation(s)
- Dovin Kiernan
- Biomedical Engineering Graduate Group, University of California, Davis, Davis, CA 95616, USA
| | - Brandon Ng
- Department of Biomedical Engineering, University of California, Davis, Davis, CA 95616, USA
| | - David A. Hawkins
- Biomedical Engineering Graduate Group, University of California, Davis, Davis, CA 95616, USA
- Department of Neurobiology, Physiology, & Behavior, University of California, Davis, Davis, CA 95616, USA
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Abd-Eltawab AE, Elbandrawy AM, Ghanem HB, Farhana A. Three dimensional analysis of ground reaction force during level walking correlates with sacrum displacement. Int J Health Sci (Qassim) 2023; 17:31-38. [PMID: 37692994 PMCID: PMC10484067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023] Open
Abstract
Objective We determined the association between sacrum displacement and ground reaction force (GRF) during walking on a level surface and identify the sub-phase of gait cycle most affected by GRF. The kinematic parameters of angular displacement of sacrum bone in three directions were measured and a correlation was derived to integrate the effect of GRF to sacrum displacement. Furthermore, gender variation in the sacrum bone configuration that induces the GRF to shift in one direction was determined. Methods Forty healthy university students were evaluated for a normal gait pattern using the Qualysys motion capture system or a motion analysis system (MAS). The synchronization between MAS and force plate was done through computer software for the three-dimensional analysis (3D) of the force and angular displacement. Results A positive correlation in the vertical direction was observed in the early and late phases of the stance phase in females. In males, a positive correlation was demonstrated in the middle and late phase of the stance phase. However, a positive correlation in the anteroposterior direction during the middle part of the stance phase was found only among the male group. Conclusion Incorporation of strength training exercises help to increase the rotator muscle strength of the trunk and lower extremities in both genders. In the male group, flexors and extensors of the trunk and lower extremities in the middle part need to be focused during strength training, especially for athletes. This would be useful in decreasing the incidence of sports injuries.
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Affiliation(s)
- Amany E. Abd-Eltawab
- Department of Physical Therapy and Health Rehabilitation, Faculty of Applied Medical Sciences, Jouf University, P. O. Box 2014 Sakaka, KSA
- Department of Biomechanics, Faculty of Physical Therapy, Cairo University, Cairo, Egypt
| | - Asmaa M. Elbandrawy
- Department of Physical Therapy for Women’s Health, Faculty of Physical Therapy, Cairo University, Giza, Egypt
- Department of Physical Therapy for Women’s Health, Faculty of Physical Therapy, Al-Salam University, Gharbia, Egypt
| | - Heba B. Ghanem
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University,Sakaka, Aljouf, Saudi Arabia
- Department of Medical Biochemistry, Faculty of Medicine, Tanta University, Tanta, Egypt
| | - Aisha Farhana
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University,Sakaka, Aljouf, Saudi Arabia
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Zeng Z, Liu Y, Wang L. Validity of IMU measurements on running kinematics in non-rearfoot strike runners across different speeds. J Sports Sci 2023; 41:1083-1092. [PMID: 37733423 DOI: 10.1080/02640414.2023.2259211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/17/2023] [Indexed: 09/22/2023]
Abstract
This study aims to determine the validity of the lower extremity joint kinematics measured by inertial measurement units (IMUs) in non-rearfoot strike pattern (NRFS) runners across different speeds. Fifteen NRFS runners completed three 2-min running tests on a treadmill in random order at 8, 10 and 12 km/h, whilst data were synchronously collected using the IMU system and an optical motion capture system. Before the offset was corrected, the validity of the knee angle waveform was higher than that of the hip and ankle; after the offset was corrected, the validity increased in all three joints. The correlation between the touchdown angles in the sagittal plane measured by the two systems was relatively high after the offset was corrected. The running speed influenced the offset-corrected measurements, with higher error values at higher speeds. The IMU system was able to provide measurements of running kinematics in the sagittal plane of NRFS runners at different running speeds but was unable to reliably measure motion in the frontal and horizontal planes. Future research should analyse the 3D gait of NRFS runners under a larger range of speed conditions to provide evidentiary support for the use of IMUs in running analysis outside the laboratory.
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Affiliation(s)
- Ziwei Zeng
- Key Laboratory of Exercise and Health Sciences (Shanghai University of Sport), Ministry of Education, Shanghai, China
| | - Yue Liu
- Key Laboratory of Exercise and Health Sciences (Shanghai University of Sport), Ministry of Education, Shanghai, China
| | - Lin Wang
- Key Laboratory of Exercise and Health Sciences (Shanghai University of Sport), Ministry of Education, Shanghai, China
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Kiernan D, Dunn Siino K, Hawkins DA. Unsupervised Gait Event Identification with a Single Wearable Accelerometer and/or Gyroscope: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115022. [PMID: 37299749 DOI: 10.3390/s23115022] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 05/20/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023]
Abstract
We evaluated 18 methods capable of identifying initial contact (IC) and terminal contact (TC) gait events during human running using data from a single wearable sensor on the shank or sacrum. We adapted or created code to automatically execute each method, then applied it to identify gait events from 74 runners across different foot strike angles, surfaces, and speeds. To quantify error, estimated gait events were compared to ground truth events from a time-synchronized force plate. Based on our findings, to identify gait events with a wearable on the shank, we recommend the Purcell or Fadillioglu method for IC (biases +17.4 and -24.3 ms; LOAs -96.8 to +131.6 and -137.0 to +88.4 ms) and the Purcell method for TC (bias +3.5 ms; LOAs -143.9 to +150.9 ms). To identify gait events with a wearable on the sacrum, we recommend the Auvinet or Reenalda method for IC (biases -30.4 and +29.0 ms; LOAs -149.2 to +88.5 and -83.3 to +141.3 ms) and the Auvinet method for TC (bias -2.8 ms; LOAs -152.7 to +147.2 ms). Finally, to identify the foot in contact with the ground when using a wearable on the sacrum, we recommend the Lee method (81.9% accuracy).
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Affiliation(s)
- Dovin Kiernan
- Biomedical Engineering Graduate Group, University of California, Davis, Davis, CA 95616, USA
| | - Kristine Dunn Siino
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA 95616, USA
| | - David A Hawkins
- Biomedical Engineering Graduate Group, University of California, Davis, Davis, CA 95616, USA
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA 95616, USA
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12
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Donahue SR, Hahn ME. Estimation of ground reaction force waveforms during fixed pace running outside the laboratory. Front Sports Act Living 2023; 5:974186. [PMID: 36860734 PMCID: PMC9968876 DOI: 10.3389/fspor.2023.974186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 01/16/2023] [Indexed: 02/15/2023] Open
Abstract
In laboratory experiments, biomechanical data collections with wearable technologies and machine learning have been promising. Despite the development of lightweight portable sensors and algorithms for the identification of gait events and estimation of kinetic waveforms, machine learning models have yet to be used to full potential. We propose the use of a Long Short Term Memory network to map inertial data to ground reaction force data gathered in a semi-uncontrolled environment. Fifteen healthy runners were recruited for this study, with varied running experience: novice to highly trained runners (<15 min 5 km race), and ages ranging from 18 to 64 years old. Force sensing insoles were used to measure normal foot-shoe forces, providing the standard for identification of gait events and measurement of kinetic waveforms. Three inertial measurement units (IMUs) were mounted to each participant, two bilaterally on the dorsal aspect of the foot and one clipped to the back of each participant's waistband, approximating their sacrum. Data input into the Long Short Term Memory network were from the three IMUs and output were estimated kinetic waveforms, compared against the standard of the force sensing insoles. The range of RMSE for each stance phase was from 0.189-0.288 BW, which is similar to multiple previous studies. Estimation of foot contact had an r 2 = 0.795. Estimation of kinetic variables varied, with peak force presenting the best output with an r 2 = 0.614. In conclusion, we have shown that at controlled paces over level ground a Long Short Term Memory network can estimate 4 s temporal windows of ground reaction force data across a range of running speeds.
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Affiliation(s)
- Seth R. Donahue
- Bowerman Sports Science Center, Department of Human Physiology, University of Oregon, Eugene, OR, United States
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Estimation of gait events and kinetic waveforms with wearable sensors and machine learning when running in an unconstrained environment. Sci Rep 2023; 13:2339. [PMID: 36759681 PMCID: PMC9911774 DOI: 10.1038/s41598-023-29314-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 02/02/2023] [Indexed: 02/11/2023] Open
Abstract
Wearable sensors and machine learning algorithms are becoming a viable alternative for biomechanical analysis outside of the laboratory. The purpose of this work was to estimate gait events from inertial measurement units (IMUs) and utilize machine learning for the estimation of ground reaction force (GRF) waveforms. Sixteen healthy runners were recruited for this study, with varied running experience. Force sensing insoles were used to measure normal foot-shoe forces, providing a proxy for vertical GRF and a standard for the identification of gait events. Three IMUs were mounted on each participant, two bilaterally on the dorsal aspect of each foot and one clipped to the back of each participant's waistband, approximating their sacrum. Participants also wore a GPS watch to record elevation and velocity. A Bidirectional Long Short Term Memory Network (BD-LSTM) was used to estimate GRF waveforms from inertial waveforms. Gait event estimation from both IMU data and machine learning algorithms led to accurate estimations of contact time. The GRF magnitudes were generally underestimated by the machine learning algorithm when presented with data from a novel participant, especially at faster running speeds. This work demonstrated that estimation of GRF waveforms is feasible across a range of running velocities and at different grades in an uncontrolled environment.
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14
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Patoz A, Lussiana T, Breine B, Gindre C, Malatesta D. Accurate estimation of peak vertical ground reaction force using the duty factor in level treadmill running. Scand J Med Sci Sports 2023; 33:169-177. [PMID: 36310520 DOI: 10.1111/sms.14252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/14/2022] [Accepted: 10/12/2022] [Indexed: 11/05/2022]
Abstract
This study aimed to (1) construct a statistical model (SMM) based on the duty factor (DF) to estimate the peak vertical ground reaction force ( F v , max ) and (2) to compare the estimated F v , max to force plate gold standard (GSM). One hundred and fifteen runners ran at 9, 11, and 13 km/h. Force (1000 Hz) and kinematic (200 Hz) data were acquired with an instrumented treadmill and an optoelectronic system, respectively, to assess force-plate and kinematic based DFs. SMM linearly relates F v , max to the inverse of DF because DF was analytically associated with the inverse of the average vertical force during ground contact time and the latter was very highly correlated to F v , max . No systematic bias and a 4% root mean square error (RMSE) were reported between GSM and SMM using force-plate based DF values when considering all running speeds together. Using kinematic based DF values, SMM reported a systematic but small bias (0.05BW) and a 5% RMSE when considering all running speeds together. These findings support the use of SMM to estimate F v , max during level treadmill runs at endurance speeds if underlying DF values are accurately measured.
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Affiliation(s)
- Aurélien Patoz
- Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland.,Research and Development Department, Volodalen Swiss Sport Lab, Aigle, Switzerland
| | - Thibault Lussiana
- Research and Development Department, Volodalen Swiss Sport Lab, Aigle, Switzerland.,Research and Development Department, Volodalen, Chavéria, France.,Research Unit EA3920 Prognostic Markers and Regulatory Factors of Cardiovascular Diseases and Exercise Performance, Health, Innovation Platform, University of Franche-Comté, Besançon, France
| | - Bastiaan Breine
- Research and Development Department, Volodalen Swiss Sport Lab, Aigle, Switzerland.,Department of Movement and Sports Sciences, Ghent University, Ghent, Belgium
| | - Cyrille Gindre
- Research and Development Department, Volodalen Swiss Sport Lab, Aigle, Switzerland.,Research and Development Department, Volodalen, Chavéria, France
| | - Davide Malatesta
- Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland
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15
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Patoz A, Lussiana T, Breine B, Gindre C, Malatesta D. Comparison of different machine learning models to enhance sacral acceleration-based estimations of running stride temporal variables and peak vertical ground reaction force. Sports Biomech 2023:1-17. [PMID: 36606626 DOI: 10.1080/14763141.2022.2159870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Machine learning (ML) was used to predict contact (tc) and flight (tf) time, duty factor (DF) and peak vertical force (Fv,max) from IMU-based estimations. One hundred runners ran on an instrumented treadmill (9-13 km/h) while wearing a sacral-mounted IMU. Linear regression (LR), support vector regression and two-layer neural-network were trained (80 participants) using IMU-based estimations, running speed, stride frequency and body mass. Predictions (remaining 20 participants) were compared to gold standard (kinetic data collected using the force plate) by calculating the mean absolute percentage error (MAPE). MAPEs of Fv,max did not significantly differ among its estimation and predictions (P = 0.37), while prediction MAPEs for tc, tf and DF were significantly smaller than corresponding estimation MAPEs (P ≤ 0.003). There were no significant differences among prediction MAPEs obtained from the three ML models (P ≥ 0.80). Errors of the ML models were equal to or smaller than (≤32%) the smallest real difference for the four variables, while errors of the estimations were not (15-45%), indicating that ML models were sufficiently accurate to detect a clinically important difference. The simplest ML model (LR) should be used to improve the accuracy of the IMU-based estimations. These improvements may be beneficial when monitoring running-related injury risk factors in real-world settings.
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Affiliation(s)
- Aurélien Patoz
- Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland.,Research and Development Department, Volodalen Swiss Sport Lab, Aigle, Switzerland
| | - Thibault Lussiana
- Research and Development Department, Volodalen Swiss Sport Lab, Aigle, Switzerland.,Research and Development Department, Chavéria, France.,Research Unit EA3920 Prognostic Markers and Regulatory Factors of Cardiovascular Diseases and Exercise Performance, Health, Innovation platform, University of Franche-Comté, Besançon, France
| | - Bastiaan Breine
- Research and Development Department, Volodalen Swiss Sport Lab, Aigle, Switzerland.,Department of Movement and Sports Sciences, Ghent University, Ghent, Belgium
| | - Cyrille Gindre
- Research and Development Department, Volodalen Swiss Sport Lab, Aigle, Switzerland.,Research and Development Department, Chavéria, France
| | - Davide Malatesta
- Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland
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16
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Meyer F, Lund-Hansen M, Seeberg TM, Kocbach J, Sandbakk Ø, Austeng A. Inner-Cycle Phases Can Be Estimated from a Single Inertial Sensor by Long Short-Term Memory Neural Network in Roller-Ski Skating. SENSORS (BASEL, SWITZERLAND) 2022; 22:9267. [PMID: 36501969 PMCID: PMC9739028 DOI: 10.3390/s22239267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/04/2022] [Accepted: 11/11/2022] [Indexed: 06/17/2023]
Abstract
OBJECTIVE The aim of this study was to provide a new machine learning method to determine temporal events and inner-cycle parameters (e.g., cycle, pole and ski contact and swing time) in cross-country roller-ski skating on the field, using a single inertial measurement unit (IMU). METHODS The developed method is based on long short-term memory neural networks to detect the initial and final contact of the poles and skis with the ground during the cyclic movements. Eleven athletes skied four laps of 2.5 km at a low and high intensity using skis with two different rolling coefficients. They were equipped with IMUs attached to the upper back, lower back and to the sternum. Data from force insoles and force poles were used as the reference system. RESULTS The IMU placed on the upper back provided the best results, as the LSTM network was able to determine the temporal events with a mean error ranging from -1 to 11 ms and had a standard deviation (SD) of the error between 64 and 70 ms. The corresponding inner-cycle parameters were calculated with a mean error ranging from -11 to 12 ms and an SD between 66 and 74 ms. The method detected 95% of the events for the poles and 87% of the events for the skis. CONCLUSION The proposed LSTM method provides a promising tool for assessing temporal events and inner-cycle phases in roller-ski skating, showing the potential of using a single IMU to estimate different spatiotemporal parameters of human locomotion.
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Affiliation(s)
- Frédéric Meyer
- Department of Informatics, University of Oslo, 0373 Oslo, Norway
| | - Magne Lund-Hansen
- Department of Physical Performance, Norwegian School of Sport Science, 0806 Oslo, Norway
| | - Trine M. Seeberg
- SINTEF Digital, Forskningsveien 1, 0373 Oslo, Norway
- Centre for Elite Sports Research, Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Jan Kocbach
- Centre for Elite Sports Research, Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Øyvind Sandbakk
- Centre for Elite Sports Research, Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Andreas Austeng
- Department of Informatics, University of Oslo, 0373 Oslo, Norway
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17
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A Single Sacral-Mounted Inertial Measurement Unit to Estimate Peak Vertical Ground Reaction Force, Contact Time, and Flight Time in Running. SENSORS 2022; 22:s22030784. [PMID: 35161530 PMCID: PMC8838733 DOI: 10.3390/s22030784] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/22/2021] [Accepted: 01/18/2022] [Indexed: 02/04/2023]
Abstract
Peak vertical ground reaction force (Fz,max), contact time (tc), and flight time (tf) are key variables of running biomechanics. The gold standard method (GSM) to measure these variables is a force plate. However, a force plate is not always at hand and not very portable overground. In such situation, the vertical acceleration signal recorded by an inertial measurement unit (IMU) might be used to estimate Fz,max, tc, and tf. Hence, the first purpose of this study was to propose a method that used data recorded by a single sacral-mounted IMU (IMU method: IMUM) to estimate Fz,max. The second aim of this study was to estimate tc and tf using the same IMU data. The vertical acceleration threshold of an already existing IMUM was modified to detect foot-strike and toe-off events instead of effective foot-strike and toe-off events. Thus, tc and tf estimations were obtained instead of effective contact and flight time estimations. One hundred runners ran at 9, 11, and 13 km/h. IMU data (208 Hz) and force data (200 Hz) were acquired by a sacral-mounted IMU and an instrumented treadmill, respectively. The errors obtained when comparing Fz,max, tc, and tf estimated using the IMUM to Fz,max, tc, and tf measured using the GSM were comparable to the errors obtained using previously published methods. In fact, a root mean square error (RMSE) of 0.15 BW (6%) was obtained for Fz,max while a RMSE of 20 ms was reported for both tc and tf (8% and 18%, respectively). Moreover, even though small systematic biases of 0.07 BW for Fz,max and 13 ms for tc and tf were reported, the RMSEs were smaller than the smallest real differences [Fz,max: 0.28 BW (11%), tc: 32.0 ms (13%), and tf: 32.0 ms (30%)], indicating no clinically important difference between the GSM and IMUM. Therefore, these results support the use of the IMUM to estimate Fz,max, tc, and tf for level treadmill runs at low running speeds, especially because an IMU has the advantage to be low-cost and portable and therefore seems very practical for coaches and healthcare professionals.
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Alcantara RS, Edwards WB, Millet GY, Grabowski AM. Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: a recurrent neural network solution. PeerJ 2022; 10:e12752. [PMID: 35036107 PMCID: PMC8740512 DOI: 10.7717/peerj.12752] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/15/2021] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Ground reaction forces (GRFs) are important for understanding human movement, but their measurement is generally limited to a laboratory environment. Previous studies have used neural networks to predict GRF waveforms during running from wearable device data, but these predictions are limited to the stance phase of level-ground running. A method of predicting the normal (perpendicular to running surface) GRF waveform using wearable devices across a range of running speeds and slopes could allow researchers and clinicians to predict kinetic and kinematic variables outside the laboratory environment. PURPOSE We sought to develop a recurrent neural network capable of predicting continuous normal (perpendicular to surface) GRFs across a range of running speeds and slopes from accelerometer data. METHODS Nineteen subjects ran on a force-measuring treadmill at five slopes (0°, ±5°, ±10°) and three speeds (2.5, 3.33, 4.17 m/s) per slope with sacral- and shoe-mounted accelerometers. We then trained a recurrent neural network to predict normal GRF waveforms frame-by-frame. The predicted versus measured GRF waveforms had an average ± SD RMSE of 0.16 ± 0.04 BW and relative RMSE of 6.4 ± 1.5% across all conditions and subjects. RESULTS The recurrent neural network predicted continuous normal GRF waveforms across a range of running speeds and slopes with greater accuracy than neural networks implemented in previous studies. This approach may facilitate predictions of biomechanical variables outside the laboratory in near real-time and improves the accuracy of quantifying and monitoring external forces experienced by the body when running.
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Affiliation(s)
- Ryan S. Alcantara
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, United States of America, Current affiliation: Department of Bioengineering, Stanford University, Stanford, CA, United States of America
| | - W. Brent Edwards
- Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada
| | - Guillaume Y. Millet
- Laboratoire Interuniversitaire de Biologie de la Motricité, Université Lyon, UJM-Saint-Etienne, Saint-Etienne, France
| | - Alena M. Grabowski
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, United States of America
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Patoz A, Lussiana T, Breine B, Gindre C, Malatesta D. A Multivariate Polynomial Regression to Reconstruct Ground Contact and Flight Times Based on a Sine Wave Model for Vertical Ground Reaction Force and Measured Effective Timings. Front Bioeng Biotechnol 2021; 9:687951. [PMID: 34805103 PMCID: PMC8599988 DOI: 10.3389/fbioe.2021.687951] [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: 03/30/2021] [Accepted: 09/29/2021] [Indexed: 11/13/2022] Open
Abstract
Effective contact (tce) and flight (tfe) times, instead of ground contact (tc) and flight (tf) times, are usually collected outside the laboratory using inertial sensors. Unfortunately, tce and tfe cannot be related to tc and tf because the exact shape of vertical ground reaction force is unknown. However, using a sine wave approximation for vertical force, tce and tc as well as tfe and tf could be related. Indeed, under this approximation, a transcendental equation was obtained and solved numerically over a tce x tfe grid. Then, a multivariate polynomial regression was applied to the numerical outcome. In order to reach a root-mean-square error of 0.5 ms, the final model was given by an eighth-order polynomial. As a direct application, this model was applied to experimentally measured tce values. Then, reconstructed tc (using the model) was compared to corresponding experimental ground truth. A systematic bias of 35 ms was depicted, demonstrating that ground truth tc values were larger than reconstructed ones. Nonetheless, error in the reconstruction of tc from tce was coming from the sine wave approximation, while the polynomial regression did not introduce further error. The presented model could be added to algorithms within sports watches to provide robust estimations of tc and tf in real time, which would allow coaches and practitioners to better evaluate running performance and to prevent running-related injuries.
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Affiliation(s)
- Aurélien Patoz
- Institute of Sport Sciences University of Lausanne, Lausanne, Switzerland.,Research and Development Department Volodalen Swiss Sport Lab, Aigle, Switzerland
| | - Thibault Lussiana
- Research and Development Department Volodalen Swiss Sport Lab, Aigle, Switzerland.,Research and Development Department Volodalen, Chavéria, France.,Research Unit EA3920 Prognostic Markers and Regulatory Factors of Cardiovascular Diseases and Exercise Performance Health Innovation Platform University of Franche-Comté, Besançon, France
| | - Bastiaan Breine
- Research and Development Department Volodalen Swiss Sport Lab, Aigle, Switzerland.,Department of Movement and Sports Sciences Ghent University, Ghent, Belgium
| | - Cyrille Gindre
- Research and Development Department Volodalen Swiss Sport Lab, Aigle, Switzerland.,Research and Development Department Volodalen, Chavéria, France
| | - Davide Malatesta
- Institute of Sport Sciences University of Lausanne, Lausanne, Switzerland
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Both a single sacral marker and the whole-body center of mass accurately estimate peak vertical ground reaction force in running. Gait Posture 2021; 89:186-192. [PMID: 34325223 DOI: 10.1016/j.gaitpost.2021.07.013] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 07/20/2021] [Accepted: 07/22/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND While running, the human body absorbs repetitive shocks with every step. These shocks can be quantified by the peak vertical ground reaction force (Fv,max). To measure so, using a force plate is the gold standard method (GSM), but not always at hand. In this case, a motion capture system might be an alternative if it accurately estimates Fv,max. RESEARCH QUESTION The purpose of this study was to estimate Fv,max based on motion capture data and validate the obtained estimates with force plate-based measures. METHODS One hundred and fifteen runners participated at this study and ran at 9, 11, and 13 km/h. Force data (1000 Hz) and whole-body kinematics (200 Hz) were acquired with an instrumented treadmill and an optoelectronic system, respectively. The vertical ground reaction force was reconstructed from either the whole-body center of mass (COM-M) or sacral marker (SACR-M) accelerations, calculated as the second derivative of their respective positions, and further low-pass filtered using several cutoff frequencies (2-20 Hz) and a fourth-order Butterworth filter. RESULTS The most accurate estimations of Fv,max were obtained using 5 and 4 Hz cutoff frequencies for the filtering of COM and sacral marker accelerations, respectively. GSM, COM-M, and SACR-M were not significantly different at 11 km/h but were at 9 and 13 km/h. The comparison between GSM and COM-M or SACR-M for each speed depicted root mean square error (RMSE) smaller or equal to 0.17BW (≤6.5 %) and no systematic bias at 11 km/h but small systematic biases at 9 and 13 km/h (≤0.09 BW). COM-M gave systematic biases three times smaller than SACR-M and two times smaller RMSE. SIGNIFICANCE The findings of this study support the use of either COM-M or SACR-M using data filtered at 5 and 4 Hz, respectively, to estimate Fv,max during level treadmill runs at endurance speeds.
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Patoz A, Lussiana T, Breine B, Gindre C, Malatesta D. Estimating effective contact and flight times using a sacral-mounted inertial measurement unit. J Biomech 2021; 127:110667. [PMID: 34365285 DOI: 10.1016/j.jbiomech.2021.110667] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/27/2021] [Accepted: 07/28/2021] [Indexed: 10/20/2022]
Abstract
Effective ground contact (tce) and flight (tfe) times were proven to be more appropriate to decipher the landing-take-off asymmetry of running than usual ground contact (tc) and flight (tf) times. To measure these effective timings, force plate is the gold standard method (GSM), though not very portable overground. In such situation, alternatives could be to use portable tools such as inertial measurement unit (IMU). Therefore, the purpose of this study was to propose a method that uses the vertical acceleration recorded using a sacral-mounted IMU to estimate tce and tfe and to compare these estimations to those from GSM. Besides, tce and tfe were used to evaluate the landing-take-off asymmetry, which was further compared to GSM. One hundred runners ran at 9, 11, and 13 km/h. Force data (200 Hz) and IMU data (208 Hz) were acquired by an instrumented treadmill and a sacral-mounted IMU, respectively. The comparison between GSM and IMU method depicted root mean square error ≤22 ms (≤14%) for tce and tfe along with small systematic biases (≤20 ms) for each tested speed. These errors are similar to previously published methods that estimated usual tc and tf. The systematic biases on tce and tfe were subtracted before calculating the landing-take-off asymmetry, which permitted to correctly evaluate it at a group level. Therefore, the findings of this study support the use of this method based on vertical acceleration recorded using a sacral-mounted IMU to estimate tce and tfe for level treadmill runs and to evaluate the landing-take-off asymmetry but only after subtraction of systematic biases and at a group level.
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Affiliation(s)
- Aurélien Patoz
- Institute of Sport Sciences, University of Lausanne, Lausanne 1015, Switzerland; Research and Development Department, Volodalen Swiss Sport Lab, Aigle 1860, Switzerland.
| | - Thibault Lussiana
- Research and Development Department, Volodalen Swiss Sport Lab, Aigle 1860, Switzerland; Research and Development Department, Volodalen, Chavéria 39270, France; Research Unit EA3920 Prognostic Markers and Regulatory Factors of Cardiovascular Diseases and Exercise Performance, Health, Innovation platform, University of Franche-Comté, Besançon, France
| | - Bastiaan Breine
- Research and Development Department, Volodalen Swiss Sport Lab, Aigle 1860, Switzerland; Department of Movement and Sports Sciences, Ghent University, Ghent 9000, Belgium
| | - Cyrille Gindre
- Research and Development Department, Volodalen Swiss Sport Lab, Aigle 1860, Switzerland; Research and Development Department, Volodalen, Chavéria 39270, France
| | - Davide Malatesta
- Institute of Sport Sciences, University of Lausanne, Lausanne 1015, Switzerland
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