Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics
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This study aims to validate a commercially available inertial sensor based motion capture system, Xsens MVN BIOMECH using its native protocols, against a camera-based motion capture system for the measurement of joint angular kinematics. Performance was evaluated by comparing waveform similarity using range of motion, mean error and a new formulation of the coefficient of multiple correlation (CMC). Three dimensional joint angles of the lower limbs were determined for ten healthy subjects while they performed three daily activities: level walking, stair ascent, and stair descent. Under all three walking conditions, the Xsens system most accurately determined the flexion/extension joint angle (CMC > 0.96) for all joints. The joint angle measurements associated with the other two joint axes had lower correlation including complex CMC values. The poor correlation in the other two joint axes is most likely due to differences in the anatomical frame definition of limb segments used by the Xsens and Optotrak systems. Implementation of a protocol to align these two systems is necessary when comparing joint angle waveforms measured by the Xsens and other motion capture systems.
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The estimated marker locations and subsequent model produced has previously been found to be a reliable and valid measure of joint kinematics during dynamic movements when in combination with a biomechanical model.
“However, the estimated marker locations and subsequent model produced has previously been found to be a reliable and valid measure of joint kinematics during dynamic movements when in combination with a biomechanical model [19,20,21].”
From The Effects of an Eight over Cricket Bowling Spell upon Pace Bowling Biomechanics and Performance within Different Delivery Lengths (2019) · cites this paper as [21]
The Introduction (page not provided) says Xsens uses a biomechanical model to estimate 3D joint angles and describes the system as reliable. The Results (page not provided) show close agreement with camera-based measurements for flexion/extension during walking and stair use, but substantially lower agreement for the other joint axes. The paper does not validate estimated marker locations, so it does not support the claim as stated for joint kinematics generally. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe XSENS motion analysis system has previously been found to be a reliable and valid means of assessing joint angles in dynamic movements.
“The XSENS motion analysis system has previously been found to be a reliable and valid means of assessing joint angles in dynamic movements [19,20,21].”
From The Effects of an Eight over Cricket Bowling Spell upon Pace Bowling Biomechanics and Performance within Different Delivery Lengths (2019) · cites this paper as [21]
The Introduction reports prior repeatability of Xsens joint-angle measurements during walking, and this study validates its flexion/extension measurements during walking and stair use. However, the Results and Discussion report substantially poorer agreement for joint angles on the other two axes. The claim is therefore too broad if it refers to joint angles generally across dynamic movements. (LLM, conf=0.88)
Experimental — accuracy not yet measuredKnee intra-rater reliability was fair to excellent and inter-rater reliability varied across movements (good for walking ICC 0.4 to 0.74 and poor for jumping ICC ≤0.39).
“Intra-rater reliability was fair to excellent and inter-rater reliability varied across movements performed (good for walking [ICC from 0.4 to 0.74] and poor for jumping [ICC ≤ 0.39] [45,53,59]).”
From Validity and Reliability of Wearable Sensors for Joint Angle Estimation: A Systematic Review (2019) · cites this paper as [59]
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Cites
(2009) · Cited by 701 · see citing works
Functional calibration procedure for 3D knee joint angle description using inertial sensors
Favre, J., Aïssaoui, R., Jolles, B. M., de Guise, J. A., & Aminian, K. (2009) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2009.06.025 · Cited by 302 · see citing works
Joint kinematics estimate using wearable inertial and magnetic sensing modules
Picerno, P., Cereatti, A., & Cappozzo, A. (2008) · Gait & Posture · doi:10.1016/j.gaitpost.2008.04.003 · Cited by 263 · see citing works
Development of a wearable sensor system for quantitative gait analysis
Liu, T., INOUE, Y., & SHIBATA, K. (2009) · Measurement · doi:10.1016/j.measurement.2009.02.002 · Cited by 262 · see citing works
Croce, U. D., Cappozzo, A., & Kerrigan, D. C. (1999) · Medical & Biological Engineering & Computing · doi:10.1007/bf02513282 · Cited by 260 · see citing works
‘Outwalk’: a protocol for clinical gait analysis based on inertial and magnetic sensors
Cutti, A. G., Ferrari, A., Garofalo, P., Raggi, M., Cappello, A., & Ferrari, A. (2009) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-009-0545-x · Cited by 218 · see citing works
Ferrari, A., Cutti, A. G., Garofalo, P., Raggi, M., Heijboer, M., Cappello, A., & Davalli, A. (2009) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-009-0544-y · Cited by 181 · see citing works
Ferrari, A., Cutti, A. G., & Cappello, A. (2010) · Gait & Posture · doi:10.1016/j.gaitpost.2010.02.009 · Cited by 149 · see citing works
Novak, A. C., & Brouwer, B. J. (2010) · Gait & Posture · doi:10.1016/j.gaitpost.2010.09.024 · Cited by 140 · see citing works
Self-citationHuman pose recovery using wireless inertial measurement units
Lin, J. F.-S., & Kulić, D. (2012) · Physiological Measurement · doi:10.1088/0967-3334/33/12/2099 · Cited by 112 · see citing works
Brennan, A. C., Zhang, J., Deluzio, K. J., & Li, Q. (2011) · Gait & Posture · doi:10.1016/j.gaitpost.2011.05.018 · Cited by 66 · see citing works
Self-citationCloete, T., & Scheffer, C. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5626196 · Cited by 51 · see citing works
Favre, J., Crevoisier, X. M., Jolles, B. M., & Aminian, K. (2010) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2010.03.041 · Cited by 37 · see citing works
Assessment of anatomical frame variation effect on joint angles: A linear perturbation approach
Brennan, A. C., Deluzio, K. J., & Li, Q. (2011) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2011.09.006 · Cited by 28 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Muro, A., García-Zapirain, B., & Zorrilla, A. M. (2014) · Sensors · doi:10.3390/s140203362 · Cited by 1,181 · see citing works
IMU-Based Joint Angle Measurement for Gait Analysis
Seel, T., Raisch, J., & Schauer, T. (2014) · Sensors · doi:10.3390/s140406891 · Cited by 871 · see citing works
Robert-Lachaine, X., Mecheri, H., Larue, C., & Plamondon, A. (2017) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-016-1537-2 · Cited by 329 · see citing works
Inertial Measurement Units for Clinical Movement Analysis: Reliability and Concurrent Validity
Al-Amri, M., Nicholas, K., Button, K., Sparkes, V., Sheeran, L., & Davies, J. (2018) · Sensors · doi:10.3390/s18030719 · Cited by 318 · see citing works
Kobsar, D., Charlton, J. M., Tse, C. T. F., Esculier, J.-F., Graffos, A., Krowchuk, N. M., Thatcher, D., & Hunt, M. A. (2020) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-020-00685-3 · Cited by 324 · see citing works
Validity and Reliability of Wearable Sensors for Joint Angle Estimation: A Systematic Review
Poitras, I., Dupuis, F., Bielmann, M., Campeau-Lecours, A., Mercier, C., Bouyer, L., & Roy, J.-S. (2019) · Sensors · doi:10.3390/s19071555 · Cited by 309 · see citing works
Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture
Karatsidis, A., Bellusci, G., Schepers, H. T. A. M., de Zee, M., Andersen, M. S., & Veltink, P. H. (2016) · Sensors · doi:10.3390/s17010075 · Cited by 288 · see citing works
Bolink, S. A. A. N., Naisas, H., Senden, R., Essers, H., Heyligers, I. C., Meijer, K., & Grimm, B. (2016) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2015.11.009 · Cited by 147 · see citing works
Monitoring gait in multiple sclerosis with novel wearable motion sensors
Moon, Y., McGinnis, R. S., Seagers, K. A., Motl, R. W., Sheth, N., Wright, J. A., Ghaffari, R., & Sosnoff, J. J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171346 · Cited by 134 · see citing works
An Open-Source and Wearable System for Measuring 3D Human Motion in Real-Time
Slade, P., Habib, A., Hicks, J. L., & Delp, S. L. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3103201 · Cited by 134 · see citing works
Karatsidis, A., Jung, M., Schepers, H. M., Bellusci, G., de Zee, M., Veltink, P. H., & Andersen, M. S. (2019) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2018.12.021 · Cited by 129 · see citing works
Teufl, W., Miezal, M., Taetz, B., Fröhlich, M., & Bleser, G. (2019) · PLOS ONE · doi:10.1371/journal.pone.0213064 · Cited by 129 · see citing works
Blair, S., Duthie, G., Robertson, S., Hopkins, W., & Ball, K. (2018) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2018.03.031 · Cited by 117 · see citing works
Alignment-Free, Self-Calibrating Elbow Angles Measurement Using Inertial Sensors
Müller, P., Bégin, M.-A., Schauer, T., & Seel, T. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2639537 · Cited by 114 · see citing works
Self-Aligning Mechanism Improves Comfort and Performance With a Powered Knee Exoskeleton
Sarkisian, S. V., Ishmael, M. K., & Lenzi, T. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3064463 · Cited by 113 · see citing works
Teufl, W., Miezal, M., Taetz, B., Fröhlich, M., & Bleser, G. (2018) · Sensors · doi:10.3390/s18071980 · Cited by 105 · see citing works
Validation of a 3D Markerless System for Gait Analysis Based on OpenPose and Two RGB Webcams
D’Antonio, E., Taborri, J., Mileti, I., Rossi, S., & Patanè, F. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3081188 · Cited by 103 · see citing works
TENG-Boosted Smart Sports with Energy Autonomy and Digital Intelligence
Wang, Y., Gao, Z., Wu, W., Xiong, Y., Luo, J., Sun, Q., Mao, Y., & Wang, Z. L. (2025) · Nano-Micro Letters · doi:10.1007/s40820-025-01778-1 · Cited by 108 · see citing works
Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 1: Robustness
Pagnon, D., Domalain, M. F., & Revéret, L. (2021) · Sensors · doi:10.3390/s21196530 · Cited by 100 · see citing works
Determining anatomical frames via inertial motion capture: A survey of methods
Vitali, R. V., & Perkins, N. C. (2020) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2020.109832 · Cited by 100 · see citing works
GAIT ANALYSIS: SYSTEMS, TECHNOLOGIES, AND IMPORTANCE
Akhtaruzzaman, M., Shafie, A. A., & Khan, M. R. (2016) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519416300039 · Cited by 97 · see citing works
Nijmeijer, E. M., Heuvelmans, P., Bolt, R., Gokeler, A., Otten, E., & Benjaminse, A. (2023) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2023.111637 · Cited by 101 · see citing works
Wearable Goniometer and Accelerometer Sensory Fusion for Knee Joint Angle Measurement in Daily Life
Tognetti, A., Lorussi, F., Carbonaro, N., & Rossi, D. D. (2015) · Sensors · doi:10.3390/s151128435 · Cited by 95 · see citing works
Data set for fall events and daily activities from inertial sensors
Ojetola, O., Gaura, E. I., & Brusey, J. (2015) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/2713168.2713198 · Cited by 94 · see citing works
Lin, M.-I. B., & Lin, K. (2016) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2016.00092 · Cited by 90 · see citing works
Validation of an IMU Suit for Military-Based Tasks
Mavor, M. P., Ross, G. B., Clouthier, A. L., Karakolis, T., & Graham, R. B. (2020) · Sensors · doi:10.3390/s20154280 · Cited by 86 · see citing works
A Novel Biomechanical Model-Aided IMU/UWB Fusion for Magnetometer-Free Lower Body Motion Capture
Zihajehzadeh, S., & Park, E. J. (2016) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2016.2521823 · Cited by 82 · see citing works
Paolo, S. D., Lopomo, N. F., Villa, F. D., Paolini, G., Figari, G., Bragonzoni, L., Grassi, A., & Zaffagnini, S. (2021) · Sensors · doi:10.3390/s21072331 · Cited by 85 · see citing works
Teufl, W., Taetz, B., Miezal, M., Lorenz, M., Pietschmann, J., Jöllenbeck, T., Fröhlich, M., & Bleser, G. (2019) · Sensors · doi:10.3390/s19225006 · Cited by 82 · see citing works
Merino, G. S. A. D., da Silva, L., de Mattos, D. L., Guimarães, B. M. D., & Merino, E. A. D. (2018) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2018.10.004 · Cited by 75 · see citing works
Validity of the Perception Neuron inertial motion capture system for upper body motion analysis
Sers, R., Forrester, S., Moss, E. L., Ward, S., Ma, J., & Zecca, M. (2019) · Measurement · doi:10.1016/j.measurement.2019.107024 · Cited by 75 · see citing works
UWB-Aided Inertial Motion Capture for Lower Body 3-D Dynamic Activity and Trajectory Tracking
Zihajehzadeh, S., Yoon, P. K., Kang, B.-S., & Park, E. J. (2015) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2015.2459532 · Cited by 70 · see citing works
Hood, S., Gabert, L., & Lenzi, T. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3152134 · Cited by 69 · see citing works
Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 2: Accuracy
Pagnon, D., Domalain, M. F., & Revéret, L. (2022) · Sensors · doi:10.3390/s22072712 · Cited by 72 · see citing works
Powered hip exoskeleton improves walking economy in individuals with above-knee amputation
Ishmael, M. K., Archangeli, D., & Lenzi, T. (2021) · Nature Medicine · doi:10.1038/s41591-021-01515-2 · Cited by 67 · see citing works
A Compact, Lightweight Robotic Ankle-Foot Prosthesis: Featuring a Powered Polycentric Design
Gabert, L., Hood, S., Tran, M., Cempini, M., & Lenzi, T. (2020) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2019.2955740 · Cited by 66 · see citing works
Das, R., Paul, S., Mourya, G. K., Kumar, N., & Hussain, M. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.859298 · Cited by 67 · see citing works
Cain, S. M., McGinnis, R. S., Davidson, S. P., Vitali, R. V., Perkins, N. C., & McLean, S. G. (2015) · Gait & Posture · doi:10.1016/j.gaitpost.2015.10.022 · Cited by 64 · see citing works
Towards Self-Calibrating Inertial Body Motion CapturePreprint
Taetz, B., Bleser, G., & Miezal, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.03754 · Cited by 64 · see citing works
Lebel, K., Boissy, P., Nguyen, H., & Duval, C. (2017) · BioMedical Engineering OnLine · doi:10.1186/s12938-017-0347-6 · Cited by 65 · see citing works
Colim, A., Faria, C., Cunha, J., Oliveira, J., Sousa, N., & Rocha, L. A. (2021) · Safety · doi:10.3390/safety7010014 · Cited by 59 · see citing works
Noninvasive spinal stimulation safely enables upright posture in children with spinal cord injury
Keller, A., Singh, G., Sommerfeld, J. H., King, M., Parikh, P., Ugiliweneza, B., D’Amico, J. M., Gerasimenko, Y., & Behrman, A. L. (2021) · Nature Communications · doi:10.1038/s41467-021-26026-z · Cited by 56 · see citing works
Kuber, P. M., Abdollahi, M., Alemi, M. M., & Rashedi, E. (2022) · Annals of Biomedical Engineering · doi:10.1007/s10439-022-03003-1 · Cited by 59 · see citing works
Ye, M., Yang, C., Stanković, V., Stanković, L., & Kerr, A. (2016) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2016.2559446 · Cited by 59 · see citing works
Fan, B., Li, Q., Tan, T., Kang, P., & Shull, P. B. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3137305 · Cited by 57 · see citing works
Self-citationEstimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach?
Wouda, F. J., Matteo Giuberti, A., Bellusci, G., & Veltink, P. H. (2016) · Sensors · doi:10.3390/s16122138 · Cited by 56 · see citing works
Tavassolian, M., Cuthbert, T. J., Napier, C., Peng, J., & Menon, C. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.201900165 · Cited by 55 · see citing works
Accuracy and Acceptability of Wearable Motion Tracking for Inpatient Monitoring Using Smartwatches
Auepanwiriyakul, C., Waibel, S., Songa, J., Bentley, P., & Faisal, A. A. (2020) · Sensors · doi:10.3390/s20247313 · Cited by 56 · see citing works
van der Straaten, R., Baets, L. D., Jonkers, I., & Timmermans, A. A. A. (2017) · Gait & Posture · doi:10.1016/j.gaitpost.2017.10.005 · Cited by 54 · see citing works
Hindle, B. R., Keogh, J., & Lorimer, A. V. (2021) · Applied Bionics and Biomechanics · doi:10.1155/2021/6628320 · Cited by 57 · see citing works
Gholami, M., Rezaei, A., Cuthbert, T. J., Napier, C., & Menon, C. (2019) · Sensors · doi:10.3390/s19235325 · Cited by 51 · see citing works
Cudejko, T., Button, K., & Al-Amri, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-18845-x · Cited by 60 · see citing works
Doğan, M., Koçak, M., Kılınç, Ö. O., Ayvat, F., Sütçü, G., Ayvat, E., Kılınç, M., Ünver, Ö., & Yıldırım, S. A. (2019) · Gait & Posture · doi:10.1016/j.gaitpost.2019.02.024 · Cited by 48 · see citing works
Dentamaro, V., Impedovo, D., & Pirlo, G. (2020) · IEEE Access · doi:10.1109/access.2020.3032202 · Cited by 47 · see citing works
Accuracy of Base of Support Using an Inertial Sensor Based Motion Capture System
Guo, L., & Xiong, S. (2017) · Sensors · doi:10.3390/s17092091 · Cited by 46 · see citing works
Soft robotic exosuit augmented high intensity gait training on stroke survivors: a pilot study
Shin, S. Y., Hohl, K., Giffhorn, M., Awad, L. N., Walsh, C. J., & Jayaraman, A. (2022) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-022-01034-2 · Cited by 44 · see citing works
Assessment of the measurement accuracy of inertial sensors during different tasks of daily living
Mundt, M., Thomsen, W., David, S., Dupré, T., Bamer, F., Potthast, W., & Markert, B. A. (2018) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2018.12.023 · Cited by 45 · see citing works
Shuai, Z., Dong, A., Liu, H., & Cui, Y. (2022) · Sensors · doi:10.3390/s22030863 · Cited by 43 · see citing works
Reliability of Xsens IMU-Based Lower Extremity Joint Angles during In-Field Running
Debertin, D., Wargel, A., & Mohr, M. (2024) · Sensors · doi:10.3390/s24030871 · Cited by 45 · see citing works
Ghai, S., Schmitz, G. H., Hwang, T., & Effenberg, A. O. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00142 · Cited by 41 · see citing works
Inertial Sensor-Based Motion Tracking in Football with Movement Intensity Quantification
Wilmes, E., de Ruiter, C. J., Bastiaansen, B. J. C., van Zon, J. F. J. A., Vegter, R. J. K., Brink, M. S., Goedhart, E. A., LEMMINK, K. A. P. M., & Savelsbergh, G. J. P. (2020) · Sensors · doi:10.3390/s20092527 · Cited by 41 · see citing works
Grading the Functional Movement Screen
Whiteside, D., Zendler, J. D., Pohorence, M. A., Sandoval, B., Russell, J. R., McLean, S. G., Zernicke, R. F., & Goulet, G. C. (2014) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0000000000000654 · Cited by 40 · see citing works
Miezal, M., Taetz, B., & Bleser, G. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989371 · Cited by 39 · see citing works
Holzgreve, F., Fraeulin, L., Betz, W., Erbe, C., Wanke, E. M., Brüggmann, D., Nienhaus, A., Groneberg, D. A., Maurer, C., & Ohlendorf, D. (2022) · Sensors · doi:10.3390/s22030805 · Cited by 39 · see citing works
Chen, H., Schall, M. C., & Fethke, N. B. (2020) · Applied Ergonomics · doi:10.1016/j.apergo.2020.103187 · Cited by 38 · see citing works
Carbonaro, N., Mascherini, G., Bartolini, I., Ringressi, M. N., Taddei, A., Tognetti, A., & Vanello, N. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18073734 · Cited by 38 · see citing works
The Waist Width of Skis Influences the Kinematics of the Knee Joint in Alpine Skiing.
Zorko, M., Nemec, B., Babič, J., Lešnik, B., & Supej, M. (2015) · Europe PMC (PubMed Central) · Cited by 37 · see citing works
Cordillet, S., Bideau, N., Bideau, B., & Nicolas, G. (2019) · Sensors · doi:10.3390/s19112474 · Cited by 37 · see citing works
Robert‐Lachaine, X., Parent, G., Fuentes, A., Hagemeister, N., & Aïssaoui, R. (2020) · Gait & Posture · doi:10.1016/j.gaitpost.2020.01.029 · Cited by 36 · see citing works
Measurement of Ankle Joint Movements Using IMUs during Running
Kim, B. H., Hong, S. H., Oh, I., Lee, Y. W., Kee, R. I., & Lee, S. Y. (2021) · Sensors · doi:10.3390/s21124240 · Cited by 33 · see citing works
Bailey, C. A., Uchida, T. K., Nantel, J., & Graham, R. B. (2021) · Sensors · doi:10.3390/s21227690 · Cited by 32 · see citing works
Validation of a Device to Measure Knee Joint Angles for a Dynamic Movement
Ajdaroski, M., Tadakala, R., Nichols, L., & Esquivel, A. O. (2020) · Sensors · doi:10.3390/s20061747 · Cited by 33 · see citing works
Closing the Wearable Gap: Mobile Systems for Kinematic Signal Monitoring of the Foot and Ankle
Luczak, T., Saucier, D. N., Burch, R. F. V., Ball, J. E., Chander, H., Knight, A. C., Wei, P., & Iftekhar, A. T. (2018) · Electronics · doi:10.3390/electronics7070117 · Cited by 32 · see citing works
Inertial Motion Capture-Based Whole-Body Inverse Dynamics
Diraneyya, M. M., Ryu, J., Abdel-Rahman, E. M., & Haas, C. T. (2021) · Sensors · doi:10.3390/s21217353 · Cited by 32 · see citing works
Zhang, S., McCullagh, P. J., Zheng, H., & Nugent, C. (2017) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2017.2693238 · Cited by 31 · see citing works
Fleron, M. K., Ubbesen, N. C. H., Battistella, F., Dejtiar, D. L., & Oliveira, A. S. (2018) · Sports Biomechanics · doi:10.1080/14763141.2017.1409259 · Cited by 31 · see citing works
Concurrent validity of an inertial measurement system in tennis forehand drive
Pedro, B., Cabral, S., & Veloso, A. (2021) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2021.110410 · Cited by 34 · see citing works
Fraeulin, L., Holzgreve, F., Brinkbäumer, M., Dziuba, A., Friebe, D., Klemz, S., Schmitt, M., Anna-Lena Theis, A., Tenberg, S., van Mark, A., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0243646 · Cited by 32 · see citing works
Piche, É., Guilbot, M., Chorin, F., Guérin, O., Zory, R., & Gerus, P. (2022) · Measurement · doi:10.1016/j.measurement.2022.111442 · Cited by 29 · see citing works
Figueiredo, J., Carvalho, S. P., Vilas‐Boas, J. P., Gonçalves, L. M., Moreno, J. C., & Santos, C. P. (2020) · Sensors · doi:10.3390/s20082185 · Cited by 27 · see citing works
Mihçin, Ş. (2022) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2021-0429 · Cited by 27 · see citing works
A wearable real-time kinetic measurement sensor setup for human locomotion
Wang, H., Basu, A., Durandau, G., & Sartori, M. (2023) · Wearable Technologies · doi:10.1017/wtc.2023.7 · Cited by 29 · see citing works
Kang, G. E., & Gross, M. M. (2016) · Measurement · doi:10.1016/j.measurement.2016.01.007 · Cited by 27 · see citing works
Lundgren, L. E., Tran, T. T., Nimphius, S., Raymond, E., Secomb, J. L., Farley, O. R. L., Newton, R. U., & Sheppard, J. M. (2015) · Journal of Sports Sciences · doi:10.1080/02640414.2015.1088164 · Cited by 26 · see citing works
McGinnis, R. S., Cain, S. M., Tao, S., Whiteside, D., Goulet, G. C., Gardner, E. C., Bedi, A., & Perkins, N. C. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2392758 · Cited by 26 · see citing works
Ghaffari, A., Rahbek, O., Lauritsen, R. E. K., Kappel, A., Kold, S., & Rasmussen, J. (2022) · Sensors · doi:10.3390/s22145289 · Cited by 26 · see citing works
van der Straaten, R., Wesseling, M., Jonkers, I., Vanwanseele, B., Bruijnes, A. K. B. D., Malcorps, J., Bellemans, J., Truijen, J., Baets, L. D., & Timmermans, A. A. A. (2020) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-020-00694-2 · Cited by 26 · see citing works
Chow, D. H. K., Tremblay, L., Lam, C. Y., Yeung, A. W. Y., Cheng, W. H. W., & Tse, P. T. W. (2021) · Sensors · doi:10.3390/s21144633 · Cited by 25 · see citing works
Carcreff, L., Grouvel, G., Payen, G., Massé, F., & Armand, S. (2022) · Sensors · doi:10.3390/s22155657 · Cited by 23 · see citing works
An Ultrasonic Six Degrees-of-Freedom Pose Estimation Sensor
Laurijssen, D., Truijen, S., Saeys, W., Daems, W., & Steckel, J. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2618399 · Cited by 25 · see citing works
Predicting kinetics using musculoskeletal modeling and inertial motion capturePreprint
Karatsidis, A., Jung, M., Schepers, H. M., Bellusci, G., de Zee, M., Veltink, P. H., & Andersen, M. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.01668 · Cited by 25 · see citing works
Training proprioception with sound: effects of real‐time auditory feedback on intermodal learning
Ghai, S., Schmitz, G. H., Hwang, T., & Effenberg, A. O. (2018) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13967 · Cited by 24 · see citing works
Distinct Feature Extraction for Video-Based Gait Phase Classification
Ye, M., Yang, C., Stanković, V., Stanković, L., & Cheng, S. S. M. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2942479 · Cited by 24 · see citing works
Su, S., Zhou, Y., Wang, Z., & Chen, H. (2017) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2016.2643626 · Cited by 23 · see citing works
Validation of temporal gait metrics from three IMU locations to the gold standard force plate
Patterson, M., Johnston, W., O’Mahony, N., O'Mahony, S., Nolan, E., & Caulfield, B. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7590790 · Cited by 22 · see citing works
Alemayoh, T. T., Lee, J. H., & Okamoto, S. (2023) · Applied Sciences · doi:10.3390/app13084794 · Cited by 21 · see citing works
Changes in Muscle Activity Patterns and Joint Kinematics During Gait in Hemophilic Arthropathy
Cruz‐Montecinos, C., Pérez‐Alenda, S., Querol, F., Cerda, M., & Maas, H. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2019.01575 · Cited by 21 · see citing works
Potter, M. V., Cain, S. M., Ojeda, L., Gurchiek, R. D., McGinnis, R. S., & Perkins, N. C. (2022) · Sensors · doi:10.3390/s22218398 · Cited by 20 · see citing works
Shin, S. Y., Lee, R. K., Spicer, P., & Sulzer, J. S. (2020) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-020-0655-0 · Cited by 20 · see citing works
A kinematic comparison of on‐ergometer and on‐water kayaking
Klitgaard, K., Andersen, C. H., Oliveira, A. S., & Heinen, F. (2020) · European Journal of Sport Science · doi:10.1080/17461391.2020.1831617 · Cited by 20 · see citing works
- Authors
- Zhang, J.-T., Novak, A. C., Brouwer, B., & Li, Q.
- Affiliations
- Toronto Rehabilitation Institute (CA)
- Queen's University (CA)
- Corresponding author
- Li, Qingguo
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2013-07-26
- Volume
- 34
- Issue
- 8
- Pages
- N63-N69
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W2036123359
- Cited by
- 314
- References
- 14
- Citations by year
- 2026: 14 · 2025: 24 · 2024: 25 · 2023: 30 · 2022: 37 · 2021: 41 · 2020: 36 · 2019: 22 · 2018: 24 · 2017: 21 · 2016: 21 · 2015: 14 · 2014: 5
- FWCI
- 7.35
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Physiological Measurement (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Lower Extremity Biomechanics and Pathologies
- Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- inertial sensors, lower limb joint kinematics, joint angle measurement, motion capture validation, camera-based motion capture, walking, stair ascent and descent
- MeSH terms
- Biomechanical Phenomena, Female, Humans, Joints, Male, Reproducibility of Results, Range of Motion, Articular, Monitoring, Ambulatory, Lower Extremity, Young Adult
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