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Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics
Open accessAbstract
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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“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]
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“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]
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“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
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
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 279
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 270
Croce, U. D., Cappozzo, A., & Kerrigan, D. C. (1999) · Medical & Biological Engineering & Computing · doi:10.1007/bf02513282 · Cited by 260
‘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
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
Ferrari, A., Cutti, A. G., & Cappello, A. (2010) · Gait & Posture · doi:10.1016/j.gaitpost.2010.02.009 · Cited by 149
Novak, A. C., & Brouwer, B. J. (2010) · Gait & Posture · doi:10.1016/j.gaitpost.2010.09.024 · Cited by 140
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 118
Research works printed but not linked: 1
- Cloete T Scheffer C, 2010, EMBS: IEEE 32nd Annu. Int. Conf. on Engineering in Medicine and Biology Society, 5125
Cited by
Sorted by citations
Muro, A., García-Zapirain, B., & Zorrilla, A. M. (2014) · Sensors · doi:10.3390/s140203362 · Cited by 1,181
IMU-Based Joint Angle Measurement for Gait Analysis
Seel, T., Raisch, J., & Schauer, T. (2014) · Sensors · doi:10.3390/s140406891 · Cited by 871
Robert-Lachaine, X., Mecheri, H., Larue, C., & Plamondon, A. (2017) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-016-1537-2 · Cited by 329
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
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 344
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
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
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
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
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 147
Teufl, W., Miezal, M., Taetz, B., Fröhlich, M., & Bleser, G. (2019) · PLOS ONE · doi:10.1371/journal.pone.0213064 · Cited by 129
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
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
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
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
Teufl, W., Miezal, M., Taetz, B., Fröhlich, M., & Bleser, G. (2018) · Sensors · doi:10.3390/s18071980 · Cited by 111
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
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 111
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
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 109
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
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 110
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
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
Lin, M.-I. B., & Lin, K. (2016) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2016.00092 · Cited by 96
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 92
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
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 91
Teufl, W., Taetz, B., Miezal, M., Lorenz, M., Pietschmann, J., Jöllenbeck, T., Fröhlich, M., & Bleser, G. (2019) · Sensors · doi:10.3390/s19225006 · Cited by 87
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
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 78
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
Hood, S., Gabert, L., & Lenzi, T. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3152134 · Cited by 69
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
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
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
Das, R., Paul, S., Mourya, G. K., Kumar, N., & Hussain, M. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.859298 · Cited by 67
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
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
Lebel, K., Boissy, P., Nguyen, H., & Duval, C. (2017) · BioMedical Engineering OnLine · doi:10.1186/s12938-017-0347-6 · Cited by 65
Colim, A., Faria, C., Cunha, J., Oliveira, J., Sousa, N., & Rocha, L. A. (2021) · Safety · doi:10.3390/safety7010014 · Cited by 59
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
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
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
Fan, B., Li, Q., Tan, T., Kang, P., & Shull, P. B. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3137305 · Cited by 57
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
Tavassolian, M., Cuthbert, T. J., Napier, C., Peng, J., & Menon, C. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.201900165 · Cited by 57
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
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
Hindle, B. R., Keogh, J., & Lorimer, A. V. (2021) · Applied Bionics and Biomechanics · doi:10.1155/2021/6628320 · Cited by 63
Gholami, M., Rezaei, A., Cuthbert, T. J., Napier, C., & Menon, C. (2019) · Sensors · doi:10.3390/s19235325 · Cited by 51
Cudejko, T., Button, K., & Al-Amri, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-18845-x · Cited by 64
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 54
Dentamaro, V., Impedovo, D., & Pirlo, G. (2020) · IEEE Access · doi:10.1109/access.2020.3032202 · Cited by 47
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
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 45
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
Shuai, Z., Dong, A., Liu, H., & Cui, Y. (2022) · Sensors · doi:10.3390/s22030863 · Cited by 46
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 47
Ghai, S., Schmitz, G. H., Hwang, T., & Effenberg, A. O. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00142 · Cited by 46
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
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
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
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
Chen, H., Schall, M. C., & Fethke, N. B. (2020) · Applied Ergonomics · doi:10.1016/j.apergo.2020.103187 · Cited by 38
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
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
Cordillet, S., Bideau, N., Bideau, B., & Nicolas, G. (2019) · Sensors · doi:10.3390/s19112474 · Cited by 42
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
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
Bailey, C. A., Uchida, T. K., Nantel, J., & Graham, R. B. (2021) · Sensors · doi:10.3390/s21227690 · Cited by 35
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 35
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
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
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
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
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
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 36
Piche, É., Guilbot, M., Chorin, F., Guérin, O., Zory, R., & Gerus, P. (2022) · Measurement · doi:10.1016/j.measurement.2022.111442 · Cited by 32
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 31
Mihçin, Ş. (2022) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2021-0429 · Cited by 30
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 31
Kang, G. E., & Gross, M. M. (2016) · Measurement · doi:10.1016/j.measurement.2016.01.007 · Cited by 27
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
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 28
Ghaffari, A., Rahbek, O., Lauritsen, R. E. K., Kappel, A., Kold, S., & Rasmussen, J. (2022) · Sensors · doi:10.3390/s22145289 · Cited by 27
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
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 26
Carcreff, L., Grouvel, G., Payen, G., Massé, F., & Armand, S. (2022) · Sensors · doi:10.3390/s22155657 · Cited by 29
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
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
Training proprioception with sound: effects of real‐time auditory feedback on intermodal learning
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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 22
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
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Metadata
- 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 › Computer Science › Computer Graphics and Computer-Aided Design › 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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