Biyomekanikte Ölçüm ve Analiz Yöntemleri

Knudson D. Fundamentals of Biomechanics. New York: Springer Science; 2007.

Joseph Hamill KMK, Timothy R. Derrick. Linear Kinetics. In: Lupash E, editor. Biomechanical Basis of Human Movement. China: Lippincott Williams & Wilkins; 2015. p. 346–90.

Thompson BJ. It's time to re-evaluate the reporting of common measures from isokinetic dynamometers: isokinetic for torque, isotonic for power. Front Sports Act Living. 2025;7:1472712.doi:10.3389/fspor.2025.1472712

Grace TG. Muscle imbalance and extremity injury. A perplexing relationship. Sports Med. 1985;2(2):77–82.doi:10.2165/00007256-198502020-00001

Stauber WT, Barill ER, Stauber RE, et al. Isotonic dynamometry for the assessment of power and fatigue in the knee extensor muscles of females. Clin Physiol. 2000;20(3):225–33.doi:10.1046/j.1365-2281.2000.00250.x

Schieb D. The biomechanics piezoelectric force plate. Soma. 1987;14:35–40.

Winter D. BIOMECHANICS AND MOTOR CONTROL OF HUMAN MOVEMENT. Canada: John Wiley & Sons, Inc.; 2009.

Elftman HO. FORCES AND ENERGY CHANGES IN THE LEG DURING WALKING. American Journal of Physiology. 1939;125:339–56.

Gage WH, Winter DA, Frank JS, et al. Kinematic and kinetic validity of the inverted pendulum model in quiet standing. Gait Posture. 2004;19(2):124–32.doi:10.1016/s0966-6362(03)00037-7

Razak AH, Zayegh A, Begg RK, et al. Foot plantar pressure measurement system: a review. Sensors (Basel). 2012;12(7):9884–912.doi:10.3390/s120709884

Wade L, Needham L, McGuigan P, et al. Applications and limitations of current markerless motion capture methods for clinical gait biomechanics. PeerJ. 2022;10:e12995.doi:10.7717/peerj.12995

Lu TW, Chang CF. Biomechanics of human movement and its clinical applications. Kaohsiung J Med Sci. 2012;28(2 Suppl):S13–25.doi:10.1016/j.kjms.2011.08.004

Song K, Hullfish TJ, Scattone Silva R, et al. Markerless motion capture estimates of lower extremity kinematics and kinetics are comparable to marker-based across 8 movements. J Biomech. 2023;157:111751.doi:10.1016/j.jbiomech.2023.111751

Das K, de Paula Oliveira T, Newell J. Comparison of markerless and marker-based motion capture systems using 95% functional limits of agreement in a linear mixed-effects modelling framework. Scientific Reports. 2023;13(1):22880.doi:10.1038/s41598-023-49360-2

Turner JA, Chaaban CR, Padua DA. Validation of OpenCap: A low-cost markerless motion capture system for lower-extremity kinematics during return-to-sport tasks. J Biomech. 2024;171:112200.doi:10.1016/j.jbiomech.2024.112200

Carlo Dindorf EB, Freya Gassmann, Michael Fröhlich. Artificial Intelligence in Sports, Movement, and Health: Springer Cham; 2024.

Lee CJ, Lee JK. Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review. Sensors (Basel). 2022;22(7).doi:10.3390/s22072507

Souaifi M, Dhahbi W, Jebabli N, et al. Artificial Intelligence in Sports Biomechanics: A Scoping Review on Wearable Technology, Motion Analysis, and Injury Prevention. Bioengineering (Basel). 2025;12(8).doi:10.3390/bioengineering12080887

Prisco G, Pirozzi MA, Santone A, et al. Validity of Wearable Inertial Sensors for Gait Analysis: A Systematic Review. Diagnostics. 2025;15(1):36.

Ohmura T, Kojima S, Azuma T, et al. Assessing the Validity and Reliability of a Markerless Motion Capture System for Sagittal-Plane Gait Range of Motion. Cureus. 2025;17(12):e99875.doi:10.7759/cureus.99875

Song K, Hullfish TJ, Silva RS, et al. Markerless motion capture estimates of lower extremity kinematics and kinetics are comparable to marker-based across 8 movements. bioRxiv. 2023.doi:10.1101/2023.02.21.526496

Cereatti A, Gurchiek R, Mündermann A, et al. ISB recommendations on the definition, estimation, and reporting of joint kinematics in human motion analysis applications using wearable inertial measurement technology. J Biomech. 2024;173:112225.doi:10.1016/j.jbiomech.2024.112225

Scataglini S, Abts E, Van Bocxlaer C, et al. Accuracy, Validity, and Reliability of Markerless Camera-Based 3D Motion Capture Systems versus Marker-Based 3D Motion Capture Systems in Gait Analysis: A Systematic Review and Meta-Analysis. Sensors (Basel). 2024;24(11).doi:10.3390/s24113686

Flaxman TE, Alkjaer T, Smale KB, et al. Differences in EMG-moment relationships between ACL-injured and uninjured adults during a weight-bearing multidirectional force control task. J Orthop Res. 2019;37(1):113–23.doi:10.1002/jor.24145

Preston DC, Shapiro BE. Electromyography and neuromuscular disorders : clinical-electrophysiologic-ultrasound correlations. Fourth edition. ed. Philadelphia, PA: Elsevier; 2021.

Papagiannis GI, Triantafyllou AI, Roumpelakis IM, et al. Methodology of surface electromyography in gait analysis: review of the literature. J Med Eng Technol. 2019;43(1):59–65.doi:10.1080/03091902.2019.1609610

Hellig T, Rick V, Mertens A, et al. Investigation of observational methods assessing workload of static working postures based on surface electromyography. Work. 2019;62(2):185–95.doi:10.3233/wor-192854

Fernández-Lázaro D, Mielgo-Ayuso J, Adams DP, et al. Electromyography: A Simple and Accessible Tool to Assess Physical Performance and Health during Hypoxia Training. A Systematic Review. Sustainability. 2020;12(21):9137.

Fukuhara S, Kawashima T, Oka H. Indices reflecting muscle contraction performance during exercise based on a combined electromyography and mechanomyography approach. Sci Rep. 2021;11(1):21208.doi:10.1038/s41598-021-00671-2

Ficek K, Gołaś A, Pietraszewski P, et al. The Effects of a Combined Pre- and Post-Operative Anterior Cruciate Ligament Reconstruction Rehabilitation Program on Lower Extremity Muscle Imbalance. Applied Sciences. 2022;12(15):7411.

Lee K. EMG-Triggered Pedaling Training on Muscle Activation, Gait, and Motor Function for Stroke Patients. Brain Sci. 2022;12(1).doi:10.3390/brainsci12010076

Osborne JA, Botkin R, Colon-Semenza C, et al. Physical Therapist Management of Parkinson Disease: A Clinical Practice Guideline From the American Physical Therapy Association. Phys Ther. 2022;102(4).doi:10.1093/ptj/pzab302

Pilkar R, Momeni K, Ramanujam A, et al. Use of Surface EMG in Clinical Rehabilitation of Individuals With SCI: Barriers and Future Considerations. Front Neurol. 2020;11:578559.doi:10.3389/fneur.2020.578559

Campanini I, Disselhorst-Klug C, Rymer WZ, et al. Surface EMG in Clinical Assessment and Neurorehabilitation: Barriers Limiting Its Use. Front Neurol. 2020;11:934.doi:10.3389/fneur.2020.00934

Piyathilaka L, Sul J-H, Dunu Arachchige S, et al. Advances in EMG Signal Processing and Pattern Recognition: Techniques, Challenges, and Emerging Applications. Electronics. 2026;15(3):590.

Al-Ayyad M, Owida HA, De Fazio R, et al. Electromyography Monitoring Systems in Rehabilitation: A Review of Clinical Applications, Wearable Devices and Signal Acquisition Methodologies. Electronics. 2023;12(7):1520.

Karacan I, Türker KS. A comparison of electromyography techniques: surface versus intramuscular recording. Eur J Appl Physiol. 2025;125(1):7–23.doi:10.1007/s00421-024-05640-x

John V. Basmajian CJDL. Muscles Alive: Their Function Revealed by Electromyography. Baltimore: Williams & Wilkins; 1985.

Ho T-Y, Chen Y-J, Hung W-C, et al. The Design of EMG Measurement System for Arm Strength Training Machine. Mathematical Problems in Engineering. 2015;2015(1):356028.doi:https://doi.org/10.1155/2015/356028

Tankisi H, Burke D, Cui L, et al. Standards of instrumentation of EMG. Clin Neurophysiol. 2020;131(1):243–58.doi:10.1016/j.clinph.2019.07.025

Muceli S, Merletti R. Tutorial. Frequency analysis of the surface EMG signal: Best practices. J Electromyogr Kinesiol. 2024;79:102937.doi:10.1016/j.jelekin.2024.102937

Potvin JR, Brown SH. Less is more: high pass filtering, to remove up to 99% of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates. J Electromyogr Kinesiol. 2004;14(3):389–99.doi:10.1016/j.jelekin.2003.10.005

Shields JE, Hernandez-Sarabia JA, Lanza MB, et al. A Comparison of Popular Filters for the Electromyography Beginner. Measurement in Physical Education and Exercise Science. 2025:1–10.doi:10.1080/1091367X.2025.2589872

Merletti R. Standards for Reporting EMG Data. Journal of Electromyography and Kinesiology. 2000.

Arteaga MV, Castiblanco JC, Mondragon IF, et al. EMG-driven hand model based on the classification of individual finger movements. Biomedical Signal Processing and Control. 2020;58:101834.doi:https://doi.org/10.1016/j.bspc.2019.101834

Besomi M, Hodges PW, Clancy EA, et al. Consensus for experimental design in electromyography (CEDE) project: Amplitude normalization matrix. J Electromyogr Kinesiol. 2020;53:102438.doi:10.1016/j.jelekin.2020.102438

Konrad P. The abc of emg. A practical introduction to kinesiological electromyography. 2005;1.

The SENIAM Project [Available from: http://www.seniam.org/.

Delp SL, Anderson FC, Arnold AS, et al. OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans Biomed Eng. 2007;54(11):1940–50.doi:10.1109/tbme.2007.901024

Erdemir A, McLean S, Herzog W, et al. Model-based estimation of muscle forces exerted during movements. Clin Biomech (Bristol). 2007;22(2):131–54.doi:10.1016/j.clinbiomech.2006.09.005

Robertson D, Caldwell G, Hamill J, et al. Research Methods in Biomechanics: Human Kinetics; 2015.

Myers CA, Laz PJ, Shelburne KB, et al. A Probabilistic Approach to Quantify the Impact of Uncertainty Propagation in Musculoskeletal Simulations. Annals of Biomedical Engineering. 2015;43(5):1098–111.doi:10.1007/s10439-014-1181-7

Ancillao A, Aertbeliën E, De Schutter J. Effect of the soft tissue artifact on marker measurements and on the calculation of the helical axis of the knee during a squat movement: A study on the CAMS-Knee dataset. Med Eng Phys. 2022;110:103915.doi:10.1016/j.medengphy.2022.103915

Crowninshield RD, Brand RA. A physiologically based criterion of muscle force prediction in locomotion. J Biomech. 1981;14(11):793–801.doi:10.1016/0021-9290(81)90035-x

Rasmussen J. From Knowledge to Leverage: How to Use Musculoskeletal Simulation to Design Exoskeleton Concepts. Applied Sciences. 2025;15(11):5903.

Jianqiao -G, Yanbing -W, Qiang -T, et al. - Advances in flexible multibody dynamics of human musculoskeletal systems. - Advances in Mechanics. 2022;- 52(- 2):– 253.doi:- 10.6052/1000-0992-21-056

Xin H, Diao H. Computational biomechanical modelling of the spine, In: (ed.)^(eds.). p.

Lloyd DG, Besier TF. An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. J Biomech. 2003;36(6):765–76.doi:10.1016/s0021-9290(03)00010-1

Akbaş M, Akbulut MB, Belli S. Sonlu Elemanlar Stres Analizi ve Endodontide Kullanımı. European Journal of Research in Dentistry. 2021;5(2):99–108.doi:10.29228/erd.15

Luis I, Afschrift M, Gutierrez-Farewik EM. Springs vs. motors: Ideal assistance in the lower limbs during walking at different speeds. PLoS Comput Biol. 2024;20(9):e1011837.doi:10.1371/journal.pcbi.1011837

He C, Fu H, Ma CZ. Biomechanics and Motion Analysis: From Human Performance to Clinical Practice. Bioengineering (Basel). 2025;12(10).doi:10.3390/bioengineering12101114

Moreira L, Figueiredo J, Fonseca P, et al. Lower limb kinematic, kinetic, and EMG data from young healthy humans during walking at controlled speeds. Sci Data. 2021;8(1):103.doi:10.1038/s41597-021-00881-3

Hensley CP, Millican D, Hamilton N, et al. Video-Based Motion Analysis Use: A National Survey of Orthopedic Physical Therapists. Phys Ther. 2020;100(10):1759–70.doi:10.1093/ptj/pzaa125

Halmich C, Höschler L, Schranz C, et al. Data augmentation of time-series data in human movement biomechanics: A scoping review. PLoS One. 2025;20(7):e0327038.doi:10.1371/journal.pone.0327038

Wei W, Tan F, Zhang H, et al. Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition. Sci Data. 2023;10(1):358.doi:10.1038/s41597-023-02263-3

Crenna F, Rossi GB, Berardengo M. Filtering Biomechanical Signals in Movement Analysis. Sensors (Basel). 2021;21(13).doi:10.3390/s21134580

Williams JM, Frey M, Breen A, et al. Systematic analysis of different low-pass filter cut-off frequencies on lumbar spine kinematics data and the impact on the agreement between accelerometers and an optoelectronic system. J Biomech. 2022;145:111395.doi:10.1016/j.jbiomech.2022.111395

Baker R, McGinley JL, Schwartz MH, et al. The gait profile score and movement analysis profile. Gait Posture. 2009;30(3):265–9.doi:10.1016/j.gaitpost.2009.05.020

Hermens HJ, Freriks B, Disselhorst-Klug C, et al. Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol. 2000;10(5):361–74.doi:10.1016/s1050-6411(00)00027-4

Wonsetler EC, Bowden MG. A systematic review of mechanisms of gait speed change post-stroke. Part 2: exercise capacity, muscle activation, kinetics, and kinematics. Top Stroke Rehabil. 2017;24(5):394–403.doi:10.1080/10749357.2017.1282413

Allison K, Wrigley TV, Vicenzino B, et al. Kinematics and kinetics during walking in individuals with gluteal tendinopathy. Clin Biomech (Bristol). 2016;32:56–63.doi:10.1016/j.clinbiomech.2016.01.003

McManus L, De Vito G, Lowery MM. Analysis and Biophysics of Surface EMG for Physiotherapists and Kinesiologists: Toward a Common Language With Rehabilitation Engineers. Front Neurol. 2020;11:576729.doi:10.3389/fneur.2020.576729

Jover-Jorge N, González-Rojo P, Amaya-Valero JV, et al. Comparative analysis of spatiotemporal gait parameters in patients with distal femoral megaprosthesis and healthy subjects using an inertial measurement unit (IMU). Wearable Technol. 2025;6:e25.doi:10.1017/wtc.2025.10009

Esmaeilpour F, Letafatkar A, Karimi MT, et al. Comparative analysis of ground reaction forces and spatiotemporal gait parameters in older adults with sway-back posture and chronic low back pain: a cross-sectional study. BMC Sports Sci Med Rehabil. 2025;17(1):71.doi:10.1186/s13102-025-01126-1

Ayvat F, Doğan M, Ayvat E, et al. Reduced physical activity in multiple sclerosis: The role of gait parameters, gait variability parameters and self-reported measure of gait. Mult Scler Relat Disord. 2025;98:106452.doi:10.1016/j.msard.2025.106452

Skvortsov DV, Kaurkin SN, Grebenkina NV, et al. Typical Changes in Gait Biomechanics in Patients with Subacute Ischemic Stroke. Diagnostics (Basel). 2025;15(5).doi:10.3390/diagnostics15050511

Gaudette LW, Bradach MM, de Souza Junior JR, et al. Clinical Application of Gait Retraining in the Injured Runner. J Clin Med. 2022;11(21).doi:10.3390/jcm11216497