McMaster University will continue to host undergraduate academic activities remotely for the Spring/Summer/Intersession term with only a few exceptions for courses that need student access to specialized equipment.
My research is aimed at making biomechanics and human movement analyses more accessible and real-world relevant. Specifically, this work focuses on the use of wearable inertial sensors to help track and treat musculoskeletal disorders or injuries. When we think of wearable sensors, we often think of fitness tracking watches, but in fact wearable sensors have the capabilities to collect a variety of detailed human movement data similar to conventional motion capture gait laboratories. Further, when we combine these devices with innovative analyses (e.g., machine learning algorithms) and visualization techniques, a whole new world of possibilities for assessing human movement is realized. To this point, my research has used these accessible devices and innovative techniques to uncover patterns in human movement that can help us better understand the progression and treatment of individuals with knee osteoarthritis and running injuries.
Kobsar, D., Charlton, J., Hunt, M. A. (2019). Individuals with knee osteoarthritis present increased gait pattern deviations as measured by a knee-specific Gait Deviation Index. Gait & Posture. 72, 82-88.
Kobsar, D., Osis, S. T., Jacob, C., & Ferber, R. (2019). Validity of a novel method to measure vertical oscillation during running using a depth camera. Journal of Biomechanics. 85, 182-186.
Ahamed, N. U., Kobsar, D., Benson, L., Clermont, C., Osis, S. T., & Ferber, R. (2019). Subject-specific and group-based running pattern classification using a single wearable sensor. Journal of Biomechanics. 84, 227-233
Benson, L., Ahamed, N. U., Kobsar, D., Ferber, R. (2019). New considerations for collecting biomechanical data using wearable sensors: Number of level runs to define a stable running pattern with a single IMU. Journal of Biomechanics. 85, 187-192.
Ahamed, N. U., Kobsar, D., Benson, L., Clermont, C., Kohrs, R., Osis, S. T., & Ferber, R. (2018). Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions. PLOS ONE, 13(9):e0203839.
Kobsar, D., & Ferber, R. (2018). Wearable sensor data to track subject-specific movement patterns related to clinical outcomes using a machine learning approach. Sensors. 18(9), 2828.
Clermont, C. A., Benson, L. C., Osis, S. T., Kobsar, D., & Ferber, R. (2018). Running patterns for male and female competitive and recreational runners based on accelerometer data. Journal of Sport Sciences. 19, 1-8.
Benson, L. C., Clermont, C. A., Osis, S. T., Kobsar, D., Ferber, R. (2018). Classifying running speed conditions using a single wearable sensor: Optimal segmentation and feature extraction methods. Journal of Biomechanics. 71, 94-99.
Kobsar, D., Osis, S. T., Boyd, J., Hettinga, B. A., & Ferber, R. (2017). Wearable sensors to predict response to a hip strengthening exercise intervention in patients with knee osteoarthritis. Journal of Neuroengineering and Rehabilitation. 25: S23-S24.
Osis, S. T., Kobsar, D., Leigh, R., Macaulay, C. & Ferber, R. (2017). An expert system feedback tool improves the reliability of clinical gait kinematics for older adults with lower limb osteoarthritis. Gait & Posture. 58, 261-267.</p>
Kobsar, D., Osis, S. T., Phinyomark, A., Boyd, J., & Ferber, R. (2016). Reliability of gait analysis using wearable sensors in a clinical population. Journal of Biomechanics, 49(16), 3977-3982.
Barden, J. M., Clermont, C., Kobsar, D. Beauchet, O. (2016). Acceleration-based step regularity is lower in older adults with bilateral knee osteoarthritis. Frontiers in Human Neuroscience, 10, 1-9.
Park, S-K., Kobsar, D., & Ferber, R. (2016). Relationship between lower limb muscle strength, self-reported pain and function, and frontal plane gait kinematics in knee osteoarthritis. Clinical Biomechanics, 38, 68-74.
Watari, R., Kobsar, D., Phinyomark, A., Osis, S., & Ferber, R. (2016). Determination of patellofemoral pain sub-groups and predicting treatment outcome using running gait kinematics. Clinical Biomechanics, 38, 13-21.
Phinyomark, A., Osis, S. T., Hettinga, B. A., Kobsar, D., & Ferber, R. (2016). Gender differences in gait kinematics for patients with knee osteoarthritis. BMC Musculoskeletal Disorders, 17, 157.
Kobsar, D., Osis, S. T., Hettinga, B. A., & Ferber, R. (2015). Gait biomechanics and patient-reported function as predictors of response to a hip strengthening exercise intervention in patients with knee osteoarthritis. PLOS ONE, 10(10):e0139923.
Kobsar, D., Osis, S. T., Hettinga, B. A., & Ferber, R. (2014). Classification accuracy of a single tri-axial accelerometer for training background and experience. Journal of Biomechanics, 47(10), 2508-2511.
Kobsar, D., Olson, C., Paranjape, R., Hadjistravopoulos, T., & Barden, J. M. (2014). Evaluation of age-related differences in stride-to-stride fluctuations, regularity and symmetry of gait using a waist-mounted tri-axial accelerometer. Gait & Posture, 39(1), 553-557.
Kobsar, D., Olson, C., Paranjape, R. & Barden, J. M., (2014). The validity of gait variability and fractal dynamics obtained from a single, body-fixed tri-axial accelerometer. Journal of Applied Biomechanics, 30(2), 343-347.
Barden, J. M., Kell, R. T., & Kobsar, D. (2011). The effect of critical speed and exercise intensity on stroke phase duration and bilateral asymmetry in 200-m front crawl swimming. Journal of Sport Sciences, 29(5), 517-526.