All Publications

  • AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization. bioRxiv : the preprint server for biology Werling, K., Bianco, N. A., Raitor, M., Stingel, J., Hicks, J. L., Collins, S. H., Delp, S. L., Liu, C. K. 2023


    Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. We then apply a linear method followed by another non-convex optimization to find body segment masses and fine tune kinematics to minimize residual forces given corresponding trajectories of ground reaction forces. The optimization approach requires approximately 3-5 minutes to determine a subjecťs skeleton dimensions and motion kinematics, and less than 30 minutes of computation to also determine dynamically consistent skeleton inertia properties and fine-tuned kinematics and kinetics, compared with about one day of manual work for a human expert. We used AddBiomechanics to automatically reconstruct joint angle and torque trajectories from previously published multi-activity datasets, achieving close correspondence to expert-calculated values, marker root-mean-square errors less than 2cm, and residual force magnitudes smaller than 2% of peak external force. Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at, which is available at no cost and asks that users agree to share processed and de-identified data with the community. As of this writing, hundreds of researchers have used the prototype tool to process and share about ten thousand motion files from about one thousand experimental subjects. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to use state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.

    View details for DOI 10.1101/2023.06.15.545116

    View details for PubMedID 37398034

    View details for PubMedCentralID PMC10312696

  • Trajectory and Sway Prediction Towards Fall Prevention. IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation Wang, W., Raitor, M., Collins, S., Liu, C. K., Kennedy, M. 2023; 2023: 10483-10489


    Falls are the leading cause of fatal and non-fatal injuries, particularly for older persons. Imbalance can result from the body's internal causes (illness), or external causes (active or passive perturbation). Active perturbation results from applying an external force to a person, while passive perturbation results from human motion interacting with a static obstacle. This work proposes a metric that allows for the monitoring of the persons torso and its correlation to active and passive perturbations. We show that large changes in the torso sway can be strongly correlated to active perturbations. We also show that we can reasonably predict the future path and expected change in torso sway by conditioning the expected path and torso sway on the past trajectory, torso motion, and the surrounding scene. This could have direct future applications to fall prevention. Results demonstrate that the torso sway is strongly correlated with perturbations. And our model is able to make use of the visual cues presented in the panorama and condition the prediction accordingly.

    View details for DOI 10.1109/icra48891.2023.10161361

    View details for PubMedID 38009123

    View details for PubMedCentralID PMC10671274

  • Trajectory and Sway Prediction Towards Fall Prevention Wang, W., Raitor, M., Collins, S., Liu, C., Kennedy, M. 2023: 10483-10489
  • Estimating Lower Limb Kinematics Using a Reduced Wearable Sensor Count IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Sy, L., Raitor, M., Del Rosario, M., Khamis, H., Kark, L., Lovell, N. H., Redmond, S. J. 2021; 68 (4): 1293–1304


    This paper presents an algorithm for accurately estimating pelvis, thigh, and shank kinematics during walking using only three wearable inertial sensors.The algorithm makes novel use of a constrained Kalman filter (CKF). The algorithm iterates through the prediction (kinematic equation), measurement (pelvis position pseudo-measurements, zero velocity update, flat-floor assumption, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints).Evaluation of the algorithm using an optical motion capture-based sensor-to-segment calibration on nine participants (7 men and 2 women, weight [Formula: see text] kg, height [Formula: see text] m, age [Formula: see text] years old), with no known gait or lower body biomechanical abnormalities, who walked within a [Formula: see text] m 2 capture area shows that it can track motion relative to the mid-pelvis origin with mean position and orientation (no bias) root-mean-square error (RMSE) of [Formula: see text] cm and [Formula: see text], respectively. The sagittal knee and hip joint angle RMSEs (no bias) were [Formula: see text] and [Formula: see text], respectively, while the corresponding correlation coefficient (CC) values were [Formula: see text] and [Formula: see text].The CKF-based algorithm was able to track the 3D pose of the pelvis, thigh, and shanks using only three inertial sensors worn on the pelvis and shanks.Due to the Kalman-filter-based algorithm's low computation cost and the relative convenience of using only three wearable sensors, gait parameters can be computed in real-time and remotely for long-term gait monitoring. Furthermore, the system can be used to inform real-time gait assistive devices.

    View details for DOI 10.1109/TBME.2020.3026464

    View details for Web of Science ID 000633535400018

    View details for PubMedID 32970590

  • Foster inclusive community SCIENCE Raitor, M. 2020; 367 (6473): 35
  • Making science accessible. Science (New York, N.Y.) Tuosto, K. n., Johnston, J. T., Connolly, C. n., Lo, C. n., Sanganyado, E. n., Winter, K. A., Roembke, T. n., Richter, W. E., Isaacson, K. J., Raitor, M. n., Kosanic, A. n., Bessone, L. n., Heim, A. B., Srivastava, P. n., Hughes, P. W., Aamodt, C. M. 2020; 367 (6473): 34–35

    View details for DOI 10.1126/science.aba6129

    View details for PubMedID 31896709

  • HapWRAP: Soft Growing Wearable Haptic Device Agharese, N., Cloyd, T., Blumenschein, L. H., Raitor, M., Hawkes, E. W., Culbertson, H., Okamura, A. M., IEEE IEEE COMPUTER SOC. 2018: 5466–72
  • Design of a Soft Catheter for Low-Force and Constrained Surgery Slade, P., Gruebele, A., Hammond, Z., Raitor, M., Okamura, A. M., Hawkes, E. W., Bicchi, A., Okamura, A. IEEE. 2017: 174–80
  • A Dual-Flywheel Ungrounded Haptic Feedback System Provides Single-Axis Moment Pulses for Clear Direction Signals Walker, J. M., Raitor, M., Mallery, A., Culbertson, H., Stolka, P., Okamura, A. M., Choi, S. M., Kuchenbecker, K. J., Gerling, G. IEEE. 2016: 7–13