COLLABORATIVE PROJECTS

Collaborative projects (CPs) drive the development of our computational models and tools. They help us identify technological needs within the bioengineering, biomechanical modeling, data science, mHealth, and rehabilitation research communities. We work with them to define the specifications for tools to address those needs, and they serve as beta testers to refine and harden the tools we develop and share through extensive two-way collaboration. 

Our current CPs are:

  • Optimization of Exoskeleton Assistance Using Wearable Sensors
    • PI: Steve Collins, Stanford University
  • Personalized Assessment and Treatment of Patellar Instability Enabled Through Machine Learning
    • PIs: Shital Parikh, Beth Shubin Stein, University of Cincinnati
  • Using Video and Wearable Sensor Data for Fall Prevention in Older Adults
    • PIs: Women’s Health Initiative
  • Measuring Knee Adduction Moment Pre and Post Intervention using OpenCap
    • PI: Constance Chu, Stanford University
  • Digital Biomarkers of Post-traumatic Osteoarthritis: Toward Precision Rehabilitation
    • PI: Eni Halilaj, Carnegie Mellon University
  • Developing video-based biomarkers for facioscapulohumeral muscular dystrophy and myotonic dystrophy
    • PI: John Day, Stanford University
  • Empatho-Kinaesthetic Sensor Technology – Sensor Techniques and Data Analysis Methods for Empatho-Kinaesthetic Modeling and Condition Monitoring
    • PIs: Martin Vossiek, Bjoern Eskofier, EmpkinS

Graduated CPs:

  • Prediction of Freezing of Gait from Neural Recordings and IMU Data
    • PI: Helen Bronte-Stewart, Stanford University
  • Personalized Gait Retraining for Individuals with Osteoarthritis
    • PI: Julie Kolesar, VA Palo Alto Health Care System
  • Estimating Bone Loading via Wearable Sensors in Children with Cerebral Palsy
    • PI: Tishya Wren, Children’s Hospital Los Angeles
  • Patterns of Asymmetry and Energy Cost Generated from Predictive Simulations of Hemiparetic Gait
    • PI: James Finley, University of Southern California

Have a project you think would be a great CP? Please fill out this Google form or contact us at mobilize-center@stanford.edu.

Publications from Collaborative Projects
  1. Bianco, N. A., Collins, S. H., Liu, K., & Delp, S. L. (2023). Simulating the effect of ankle plantarflexion and inversion-eversion exoskeleton torques on center of mass kinematics during walking. PLOS Computational Biology, 19(8), e1010712. PMCID: PMC10434928
  2. Kaneda, J. M., Seagers, K. A., Uhlrich, S. D., Kolesar, J. A., Thomas, K. A., & Delp, S. L. (2023). Can static optimization detect changes in peak medial knee contact forces induced by gait modifications?. Journal of Biomechanics, 152, 111569. PMCID: PMC10231980
  3. Lee, M. R., Hicks, J. L., Wren, T. A., & Delp, S. L. (2022). Independently ambulatory children with spina bifida experience near-typical joint moments and forces during walking. Gait and Posture, 99, 1-8. PMCID: PMC9772073
  4. Slade, P., Kochenderfer, M. J., Delp, S. L., & Collins, S. H. (2022). Personalizing exoskeleton assistance while walking in the real world. Nature, 610(7931), 277-282. PMCID: PMC9556303
  5. Johnson, R. T., Bianco, N. A., & Finley, J. M. (2022). Patterns of asymmetry and energy cost generated from predictive simulations of hemiparetic gait. PLOS Computational Biology, 18(9), e1010466. PMCID: PMC9491609
  6. Seagers, K., Uhlrich, S. D., Kolesar, J. A., Berkson, M., Kaneda, J. M., Beaupre, G. S., & Delp, S. L. (2022). Changes in foot progression angle during gait reduce the knee adduction moment and do not increase hip moments in individuals with knee osteoarthritis. Journal of Biomechanics, 141, 111204. PMCID: PMC9466647
  7. Uhlrich, S. D., Jackson, R. W., Seth, A., Kolesar, J. A., & Delp, S. L. (2022). Muscle coordination retraining inspired by musculoskeletal simulations reduces knee contact force. Scientific Reports, 12(1), 1-13. PMCID: PMC9262899
  8. Bianco, N. A., Franks, P. W., Hicks, J. L., & Delp, S. L. (2022). Coupled exoskeleton assistance simplifies control and maintains metabolic benefits: a simulation study. PloS One, 17(1), e0261318. PMCID: PMC8730392
  9. O’Day, J., Lee, M., Seagers, K., Hoffman, S., Jih-Schiff, A., Kidziński, Ł., Delp, S., & Bronte-Stewart, H. (2022). Assessing inertial measurement unit locations for freezing of gait detection and patient preference. Journal of Neuroengineering and Rehabilitation, 19(1), 20. PMCID: PMC8842967
  10. Uhlrich, S. D., Kolesar, J. A., Kidziński, Ł., Boswell, M. A., Silder, A., Gold, G. E., Delp, S.L., & Beaupre, G. S. (2022). Personalization improves the biomechanical efficacy of foot progression angle modifications in individuals with medial knee osteoarthritis. Journal of Biomechanics, 144, 111312. PMCID: PMC9889103
  11. Slade, P., Kochenderfer, M. J., Delp, S. L., & Collins, S. H. (2021). Sensing leg movement enhances wearable monitoring of energy expenditure. Nature Communications, 12(1), 1-11. PMCID: PMC8277831
  12. Boswell, M.A., Uhlrich, S.D., Kidzinski, L., Thomas, K., Kolesar, J.A., Gold, G.E., Beaupre, G.S., & Delp, S.L. (2021). A neural network to predict the knee adduction moment in patients with osteoarthritis using anatomical landmarks obtainable from 2D video analysis, Osteoarthritis and Cartilage, 29(3), 346-356.  PMCID: PMC7925428
  13. O’Day, J., Syrkin-Nikolau, J., Anidi, C., Kidzinski, L., Delp, S., & Bronte-Stewart, H. (2020). The turning and barrier course reveals gait parameters for detecting freezing of gait and measuring the efficacy of deep brain stimulation. PLoS One, 15(4), e0231984. PMCID: PMC7190141
  14. Pearl, O., Rokhmanova, N., Dankovich, L., Faille, S., Bergbreiter, S., Halilaj, E. Capacitive Sensing for Natural Environment Rehabilitation Monitoring, Accepted, Nature Communications
  15. Pearl, O., Shin, S., Godura, A., Bergbreiter, S., & Halilaj, E. (2023). Fusion of video and inertial sensing data via dynamic optimization of a biomechanical model. Journal of Biomechanics, 155, 111617.
  16. Uhlrich SD, Ruth PS, de Monts C, Falisse A, Muccini J, Ataide P, Day J, Duong T, & Delp SL. (2024). Towards Video-Based Movement Biomarkers for Neuromuscular Diseases. In International Conference on NeuroRehabilitation (pp. 501-504). Cham: Springer Nature Switzerland.
  17. Raitor, M., Ruggles, S., Delp, S. L., Liu, C. K., & Collins, S. H. (2024). Lower-limb exoskeletons appeal to both clinicians and older adults, especially for fall prevention and joint pain reduction. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 32, 1577 – 1585
  18. Ruth P, Uhlrich SD, de Monts C, Falisse A, Covitz S, Vogt-Domke S, Muccini J, Day J, Duong T, & Delp S. (2024). Video-based biomechanical analysis captures disease-specific movement signatures of different neuromuscular diseases. bioRxiv. [Preprint] Published online September 30, 2024. doi: 10.1101/2024.09.26.613967
  19. Slade P, Atkeson C, Donelan JM, Houdijk H, Ingraham KA, Kim M, Kong K, Poggensee KL, Riener R, Steinert M, Zhang J, & Collins SH. (2024). On human-in-the-loop optimization of human–robot interaction. Nature, 633(8031), 779-788.
  20. Melbourne JA, Kehnemouyi YM, O’Day JJ, Wilkins KB, Gala AS, Petrucci MN, Lambert EF, Dorris HJ, Diep C, Herron JA, & Bronte-Stewart, H. M. (2023). Kinematic adaptive deep brain stimulation for gait impairment and freezing of gait in Parkinson’s disease. Brain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation, 16(4), 1099-1101.
  21. Diep, C., O’Day, J., Kehnemouyi, Y., Burnett, G., & Bronte-Stewart, H. (2021). Gait parameters measured from wearable sensors reliably detect freezing of gait in a stepping in place task. Sensors, 21(8), 2661. PMCID: PMC8069332