Predicting Individualized Joint Kinematics Over Continuous Variations of Walking, Running, and Stair Climbing
<italic>Goal:</italic> Accounting for gait individuality is important to positive outcomes with wearable robots, but manually tuning multi-activity models is time-consuming and not viable in a clinic. Generalizations can possibly be made to predict gait individuality in unobserved condit...
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IEEE
2022-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10006886/ |
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| author | Emma Reznick Cara Gonzalez Welker Robert D. Gregg |
| author_facet | Emma Reznick Cara Gonzalez Welker Robert D. Gregg |
| author_sort | Emma Reznick |
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| description | <italic>Goal:</italic> Accounting for gait individuality is important to positive outcomes with wearable robots, but manually tuning multi-activity models is time-consuming and not viable in a clinic. Generalizations can possibly be made to predict gait individuality in unobserved conditions. <italic>Methods:</italic> Kinematic individuality—how one person's joint angles differ from the group—is quantified for every subject, joint, ambulation mode (walking, running, stair ascent, and stair descent), and intramodal task (speed, incline) in an open-access dataset with 10 able-bodied subjects. Four N-way ANOVAs test how prediction methods affect the fit to experimental data between and within ambulation modes. We test whether walking individuality (measured at a single speed on level ground) carries across modes, or whether a mode-specific prediction (based on a single task for each mode) is significantly more effective. <italic>Results:</italic> Kinematic individualization improves fit across joint and task if we consider each mode separately. Across all modes, tasks, and joints, modal individualization improved the fit in 81% of trials, improving the fit on average by 4.3<inline-formula><tex-math notation="LaTeX">${}^{\circ }$</tex-math></inline-formula> across the gait cycle. This was statistically significant at all joints for walking and running, and half the joints for stair ascent/descent. <italic>Conclusions:</italic> For walking and running, kinematic individuality can be easily generalized within mode, but the trends are mixed on stairs depending on joint. |
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| institution | Kabale University |
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| language | English |
| publishDate | 2022-01-01 |
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| spelling | doaj-art-f9d4d60c7f1848dcae4e2c09d980c7db2025-08-20T03:32:33ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762022-01-01321121710.1109/OJEMB.2023.323443110006886Predicting Individualized Joint Kinematics Over Continuous Variations of Walking, Running, and Stair ClimbingEmma Reznick0https://orcid.org/0000-0003-0422-1401Cara Gonzalez Welker1https://orcid.org/0000-0003-2769-501XRobert D. Gregg2https://orcid.org/0000-0002-0729-2857Department of Robotics, University of Michigan, Ann Arbor, MI, USADepartment of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, USADepartment of Robotics, University of Michigan, Ann Arbor, MI, USA<italic>Goal:</italic> Accounting for gait individuality is important to positive outcomes with wearable robots, but manually tuning multi-activity models is time-consuming and not viable in a clinic. Generalizations can possibly be made to predict gait individuality in unobserved conditions. <italic>Methods:</italic> Kinematic individuality—how one person's joint angles differ from the group—is quantified for every subject, joint, ambulation mode (walking, running, stair ascent, and stair descent), and intramodal task (speed, incline) in an open-access dataset with 10 able-bodied subjects. Four N-way ANOVAs test how prediction methods affect the fit to experimental data between and within ambulation modes. We test whether walking individuality (measured at a single speed on level ground) carries across modes, or whether a mode-specific prediction (based on a single task for each mode) is significantly more effective. <italic>Results:</italic> Kinematic individualization improves fit across joint and task if we consider each mode separately. Across all modes, tasks, and joints, modal individualization improved the fit in 81% of trials, improving the fit on average by 4.3<inline-formula><tex-math notation="LaTeX">${}^{\circ }$</tex-math></inline-formula> across the gait cycle. This was statistically significant at all joints for walking and running, and half the joints for stair ascent/descent. <italic>Conclusions:</italic> For walking and running, kinematic individuality can be easily generalized within mode, but the trends are mixed on stairs depending on joint.https://ieeexplore.ieee.org/document/10006886/Biomechanicsgait recognitionassistive devicesassistive robots |
| spellingShingle | Emma Reznick Cara Gonzalez Welker Robert D. Gregg Predicting Individualized Joint Kinematics Over Continuous Variations of Walking, Running, and Stair Climbing IEEE Open Journal of Engineering in Medicine and Biology Biomechanics gait recognition assistive devices assistive robots |
| title | Predicting Individualized Joint Kinematics Over Continuous Variations of Walking, Running, and Stair Climbing |
| title_full | Predicting Individualized Joint Kinematics Over Continuous Variations of Walking, Running, and Stair Climbing |
| title_fullStr | Predicting Individualized Joint Kinematics Over Continuous Variations of Walking, Running, and Stair Climbing |
| title_full_unstemmed | Predicting Individualized Joint Kinematics Over Continuous Variations of Walking, Running, and Stair Climbing |
| title_short | Predicting Individualized Joint Kinematics Over Continuous Variations of Walking, Running, and Stair Climbing |
| title_sort | predicting individualized joint kinematics over continuous variations of walking running and stair climbing |
| topic | Biomechanics gait recognition assistive devices assistive robots |
| url | https://ieeexplore.ieee.org/document/10006886/ |
| work_keys_str_mv | AT emmareznick predictingindividualizedjointkinematicsovercontinuousvariationsofwalkingrunningandstairclimbing AT caragonzalezwelker predictingindividualizedjointkinematicsovercontinuousvariationsofwalkingrunningandstairclimbing AT robertdgregg predictingindividualizedjointkinematicsovercontinuousvariationsofwalkingrunningandstairclimbing |