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Downey RJ, Richer N, Gupta R, Liu C, Pliner EM, Roy A, Hwang J, Clark DJ, Hass CJ, Manini TM, Seidler RD, Ferris DP. Uneven terrain treadmill walking in younger and older adults. PLoS One 2022; 17:e0278646. [PMID: 36534645 PMCID: PMC9762558 DOI: 10.1371/journal.pone.0278646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 11/21/2022] [Indexed: 12/24/2022] Open
Abstract
We developed a method for altering terrain unevenness on a treadmill to study gait kinematics. Terrain consisted of rigid polyurethane disks (12.7 cm diameter, 1.3-3.8 cm tall) which attached to the treadmill belt using hook-and-loop fasteners. Here, we tested four terrain unevenness conditions: Flat, Low, Medium, and High. The main objective was to test the hypothesis that increasing the unevenness of the terrain would result in greater gait kinematic variability. Seventeen younger adults (age 20-40 years), 25 higher-functioning older adults (age 65+ years), and 29 lower-functioning older adults (age 65+ years, Short Physical Performance Battery score < 10) participated. We customized the treadmill speed to each participant's walking ability, keeping the speed constant across all four terrain conditions. Participants completed two 3-minute walking trials per condition. Using an inertial measurement unit placed over the sacrum and pressure sensors in the shoes, we calculated the stride-to-stride variability in step duration and sacral excursion (coefficient of variation; standard deviation expressed as percentage of the mean). Participants also self-reported their perceived stability for each condition. Terrain was a significant predictor of step duration variability, which roughly doubled from Flat to High terrain for all participant groups: younger adults (Flat 4.0%, High 8.2%), higher-functioning older adults (Flat 5.0%, High 8.9%), lower-functioning older adults (Flat 7.0%, High 14.1%). Similarly, all groups exhibited significant increases in sacral excursion variability for the Medium and High uneven terrain conditions, compared to Flat. Participants were also significantly more likely to report feeling less stable walking over all three uneven terrain conditions compared to Flat. These findings support the hypothesis that altering terrain unevenness on a treadmill will increase gait kinematic variability and reduce perceived stability in younger and older adults.
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Affiliation(s)
- Ryan J. Downey
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America
| | - Natalie Richer
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America
| | - Rohan Gupta
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America
| | - Chang Liu
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America
| | - Erika M. Pliner
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America
| | - Arkaprava Roy
- Department of Biostatistics, University of Florida, Gainesville, FL, United States of America
| | - Jungyun Hwang
- Department of Aging and Geriatric Research, University of Florida, Gainesville, FL, United States of America
| | - David J. Clark
- Department of Aging and Geriatric Research, University of Florida, Gainesville, FL, United States of America
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, FL, United States of America
| | - Chris J. Hass
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States of America
| | - Todd M. Manini
- Department of Aging and Geriatric Research, University of Florida, Gainesville, FL, United States of America
| | - Rachael D. Seidler
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States of America
| | - Daniel P. Ferris
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America
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Kim YK, Visscher RMS, Viehweger E, Singh NB, Taylor WR, Vogl F. A deep-learning approach for automatically detecting gait-events based on foot-marker kinematics in children with cerebral palsy-Which markers work best for which gait patterns? PLoS One 2022; 17:e0275878. [PMID: 36227847 PMCID: PMC9562216 DOI: 10.1371/journal.pone.0275878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 09/25/2022] [Indexed: 11/06/2022] Open
Abstract
Neuromotor pathologies often cause motor deficits and deviations from typical locomotion, reducing the quality of life. Clinical gait analysis is used to effectively classify these motor deficits to gain deeper insights into resulting walking behaviours. To allow the ensemble averaging of spatio-temporal metrics across individuals during walking, gait events, such as initial contact (IC) or toe-off (TO), are extracted through either manual annotation based on video data, or through force thresholds using force plates. This study developed a deep-learning long short-term memory (LSTM) approach to detect IC and TO automatically based on foot-marker kinematics of 363 cerebral palsy subjects (age: 11.8 ± 3.2). These foot-marker kinematics, including 3D positions and velocities of the markers located on the hallux (HLX), calcaneus (HEE), distal second metatarsal (TOE), and proximal fifth metatarsal (PMT5), were extracted retrospectively from standard barefoot gait analysis sessions. Different input combinations of these four foot-markers were evaluated across three gait subgroups (IC with the heel, midfoot, or forefoot). For the overall group, our approach detected 89.7% of ICs within 16ms of the true event with a 18.5% false alarm rate. For TOs, only 71.6% of events were detected with a 33.8% false alarm rate. While the TOE|HEE marker combination performed well across all subgroups for IC detection, optimal performance for TO detection required different input markers per subgroup with performance differences of 5-10%. Thus, deep-learning LSTM based detection of IC events using the TOE|HEE markers offers an automated alternative to avoid operator-dependent and laborious manual annotation, as well as the limited step coverage and inability to measure assisted walking for force plate-based detection of IC events.
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Affiliation(s)
- Yong Kuk Kim
- Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
- * E-mail:
| | - Rosa M. S. Visscher
- Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
| | - Elke Viehweger
- Laboratory for Movement Analysis, Department of Orthopedics, University Children’s Hospital Basel, Basel, Switzerland
| | - Navrag B. Singh
- Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
| | - William R. Taylor
- Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
| | - Florian Vogl
- Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
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