In-lab, marker-based gait analyses may not represent real-world gait. Real-world gait analyses may be feasible using inertial measurement units (IMUs) in combination with open-source data processing pipelines (OpenSense). Before using OpenSense to study real-world gait, we must determine whether these methods estimate joint kinematics similarly to traditional marker-based motion capture (MoCap) and differentiate groups with clinically different gait mechanics. Healthy young and older adults and older adults with knee osteoarthritis completed this study. We captured MoCap and IMU data during overground walking at 2 speeds. MoCap and IMU kinematics were computed with OpenSim workflows. We tested whether sagittal kinematics differed between MoCap and IMU, whether tools detected between-group differences similarly, and whether kinematics differed between tools by speed. MoCap showed more anterior pelvic tilt (0%–100% stride) and joint flexion than IMU (hip: 0%–38% and 61%–100% stride; knee: 0%–38%, 58%–89%, and 95%–99% stride; and ankle: 6%–99% stride). There were no significant tool-by-group interactions. We found significant tool-by-speed interactions for all angles. While MoCap- and IMU-derived kinematics differed, the lack of tool-by-group interactions suggests consistent tracking across clinical cohorts. Results of the current study suggest that IMU-derived kinematics with OpenSense may enable reliable evaluation of gait in real-world settings.

Common in-lab, marker-based gait analyses may not represent daily, real-world gait. Real-world gait analyses may be feasible using inertial measurement units (IMUs), especially with recent advancements in open-source methods (e.g., OpenSense). Before using OpenSense to study real-world gait, we must determine whether these methods: (1) estimate joint kinematics similarly to traditional marker-based motion capture (MoCap) and (2) differentiate groups with clinically different gait mechanics. Healthy young and older adults and older adults with knee osteoarthritis completed this study. We captured MoCap and IMU data during overground walking at self-selected and faster speeds. MoCap and IMU kinematics were computed with appropriate OpenSim workflows. We tested whether sagittal kinematics differed between MoCap- and IMU-derived data, whether tools detected between-group differences similarly, and whether kinematics differed between tools by speed. MoCap data showed more flexion than IMU data (hip: 0-47 and 65-100% stride, knee: 0-38 and 58-91% stride, ankle: 18-100% stride). Group kinematics differed at the hip (young extension > knee osteoarthritis at 30-47% stride) and ankle (young plantar flexion > older healthy at 62-65% stride). Group-by-tool interactions occurred at the hip (61-63% stride). Significant tool-by-speed interactions were found, with hip and knee flexion increasing more for MoCap than IMU data with speed (hip: 12-15% stride, knee: 60-63% stride). While MoCap- and IMU-derived kinematics differed, our results suggested that the tools similarly detected clinically meaningful differences in gait. Results of the current study suggest that IMU-derived kinematics with OpenSense may enable the valid and reliable evaluation of gait in real-world, unobserved settings.

BackgroundFunctional orientation orients inertial measurement unit (IMU) data (i.e., linear accelerations and angular velocities) to interpretable reference frames. To confidently collect reliable out-of-lab data, it is important to determine the extent to which we can correct for sensor placement variability.Research QuestionTo what extent does a functional orientation method minimize the effect of variability in sensor placement on IMU data?MethodsTwenty healthy adults (10 younger 28.2±3.7 years, 10 older 60.8±3.3years) walked overground at preferred speed in a lab. Three IMUs were placed per segment on the pelvis, thigh, shank, and foot. IMU data were oriented using an assumed orientation and two versions of a walking-based functional orientation (X-functional anchored to axis of rotation and Z-functional anchored to gravity). Segment angular excursions were calculated for each orientation method and compared between groups and sensor placements.Results and SignificanceNo significant interaction was found between sensor placement and group for any orientation method. For assumed orientation, segment angular excursion differed between sensor placements for at least 15% and up to 95% of the gait cycle, depending on segment. For both functional orientation methods, foot and shank excursions did not differ between sensors. Thigh excursion differed only for the X-functional orientation from 27-68% of the gait cycle. Neither functional orientation fully corrected for differences at the pelvis leaving significantly different excursions between 24-50% of the gait cycle. Functional orientation can reliably correct for variability in lower extremity IMU sensor placement. These methods can enable repeatable real-world IMU data collection in settings where sensors may move within or between days. Performing functional orientation periodically throughout a day can minimize the effect of sliding or rotating of the sensors on IMU-calculated gait measures and give in-lab quality gait data throughout hours of real-world activity to better understand the true movement of participants.

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