Agreement in Measures of Gait Between a 3-Sensor Inertial Measurement System and a 3D Motion Analysis System Elise Klæbo Vonstad1, Marit N Olsen1, Linda Rennie1, Beate E Gjesdal1, Arve Opheim1,2 1Sunnaas Rehabilitation Hospital, Nesodden, Norway 2Rehabilitation Medicine, Sahlgrenska Academy, University of Gothenburg, Sweden Background Systems Methods The use of inertial measurement units (IMU) are increasing in ambulatory gait analysis A novel system with three sensors has been introduced and validated for normal and slightly asymmetric gait6 3-dimensional gait analysis (3DGA) is considered the gold standard By IMU-systems, spatio-temporal variables are most commonly calculated using gait events (initial and terminal contact, and mid-swing) 3, 4, and kinematic variables are calculated using angular velocity and estimates based on gait events1,2 The measurement accuracy in gait variables in patients with contractures and/or reduced rang of motion (RoM) in one or more joints in the lower extremities: i.e. stiff knee and crouch patterns, is not known. Such conditions greatly alters the persons gait pattern Aim: To measure the agreement in gait variables between a 3-sensor IMU-system and a 3DGA-system in 3 different situations. 3DGA: Vicon Motion Systems Ltd (Oxford, UK) Six MX13 IR-cameras Plug-in-Gait model IMU: APDM, Inc (Portland, OR) 3-axial accelerometers, gyroscopes and magnetometers Three OPAL sensors 3 situations: Normal Knee locked in 45° Knee locked in 0° Knee lock orthosis, dominant foot (R:7) 6 Walking trials pr situation Randomized order Systems capturing simultaneously Healthy adults 3 5 Mean age: 31,3 Mean BMI (SD): 21,1 (2,2) -16 reflective markers - Ankles and lumbar area Outcome measures Statistics Kinematic variable: Knee RoM (°) Spatio-temporal variables: Mean difference (SD) One-sample t-test (sig. 2-tailed) Bland-Altman (95% Limits of Agreement)5 ICC 1,2 (95% CI) Stride length (cm) Double support (% GCT) Cadence (steps/min) Stance phase length (%GCT) Gait cycle time (GCT, sec) Walking speed (cm/sec) Results for Kinematic variable Results for Spatio-temporal variables Mean Difference (SD) 3DGA – IMU   Knee Locked 45°  p-value Knee Locked 0° Stride length (cm) 20.5 (9.81) 0.001 30.9 (8.68) <0.001 Cadence (Steps/min)  0.33 (0.99) 0.378 - 1.05 (1.13) 0.035 Gait Cycle Time (Seconds) 0.00 (0.01) 0.426 0.01 (0.01) 0.038 Double Support (% of GCT) 4.7 (1.56) 4.6 (3.14) 0.004 Stance Phase (% of GCT) 3.5 (1.50) 3.6 (2.44) Walking Speed (cm/second) 19.3 (9.54) 15.5 (17.68) 0.042 Agreement in knee RoM° 3DGA-IMU, Bland-Altman (95% LoA)    MeanDiff Lower Upper Normal 10.7 6.3 15.1 Knee Locked 45° -37.8 -50.5 -25.2 Knee Locked 0° -42.7 -51.3 -36.0 Discussion Gait analysis variables using a three-sensor IMU-system with the knee on dominant foot immobilized was assessed. The variables double support and stance phase, which are calculated using foot off events, showed moderate to low agreement between systems. The three-sensor IMU-system consequently overestimated knee RoM compared to 3DGA when the knee was locked both in 0° and 45°. There was a tendency towards lower agreement for most variables when the knee was locked in 0° compared to 45°. During normal gait, IMU-system measured knee RoM to be on average 10° less than knee RoM measured by the 3DGA. The variables that were directly calculated from initial contact of the foot, seems to be the ones with the highest agreement. Kinematic variables showed very low agreement between systems with the knee locked in both in 0° and 45°, indicating a poor ability of the IMU system in recognizing low RoM. The variables that were estimated using foot off events seem to have lower agreement. The IMU system underestimated almost all the spatio-temporal variables, with knee locked both in 0° and 45°, compared to 3DGA. Take home message: For the temporal variables gait cycle time and cadence, which are based on initial contact events, there were excellent agreements between systems. An IMU system consisting of three sensors seem to be vulnerable to alterations in gait patterns caused by greatly reduced RoM in one joint, e.g. patients with stiff knee gait or crouch patterns. Further investigation is encouraged. Spatial variables measuring distance, such as stride length and walking speed, showed moderate to very low agreement. References 1Dejnabadi, H., Jolles, B. M., Casanova, E., Fua, P., & Aminian, K. (2006). Estimation and visualization of sagittal kinematics of lower limbs orientation using body-fixed sensors. IEEE Trans Biomed Eng, 53(7), 1385-1393. 2 Leardini, A., Lullini, G., Giannini, S., Berti, L., Ortolani, M., & Caravagol, P. 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