Reliability and Validity of an Inertial Measurement System to Quantify Lower Extremity Joint Angle in Functional Movements

Abstract

The purpose of this research was to determine if the commercially available Perception Neuron motion capture system was valid and reliable in clinically relevant lower limb functional tasks. Twenty healthy participants performed two sessions on different days: gait, squat, single-leg squat, side lunge, forward lunge, and counter-movement jump. Seven IMUs and an OptiTrack system were used to record the three-dimensional joint kinematics of the lower extremity. To evaluate the performance, the multiple correlation coefficient (CMC) and the root mean square error (RMSE) of the waveforms as well as the difference and intraclass correlation coefficient (ICC) of discrete parameters were calculated. In all tasks, the CMC revealed fair to excellent waveform similarity (0.47–0.99) and the RMSE was between 3.57° and 13.14°. The difference between discrete parameters was lower than 14.54°. The repeatability analysis of waveforms showed that the CMC was between 0.54 and 0.95 and the RMSE was less than 5° in the frontal and transverse planes. The ICC of all joint angles in the IMU was general to excellent (0.57–1). Our findings showed that the IMU system might be utilized to evaluate lower extremity 3D joint kinematics in functional motions.

Validity and Reliability of a Wearable Goniometer Sensor Controlled by a Mobile Application for Measuring Knee Flexion/Extension Angle during the Gait Cycle

“…The AE of the wearable goniometer relative to the optical motion analysis system was 3.3° on average over the entire gait cycle. Previous studies have reported that the mean AE during a gait cycle was 2.3–3.3° between a goniometer system and an optical motion analysis system [ 13 ] and that the mean root-mean-square-error (RMSE) during a gait cycle was 7.1–9.3° between an IMU-based system and an optical motion analysis system [ 36 , 37 ], which is comparable to that reported by the present results. A discrete value analysis of the peak knee extension/flexion angle during the gait cycle also showed very large correlation coefficients between the wearable goniometer and optical motion analysis system for knee flexion angle at IC, and peak knee flexion and extension during the stance phase.…”

“…The reliability study also exhibited excellent CMC between Day 1 and Day 2 (1 week later) of the wearable goniometer testing, except for one participant. These results reflect previous three-dimensional gait analysis studies based on optical tracking and inertial sensor systems [ 31 , 36 , 37 ]. The AE between the two tests was interpreted as good (AE ≤ 2°) to acceptable (2 < AE ≤ 5°) throughout the gait cycle [ 31 , 32 ], which was comparable to a previously reported RMSE of 6.3°and 5.0° for an IMU-based and an optical motion analysis system, respectively [ 36 ].…”

Inertial Sensor-to-Segment Calibration for Accurate 3D Joint Angle Calculation for Use in OpenSim

“…This study impacts the use of wearable sensor technology for documenting 3D knee joint kinematics (e.g., patients with knee OA). However, to evaluate the impact of specific clinical treatments out-of-the-lab such as gait retraining or pain medication, the knee loading should ideally be accurately measured in real-life conditions, e.g., to estimate the impact of the patient's gait pattern modifications (toe-in/out, trunk lean) on knee loading [2,4,[72][73][74][75]. Future work should therefore validate the method developed for the individual activities of daily living with more natural activity sequences, and it should focus on the estimation of kinetics, in particular the ground reaction forces and joint moments estimation to enable true real-world monitoring of the impact of feedback and gait interventions on an individual patient's locomotor function and joint loading.…”

Predicting Coordination Variability of Selected Lower Extremity Couplings during a Cutting Movement: An Investigation of Deep Neural Networks with the LSTM Structure

“…In addition, distinct prediction models were trained for each direction of CM (90°, 135°, 180°). The coefficient of multiple correlation (CMC) values and root mean square error (RMSE) was used to evaluate the prediction accuracy of the model [ 50 , 60 ], with CMC values interpreted as perfect similarity (0.95–1), very similar (0.85–0.94), moderate similarity (0.5–0.74), and poor similarity (0–0.59) [ 61 ]. RMSE values were utilized to evaluate segmental coordination prediction data, and actual data error means.…”