A Nonproprietary Movement Analysis System (MoJoXlab) Based on Wearable Inertial Measurement Units Applicable to Healthy Participants and Those With Anterior Cruciate Ligament Reconstruction Across a Range of Complex Tasks: Validation Study

Abstract

Background Movement analysis in a clinical setting is frequently restricted to observational methods to inform clinical decision making, which has limited accuracy. Fixed-site, optical, expensive movement analysis laboratories provide gold standard kinematic measurements; however, they are rarely accessed for routine clinical use. Wearable inertial measurement units (IMUs) have been demonstrated as comparable, inexpensive, and portable movement analysis toolkits. MoJoXlab has therefore been developed to work with generic wearable IMUs. However, before using MoJoXlab in clinical practice, there is a need to establish its validity in participants with and without knee conditions across a range of tasks with varying complexity. Objective This paper aimed to present the validation of MoJoXlab software for using generic wearable IMUs for calculating hip, knee, and ankle joint angle measurements in the sagittal, frontal, and transverse planes for walking, squatting, and jumping in healthy participants and those with anterior cruciate ligament (ACL) reconstruction. Methods Movement data were collected from 27 healthy participants and 20 participants with ACL reconstruction. In each case, the participants wore seven MTw2 IMUs (Xsens Technologies) to monitor their movement in walking, jumping, and squatting tasks. The hip, knee, and ankle joint angles were calculated in the sagittal, frontal, and transverse planes using two different software packages: Xsens’ validated proprietary MVN Analyze and MoJoXlab. The results were validated by comparing the generated waveforms, cross-correlation (CC), and normalized root mean square error (NRMSE) values. Results Across all joints and activities, for data of both healthy and ACL reconstruction participants, the CC and NRMSE values for the sagittal plane are 0.99 (SD 0.01) and 0.042 (SD 0.025); 0.88 (SD 0.048) and 0.18 (SD 0.078) for the frontal plane; and 0.85 (SD 0.027) and 0.23 (SD 0.065) for the transverse plane (hip and knee joints only). On comparing the results from the two different software systems, the sagittal plane was very highly correlated, with frontal and transverse planes showing strong correlation. Conclusions This study demonstrates that nonproprietary software such as MoJoXlab can accurately calculate joint angles for movement analysis applications comparable with proprietary software for walking, squatting, and jumping in healthy individuals and those following ACL reconstruction. MoJoXlab can be used with generic wearable IMUs that can provide clinicians accurate objective data when assessing patients’ movement, even when changes are too small to be observed visually. The availability of easy-to-setup, nonproprietary software for calibration, data collection, and joint angle calculation has the potential to increase the adoption of wearable IMU sensors in clinical practice, as well as in free living conditions, and may provide wider access to accurate, objective assessment of patients’ progress over time.

Validity of a New 3-D Motion Analysis Tool for the Assessment of Knee, Hip and Spine Joint Angles during the Single Leg Squat

“…This study shows that easily accessible technology is likely to enter the market for a broad audience of professionals from different fields. The recent research interest from orthopedic and neurologic rehabilitation specialists in this type of technology shows that there is a loud call for quantification of many parameters in their specific patient populations [ 2 , 4 , 9 , 17 , 19 , 20 , 21 , 31 , 34 , 45 ]. Every movement related health problem would thus likely have its own clinical relevant movement parameters.…”

“…Parameters obtained are likely relevant to improve clinical outcomes. An important advantage of this new tool is that it can be applied on the pitch (on field) and in rehab and sports centre settings [19]. The kinematic data are then directly available in contrast with the more time consuming conventional motion capture systems [3,9,14].…”

“…Reliability of wearable active sensor systems is moderate to excellent when compared to optical tracking systems with sagittal and frontal hip and knee measurement error between sagittal 8° and 1° [ 4 , 17 , 18 ]. Active sensors recently proved to be useful and reliable in the assessment of squatting, jumping and walking in patients after ACL reconstruction [ 19 ]. Currently, active sensor monitoring of movement is the subject of many studies in the field of orthopedic and neurologic rehabilitation and sports injury populations [ 17 , 20 , 21 , 22 ].…”

Towards Out-of-Lab Anterior Cruciate Ligament Injury Prevention and Rehabilitation Assessment: A Review of Portable Sensing Approaches

“…Three studies (6%) examined their validity against parameters measured by another portable sensing system, i.e. knee angles from goniometers [63], knee angles from a commercial IMU system [46], and step time asymmetry from pressure insoles [34]. The remaining 24 studies (49%) did not examine the validity of the estimated parameters.…”

Validity and reliability of accelerations and orientations measured using wearable sensors during functional activities

“…These parameters are computed by fusing accelerometer and gyroscope outputs which requires significant processing costs, and the results may be dependent on coding choices. On the other hand, raw IMU outputs such as accelerations and orientations, have been shown to be useful in a wide range of movement-related settings, such as posture recognition 8 , quantification of physical activity levels 9 , determining spatial–temporal gait variables 10 , estimation of hip joint loading patterns 11 , estimation of joint angles 12 , or quantification of knee stability 13 . As such, with recent advances in artificial intelligence methods, machine learning algorithms driven by raw acceleration and orientation signals may provide a unique opportunity to overcome methodological challenges associated with transforming such signals into more complex calculations such as joint kinematic/kinetics, physical activity or posture recognition.…”