Validation of wearable inertial sensor-based gait analysis system for measurement of spatiotemporal parameters and lower extremity joint kinematics in sagittal plane

Wearable inertial sensor-based motion analysis systems are promising alternatives to standard camera-based motion capture systems for the measurement of gait parameters and joint kinematics. These wearable sensors, unlike camera-based gold standard systems, find usefulness in outdoor natural environment along with confined indoor laboratory-based environment due to miniature size and wireless data transmission. This study reports validation of our developed (i-Sens) wearable motion analysis system against standard motion capture system. Gait analysis was performed at self-selected speed on non-disabled volunteers in indoor ( n = 15) and outdoor ( n = 8) environments. Two i-Sens units were placed at the level of knee and hip along with passive markers (for indoor study only) for simultaneous 3D motion capture using a motion capture system. Mean absolute percentage error (MAPE) was computed for spatiotemporal parameters from the i-Sens system versus the motion capture system as a true reference. Mean and standard deviation of kinematic data for a gait cycle were plotted for both systems against normative data. Joint kinematics data were analyzed to compute the root mean squared error (RMSE) and Pearson’s correlation coefficient. Kinematic plots indicate a high degree of accuracy of the i-Sens system with the reference system. Excellent positive correlation was observed between the two systems in terms of hip and knee joint angles (Indoor: hip 3.98° ± 1.03°, knee 6.48° ± 1.91°, Outdoor: hip 3.94° ± 0.78°, knee 5.82° ± 0.99°) with low RMSE. Reliability characteristics (defined using standard statistical thresholds of MAPE) of stride length, cadence, walking speed in both outdoor and indoor environment were well within the “Good” category. The i-Sens system has emerged as a potentially cost-effective, valid, accurate, and reliable alternative to expensive, standard motion capture systems for gait analysis. Further clinical trials using the i-Sens system are warranted on participants across different age groups.

Predicting Knee Joint Kinematics from Wearable Sensor Data in People with Knee Osteoarthritis and Clinical Considerations for Future Machine Learning Models

Deep learning models developed to predict knee joint kinematics are usually trained on inertial measurement unit (IMU) data from healthy people and only for the activity of walking. Yet, people with knee osteoarthritis have difficulties with other activities and there are a lack of studies using IMU training data from this population. Our objective was to conduct a proof-of-concept study to determine the feasibility of using IMU training data from people with knee osteoarthritis performing multiple clinically important activities to predict knee joint sagittal plane kinematics using a deep learning approach. We trained a bidirectional long short-term memory model on IMU data from 17 participants with knee osteoarthritis to estimate knee joint flexion kinematics for phases of walking, transitioning to and from a chair, and negotiating stairs. We tested two models, a double-leg model (four IMUs) and a single-leg model (two IMUs). The single-leg model demonstrated less prediction error compared to the double-leg model. Across the different activity phases, RMSE (SD) ranged from 7.04° (2.6) to 11.78° (6.04), MAE (SD) from 5.99° (2.34) to 10.37° (5.44), and Pearson’s R from 0.85 to 0.99 using leave-one-subject-out cross-validation. This study demonstrates the feasibility of using IMU training data from people who have knee osteoarthritis for the prediction of kinematics for multiple clinically relevant activities.

Hip joint motion does not change one year after arthroscopic osteochondroplasty in patients with femoroacetabular impingement evaluated with dynamic radiostereometry

Purpose Dynamic radiostereometric analysis (dRSA) enables precise non-invasive three-dimensional motion-tracking of bones for assessment of joint kinematics. Hereby, the biomechanical effects of arthroscopic osteochondroplasty of the hip (ACH) can be evaluated in patients with femoroacetabular impingement (FAI). The aim was to investigate the pre- and postoperative range of motion (ROM) and the CT bone volume removed (BV) after ACH. We hypothesize increase in ROM 1 year after surgery. Methods Thirteen patients (6 female) with symptomatic FAI were included prospectively. The patient’s hips were CT-scanned and CT-bone models were created. Preoperative dRSA recordings were acquired during passive flexion to 90°, adduction, and internal rotation (FADIR). ACH was performed, CT and dRSA were repeated 3 months and 1 year postoperatively. Hip joint kinematics before, and 3 months and 1 year after ACH were compared pairwise. The bone volume removal was quantified and compared to change in ROM. Results Mean hip internal rotation, adduction and flexion were all unchanged after ACH at 1-year follow-up (p > 0.84). HAGOS scores revealed improvement of quality of life (QOL) from 32 to 60 (p = 0.02). The BV was between 406 and 1783 mm3 and did not correlate to post-operative ROM. Conclusions ACH surgery in FAI patients had no impact of ROM at 1-year follow-up. QOL improved significantly. This indicates that the positive clinical effects reported after ACH might be a result of reduced labral stress and cartilage pressure during end-range motion rather than increased ROM. Level of evidence Therapeutic prospective cohort study, level II.

Biomechanical Assessment of Mobile-Bearing Total Knee Endoprostheses Using Musculoskeletal Simulation

The purpose of this computational study was to analyze the effects of different mobile-bearing (MB) total knee replacement (TKR) designs on knee joint biomechanics. A validated musculoskeletal model of the lower right extremity implanted with a cruciate-retaining fixed-bearing TKR undergoing a squat motion was adapted for three different MB TKR design variants: (I) a commercially available TKR design allowing for tibial insert rotation about the tibial tray with end stops to limit the range of rotation, (II) the same design without end stops, and (III) a multidirectional design with an additional translational degree-of-freedom (DoF) and end stops. When modeling the MB interface, two modeling strategies of different joint topologies were deployed: (1) a six DoF joint as a baseline and (2) a combined revolute-prismatic joint (two DoF joint) with end stops in both DoF. Altered knee joint kinematics for the three MB design variants were observed. The commercially available TKR design variant I yielded a deviation in internal-external rotation of the tibial insert relative to the tray up to 5° during knee flexion. Compared to the multidirectional design variant III, the other two variants revealed less femoral anterior-posterior translation by as much as 5 mm. Concerning the modeling strategies, the two DoF joint showed less computation time by 68%, 80%, and 82% for design variants I, II, and III, respectively. However, only slight differences in the knee joint kinematics of the two modeling strategies were recorded. In conclusion, knee joint biomechanics during a squat motion differed for each of the simulated MB design variants. Specific implant design elements, such as the presence of end stops, can impact the postoperative range of knee motion with regard to modeling strategy, and the two DoF joint option tested accurately replicated the results for the simulated designs with a considerably lower computation time than the six DoF joint. The proposed musculoskeletal multibody simulation framework is capable of virtually characterizing the knee joint dynamics for different TKR designs.

Gastrocnemius medialis contractile behavior during running differs between simulated Lunar and Martian gravities

The international partnership of space agencies has agreed to proceed forward to the Moon sustainably. Activities on the Lunar surface (0.16 g) will allow crewmembers to advance the exploration skills needed when expanding human presence to Mars (0.38 g). Whilst data from actual hypogravity activities are limited to the Apollo missions, simulation studies have indicated that ground reaction forces, mechanical work, muscle activation, and joint angles decrease with declining gravity level. However, these alterations in locomotion biomechanics do not necessarily scale to the gravity level, the reduction in gastrocnemius medialis activation even appears to level off around 0.2 g, while muscle activation pattern remains similar. Thus, it is difficult to predict whether gastrocnemius medialis contractile behavior during running on Moon will basically be the same as on Mars. Therefore, this study investigated lower limb joint kinematics and gastrocnemius medialis behavior during running at 1 g, simulated Martian gravity, and simulated Lunar gravity on the vertical treadmill facility. The results indicate that hypogravity-induced alterations in joint kinematics and contractile behavior still persist between simulated running on the Moon and Mars. This contrasts with the concept of a ceiling effect and should be carefully considered when evaluating exercise prescriptions and the transferability of locomotion practiced in Lunar gravity to Martian gravity.

Use of a Novel Multi-View Image-Based Motion Analysis System in the Field of Sports

Background: Sports-related injuries are the most common in the lower extremities among physical regions, and overall injury rates were higher among males and persons aged 5–24 years. To evaluate impaired functional performance in sports training facilities and sports, a marker-less motion analysis system that can measure joint kinematics in bright indoor and outdoor environments is required. Objective: To establish the concurrent and angle-trajectory validity and intra-trial reliability of a novel multi-view image-based motion analysis system with marker-less during lower extremity tasks in healthy young men.Methods: Ten healthy young men participated voluntarily in this study. The hip and knee joint angles were collected using a multi-view image-based motion analysis system (marker-less) and a Vicon motion capture system (with markers) during the lower extremity tasks. Intraclass correlation coefficient (ICC) analyses were used to identify the concurrent and angle-trajectory validity and intra-trial reliability of the multi-view image-based motion analysis system.Results: In the concurrent validity, the correlation analysis revealed that the ICC3, k values on the hip and knee flexions during knee bending in sitting, standing, and squat movements were 0.747 to 0.936 between the two systems. In particular, the angle-trajectory validity was very high (ICC3, 1 = 0.859–0.998), indicating a high agreement between the two systems. The intra-trial reliability of each system was excellent (ICC3, 1 = 0.773–0.974), reflecting high reproducibility. Conclusion: We suggest that this novel marker-less motion analysis system is highly accurate and reliable for measuring joint kinematics of the lower extremities during the rehabilitation process and monitoring the sports performance of athletes in sports training facilities.

Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach

Dynamic computer tomography (CT) is an emerging modality to analyze in-vivo joint kinematics at the bone level, but it requires manual bone segmentation and, in some instances, landmark identification. The objective of this study is to present an automated workflow for the assessment of three-dimensional in vivo joint kinematics from dynamic musculoskeletal CT images. The proposed method relies on a multi-atlas, multi-label segmentation and landmark propagation framework to extract bony structures and detect anatomical landmarks on the CT dataset. The segmented structures serve as regions of interest for the subsequent motion estimation across the dynamic sequence. The landmarks are propagated across the dynamic sequence for the construction of bone embedded reference frames from which kinematic parameters are estimated. We applied our workflow on dynamic CT images obtained from 15 healthy subjects on two different joints: thumb base (n = 5) and knee (n = 10). The proposed method resulted in segmentation accuracies of 0.90 ± 0.01 for the thumb dataset and 0.94 ± 0.02 for the knee as measured by the Dice score coefficient. In terms of motion estimation, mean differences in cardan angles between the automated algorithm and manual segmentation, and landmark identification performed by an expert were below 1°. Intraclass correlation (ICC) between cardan angles from the algorithm and results from expert manual landmarks ranged from 0.72 to 0.99 for all joints across all axes. The proposed automated method resulted in reproducible and reliable measurements, enabling the assessment of joint kinematics using 4DCT in clinical routine.