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.

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.

Abnormal synergistic gait mitigation in acute stroke using an innovative ankle–knee–hip interlimb humanoid robot: a preliminary randomized controlled trial

Abnormal spasticity and associated synergistic patterns are the most common neuromuscular impairments affecting ankle–knee–hip interlimb coordinated gait kinematics and kinetics in patients with hemiparetic stroke. Although patients with hemiparetic stroke undergo various treatments to improve gait and movement, it remains unknown how spasticity and associated synergistic patterns change after robot-assisted and conventional treatment. We developed an innovative ankle–knee–hip interlimb coordinated humanoid robot (ICT) to mitigate abnormal spasticity and synergistic patterns. The objective of the preliminary clinical trial was to compare the effects of ICT combined with conventional physical therapy (ICT-C) and conventional physical therapy and gait training (CPT-G) on abnormal spasticity and synergistic gait patterns in 20 patients with acute hemiparesis. We performed secondary analyses aimed at elucidating the biomechanical effects of Walkbot ICT on kinematic (spatiotemporal parameters and angles) and kinetic (active force, resistive force, and stiffness) gait parameters before and after ICT in the ICT-C group. The intervention for this group comprised 60-min conventional physical therapy plus 30-min robot-assisted training, 7 days/week, for 2 weeks. Significant biomechanical effects in knee joint kinematics; hip, knee, and ankle active forces; hip, knee, and ankle resistive forces; and hip, knee, and ankle stiffness were associated with ICT-C. Our novel findings provide promising evidence for conventional therapy supplemented by robot-assisted therapy for abnormal spasticity, synergistic, and altered biomechanical gait impairments in patients in the acute post-stroke recovery phase.Trial Registration: Clinical Trials.gov identifier NCT03554642 (14/01/2020).

Biomechanical Effects of Flamenco Footwork

Footwork is one of the basic features of flamenco dancing and is performed in traditional high-heeled shoes. The purpose of this study was to analyse the mechanical profile of flamenco dancing in terms of vertical ground reaction force, and knee joint kinematics of the supporting limb in footwork technique in order to understand causes which predispose injuries derived from the practice of flamenco dancing. The participant in our study was a professional female flamenco dancer (34 years, 58 kg, 1.65 m) who performed the ZAP 3 test, a sequence of single strikes of the feet performed continuously for 15 s. 3D lower extremity kinematic data were collected using a five-camera motion analysis system (Vicon; Oxford Metrics Ltd., Oxford, UK). Ground reaction forces were recorded using a Kistler force plate. Our analysis was based on 30 cycles of each lower limb consisting of 177 footwork steps. The vertical component of the ground reaction force did not reveal any significant differences between the left and the right limb. The most dynamic strike was provided by the heel (twice the participant's body weight). The mean angular displacement of the supporting limb’s knee was ~27°. Results reveal that these impacts could make the knee joint more prone to injuries.

Effect of Lower Extremity Fatigue on Knee Joint Kinematics During Landing Maneuvers in Adult Soccer Players

Background and Aims: Since fatigue in different regions of the lower extremities can effectively alter the movement pattern of this part of the body and cause joint-related kinematic changes that increase the risk of injury and mental fatigue, which may be a factor in reducing productivity and injury. Therefore, the study of kinematic changes during fatigue can have helpful results. This study aimed to investigate the effect of lower extremity fatigue on knee joint kinematics during landing in adult soccer players. Methods: Ten adult male soccer players (Mean±SD = age: 20.7±1.05 years; Height: 178.9±4.17 cm; Weight: 71.55±8.04 kg) participated in this study. Subjects performed a pre-test, which included jumping and landing on a 40-cm box, and recording cameras of the valgus motion analyzer, flexion, and knee rotation. The Kingtools section of the plugin software attached to the Cortex software was used for segmentation and kinematic information. The lower extremity fatigue protocol consisted of 10 repetitions of single-leg squats up to 90 degrees of knee flexion, 20 vertical jumps with a single leg, and 1 repetition of step –going up and down a 31cm step. The Borg scale was used to measure fatigue. Before and after the fatigue protocol, a single-leg hop was used to determine the level of fatigue. After the fatigue protocol, a post-test was performed. Data were analyzed using the Shapiro-Wilk test for normality of the data, and paired t-test was used to compare mean in pre-test and post-test independent variables. Results: The paired t-test results for comparison of kinematic data showed that contact flexion had a significant difference from pre-test to post-test and other kinematic variables had no significant changes from pre-test to post-test. Statistical significance was considered at P≤0.05. Conclusion: According to the findings of this study, it can be concluded that lower extremity fatigue in the present study partly caused kinematic changes in predicting ACL injury. Kinematic changes have included a decrease in Contact flexion, which is one of the predictors of ACL injury during landing.

Closing the Wearable Gap—Part VIII: A Validation Study for a Smart Knee Brace to Capture Knee Joint Kinematics

Background: Wearable technology is used by clinicians and researchers and play a critical role in biomechanical assessments and rehabilitation. Objective: The purpose of this research is to validate a soft robotic stretch (SRS) sensor embedded in a compression knee brace (smart knee brace) against a motion capture system focusing on knee joint kinematics. Methods: Sixteen participants donned the smart knee brace and completed three separate tasks: non-weight bearing knee flexion/extension, bodyweight air squats, and gait trials. Adjusted R2 for goodness of fit (R2), root mean square error (RMSE), and mean absolute error (MAE) between the SRS sensor and motion capture kinematic data for all three tasks were assessed. Results: For knee flexion/extension: R2 = 0.799, RMSE = 5.470, MAE = 4.560; for bodyweight air squats: R2 = 0.957, RMSE = 8.127, MAE = 6.870; and for gait trials: R2 = 0.565, RMSE = 9.190, MAE = 7.530 were observed. Conclusions: The smart knee brace demonstrated a higher goodness of fit and accuracy during weight-bearing air squats followed by non-weight bearing knee flexion/extension and a lower goodness of fit and accuracy during gait, which can be attributed to the SRS sensor position and orientation, rather than range of motion achieved in each task.

Abnormal Synergistic Gait Mitigation after Acute Stroke Using an Innovative Ankle-Knee-Hip Interlimb Humanoid Robot: A Randomized Controlled Trial

Abnormal spasticity and associated synergistic patterns are the most common neuromuscular impairments affecting ankle-knee-hip interlimb coordinated gait kinematics and kinetics in patients with hemiparetic stroke. Although patients with hemiparetic stroke undergo various treatments to improve gait and movement, it remains unknown how spasticity and associated synergistic patterns change after robot-assisted and conventional treatment. We developed an innovative ankle-knee-hip interlimb coordinated humanoid robot (ICT) to mitigate abnormal spasticity and synergistic patterns. Our study aimed to compare the effects of ICT combined conventional physical therapy (ICT-C) and conventional physical therapy and gait training (CPT-G) on abnormal spasticity, synergistic gait patterns, and gait biomechanics in 20 patients with acute hemiparesis. The interventions were provided in 60-min sessions, 7 days/week, for 2 weeks. ICT-C demonstrated superior effects to CPT-G in knee joint kinematics, hip extensor and ankle dorsiflexor spasticity, hip, knee, ankle active force, knee and ankle resistive force, hip, knee, and ankle stiffness. Concurrently, the abnormal gait synergy was reduced in the ICT-C group. Our novel findings provided promising evidence of the ICT benefits as a successful intervention for abnormal spasticity and synergistic gait impairments in patients with acute hemiparetic stroke.Trial Registration: Clinical Trials.gov identifier NCT03554642 (14/01/2020)

Muscle activity of cutting manoeuvres and soccer inside passing suggests an increased groin injury risk during these movements

Cutting manoeuvres and inside passing are thought to increase the risk of sustaining groin injuries. But both movements have received little research attention in this regard. The purpose of this study was to investigate the muscle activity of adductor longus and gracilis as well as hip and knee joint kinematics during $$90^{\circ }$$ 90 ∘ -cutting and inside passing. Thirteen male soccer players were investigated with 3D-motion capturing and surface electromyography of adductor longus and gracilis while performing the two movements. Hip and knee joint kinematics were calculated with AnyBody Modelling System. Muscle activity of both muscles was significantly higher during the cutting manoeuvre compared to inside passing. Kinematics showed that the highest activity occurred during phases of fast muscle lengthening and eccentric contraction of the adductors which is known to increase the groin injury risk. Of both movements, cutting showed the higher activity and is therefore more likely to cause groin injuries. However, passing might also increase the risk for groin injuries as it is one of the most performed actions in soccer, and therefore most likely causes groin injuries through overuse. Practitioners need to be aware of these risks and should prepare players accordingly through strength and flexibility training.