The effect of modifying foot progression angle on the knee loading parameters in healthy participants with different static foot postures

Effect of toe-out gait modification on patellofemoral joint loading

BACKGROUND

Toe-out gait has been proposed as a conservative treatment to reduce medial tibiofemoral joint loading. However, patellofemoral joint loading during toe-out gait is not yet understood.

RESEARCH QUESTION

Does the toe-out gait modification affect patellofemoral joint loading?

METHODS

Sixteen healthy adults were enrolled in this study. The natural gait and toe-out gait were measured using a three-dimensional motion analysis and a force plate. The knee flexion angle and external knee flexion moment during the stance phase were calculated. Thus, dynamic knee joint stiffness, a proxy of patellofemoral joint loading, was defined as a linear regression of the knee flexion moment and knee flexion angle during the early stance. Additionally, the peak patellofemoral compressive force during the early stance was calculated using a musculoskeletal simulation. A paired t-test was used to compare these biomechanical parameters during the natural gait and toe-out gait.

RESULTS

The toe-out gait significantly increased the peak patellofemoral compressive force (mean difference = 0.37 BW, P = 0.017) and dynamic knee joint stiffness (mean difference = 0.07%BW*Ht/°, P = 0.001). The 1st peak of the knee flexion moment also significantly increased in the toe-out gait (mean difference = 1.01%BW*Ht, P = 0.003); however, the knee flexion angle did not change significantly (initial contact: mean difference = 1.7°, P = 0.078; peak: mean difference = 1.3°, P = 0.224).

SIGNIFICANCE

Toe-out gait increased the patellofemoral compressive force and dynamic knee joint stiffness because of increasing knee flexion moment, but not the knee flexion angle. When the toe-out gait is adapted, clinicians should pay attention to an increase in the patellofemoral joint loading.

Effect of foot progression angle alteration on medial-lateral center of pressure position during single-leg standing

[Purpose] This study aimed to investigate the effect of altering the foot progression angle (FPA) on the center of pressure (COP) position during single-leg standing. [Participants and Methods] Fifteen healthy adult males participated. The participants performed single-leg standing on the left leg in three different FPA conditions, namely toe-in, neutral, and toe-out, in which the FPA was set to 0°, 10°, and 20°, respectively. The COP positions and pelvis angles were measured using a 3D motion analysis system, and each measurement value among the three conditions was compared. [Results] The medial-lateral COP position differed among conditions in the coordinate system based on the laboratory condition but did not differ in the coordinate system based on the longitudinal axis of the foot segment. Moreover, no changes were observed in pelvis angles that would affect COP position. [Conclusion] Altering the FPA does not change the medial-lateral position of the COP during single-leg standing. Here we show that COP displacement based on the laboratory coordinate system is involved in the mechanism-linking alteration of FPA and changes in knee adduction moment.

Detecting Gait Events from Accelerations Using Reservoir Computing

Segmenting the gait cycle into multiple phases using gait event detection (GED) is a well-researched subject with many accurate algorithms. However, the algorithms that are able to perform accurate and robust GED for real-life environments and physical diseases tend to be too complex for their implementation on simple hardware systems limited in computing power and memory, such as those used in wearable devices. This study focuses on a numerical implementation of a reservoir computing (RC) algorithm called the echo state network (ESN) that is based on simple computational steps that are easy to implement on portable hardware systems for real-time detection. RC is a neural network method that is widely used for signal processing applications and uses a fast-training method based on a ridge regression adapted to the large quantity and variety of IMU data needed to use RC in various real-life environment GED. In this study, an ESN was used to perform offline GED with gait data from IMU and ground force sensors retrieved from three databases for a total of 28 healthy adults and 15 walking conditions. Our main finding is that despite its low complexity, ESN is robust for GED, with performance comparable to other state-of-the-art algorithms. Our results show the ESN is robust enough to obtain good detection results in all conditions if the algorithm is trained with variable data that match those conditions. The distribution of the mean absolute errors (MAE) between the detection times from the ESN and the force sensors were between 40 and 120 ms for 6 defined gait events (95th percentile). We compared our ESN with four different state-of-the-art algorithms from the literature. The ESN obtained a MAE not more than 10 ms above three other reference algorithms for normal walking indoor and outdoor conditions and yielded the 2nd lowest MAE and the 2nd highest true positive rate and specificity when applied to outdoor walking and running conditions. Our work opens the door to using the ESN as a GED for applications in wearable sensors for long-term patient monitoring.

Wearable Real-Time Haptic Biofeedback Foot Progression Angle Gait Modification to Assess Short-Term Retention and Cognitive Demand

Foot progression angle gait (FPA) modification is an important part of rehabilitation for a variety of neuromuscular and musculoskeletal diseases. While wearable haptic biofeedback could enable FPA gait modification for more widespread use than traditional tethered, laboratory-based approaches, retention, and cognitive demand in FPA gait modification via wearable haptic biofeedback are currently unknown and may be important to real-life implementation. Thus, the purpose of this study was to assess the feasibility of wearable haptic biofeedback to assess short-term retention and cognitive demand during FPA gait modification. Ten healthy participants performed toe-in (target 10 degrees change in internal rotation) and toe-out (target 10 degrees change in external rotation) haptic gait training trials followed by short-term retention trials, and cognitive multitasking trials. Results showed that participants were able to initially respond to the wearable haptic feedback to modify their FPA to adopt the new toe-in (9.7 ± 0.8 degree change in internal rotation) and toe-out (8.9 ± 1.0 degree change in external rotation) gait patterns. Participants retained the modified gait pattern on average within 3.9 ± 3.6 deg of the final haptic gait training FPA values. Furthermore, cognitive multitasking did not influence short-term retention in that there were no differences in gait performance during retention trials with or without cognitive multitasking. These results demonstrate that wearable haptic biofeedback can be used to assess short-term retention and cognitive demand during FPA gait modification without the need for traditional, tethered systems.