Quantifying bi-variate coordination variability during longitudinal motor learning of a complex skill

Sensor-based augmented visual feedback for coordination training in healthy adults: a scoping review

Introduction

Recent advances in sensor technology demonstrate the potential to enhance training regimes with sensor-based augmented visual feedback training systems for complex movement tasks in sports. Sensorimotor learning requires feedback that guides the learning process towards an optimal solution for the task to be learned, while considering relevant aspects of the individual control system-a process that can be summarized as learning or improving coordination. Sensorimotor learning can be fostered significantly by coaches or therapists providing additional external feedback, which can be incorporated very effectively into the sensorimotor learning process when chosen carefully and administered well. Sensor technology can complement existing measures and therefore improve the feedback provided by the coach or therapist. Ultimately, this sensor technology constitutes a means for autonomous training by giving augmented feedback based on physiological, kinetic, or kinematic data, both in real-time and after training. This requires that the key aspects of feedback administration that prevent excessive guidance can also be successfully automated and incorporated into such electronic devices.

Methods

After setting the stage from a computational perspective on motor control and learning, we provided a scoping review of the findings on sensor-based augmented visual feedback in complex sensorimotor tasks occurring in sports-related settings. To increase homogeneity and comparability of the results, we excluded studies focusing on modalities other than visual feedback and employed strict inclusion criteria regarding movement task complexity and health status of participants.

Results

We reviewed 26 studies that investigated visual feedback in training regimes involving healthy adults aged 18-65. We extracted relevant data regarding the chosen feedback and intervention designs, measured outcomes, and summarized recommendations from the literature.

Discussion

Based on these findings and the theoretical background on motor learning, we compiled a set of considerations and recommendations for the development and evaluation of future sensor-based augmented feedback systems in the interim. However, high heterogeneity and high risk of bias prevent a meaningful statistical synthesis for an evidence-based feedback design guidance. Stronger study design and reporting guidelines are necessary for future research in the context of complex skill acquisition.

A Wearable-Sensor System with AI Technology for Real-Time Biomechanical Feedback Training in Hammer Throw

Developing real-time biomechanical feedback systems for in-field applications will transfer human motor skills' learning/training from subjective (experience-based) to objective (science-based). The translation will greatly improve the efficiency of human motor skills' learning and training. Such a translation is especially indispensable for the hammer-throw training which still relies on coaches' experience/observation and has not seen a new world record since 1986. Therefore, we developed a wearable wireless sensor system combining with artificial intelligence for real-time biomechanical feedback training in hammer throw. A framework was devised for developing such practical wearable systems. A printed circuit board was designed to miniaturize the size of the wearable device, where an Arduino microcontroller, an XBee wireless communication module, an embedded load cell and two micro inertial measurement units (IMUs) could be inserted/connected onto the board. The load cell was for measuring the wire tension, while the two IMUs were for determining the vertical displacements of the wrists and the hip. After calibration, the device returned a mean relative error of 0.87% for the load cell and the accuracy of 6% for the IMUs. Further, two deep neural network models were built to estimate selected joint angles of upper and lower limbs related to limb coordination based on the IMUs' measurements. The estimation errors for both models were within an acceptable range, i.e., approximately ±12° and ±4°, respectively, demonstrating strong correlation existed between the limb coordination and the IMUs' measurements. The results of the current study suggest a remarkable novelty: the difficulty-to-measure human motor skills, especially in those sports involving high speed and complex motor skills, can be tracked by wearable sensors with neglect movement constraints to the athletes. Therefore, the application of artificial intelligence in a wearable system has shown great potential of establishing real-time biomechanical feedback training in various sports. To our best knowledge, this is the first practical research of combing wearables and machine learning to provide biomechanical feedback in hammer throw. Hopefully, more wearable biomechanical feedback systems integrating artificial intelligence would be developed in the future.

Angular dynamics in vector coding: a new approach based on angular velocity

The assessment of coordination variability in multi-joint human movements has traditionally started from angle-angle representations, and then used the angle change between subsequent time points as input for further analysis through vector coding. We propose an improvement to this approach, and suggest employing angular velocities as input data (Velocity Ellipse Method, VEM). We used experimental data and theoretical principles to contrast VEM with an existing standard (Difference Ellipse Method) and discuss its advantages and potential issues. Normalised cross-correlation was used to compare VEM and DEM in 36 angle couplings, from 20 participants running at 12 km/h on a treadmill. The hip flexion/extension-knee flexion/extension data were further investigated to discuss the robustness of the approach to measurement noise and outliers. Although DEM and VEM generally exhibited similar patterns (cross-correlation between 0.851 and 0.999), the variability curves from the two methods were noticeably different in some intervals. Also, using angular velocities as input appeared more robust to potential noise from raw data whilst retaining the following features: (a) more coherent with biomechanical conventions for calculating three-dimensional angular dynamics; (b) still suitable for coordination analysis; and, (c) more easily interpretable by practitioners when represented as relative motion plots.

Differences between adults and adolescents in responding to hip and knee pattern feedback during gait

Visual feedback techniques have shown potential in interventions aiming to retrain deviated gait patterns. Understanding the abilities of different age groups to modify their gait is needed to optimize interventions. Twelve adults (6F, 6 M; 26.3 ± 5.9 yrs.) and twelve adolescents (4F, 8 M; 13.6 ± 2.3 yrs) without disabilities participated in one training session. During the session, the responses to a visual kinematic feedback task in which one hip or knee target pattern was modified while unmodified target patterns were maintained in the other hip and knee joints were investigated. Limb orientation and acceleration data were collected using Inertial Measurement Units (IMU) (Xsens Awinda, Enschede, The Netherlands) with a sampling frequency of 60 Hz. Adults tended to outperform adolescents in tracking modified target patterns and showed smaller errors in unmodified regions of modified patterns (p = 0.045); they also outperformed adolescents in unmodified joints (Contralateral Hip: p = 0.003; Contralateral Knee: p = 0.002; Ipsilateral Joint: p = 0.048). These findings suggest different levels of awareness of the need and/or ability to minimize errors across joints, in turn suggesting the need for specialization of training for these age groups.

Training for Muscular Strength: Methods for Monitoring and Adjusting Training Intensity

Linear loading, the two-for-two rule, percent of one repetition maximum (1RM), RM zones, rate of perceived exertion (RPE), repetitions in reserve, set-repetition best, autoregulatory progressive resistance exercise (APRE), and velocity-based training (VBT) are all methods of adjusting resistance training intensity. Each method has advantages and disadvantages that strength and conditioning practitioners should be aware of when measuring and monitoring strength characteristics. The linear loading and 2-for-2 methods may be beneficial for novice athletes; however, they may be limited in their capacity to provide athletes with variation and detrimental if used exclusively for long periods of time. The percent of 1RM and RM zone methods may provide athletes with more variation and greater potential for strength-power adaptations; however, they fail to account for daily changes in athlete's performance capabilities. An athlete's daily readiness can be addressed to various extents by both subjective (e.g., RPE, repetitions in reserve, set-repetition best, and APRE) and objective (e.g., VBT) load adjustment methods. Future resistance training monitoring may aim to include a combination of measures that quantify outcome (e.g., velocity, load, time, etc.) with process (e.g., variability, coordination, efficiency, etc.) relevant to the stage of learning or the task being performed. Load adjustment and monitoring methods should be used to supplement and guide the practitioner, quantify what the practitioner 'sees', and provide longitudinal data to assist in reviewing athlete development and providing baselines for the rate of expected development in resistance training when an athlete returns to sport from injury or large training load reductions.

Ankle-knee coupling responses to ankle Kinesio™ taping during single-leg drop landings in collegiate athletes with chronic ankle instability

BACKGROUND

Ankle Kinesio-taping (KT) is being globally used an intervention to provide the ankle joint complex with sufficient support against sudden excessive mechanical stress during various activities. However, its effects on proximal joints are unclear. This study investigated the impact of ankle KT on ankle-knee joint coupling in sagittal, frontal and transverse planes.

METHODS

Adopting a pretest post-test study design, 30 collegiate athletes with chronic ankle instability performed 3 single-leg drop landings in each non-taped and Kinesio-taped conditions and their movement kinematics were recorded using 6 optoelectronic cameras.

RESULTS

The ankle angular velocities in sagittal (P=0.038, d=0.64) and transverse planes (P=0.001, d=0.95) decreased after KT application, while the knee internal rotation velocities increased (P=0.020, d=0.51). The coupling angles revealed that the ankle movement ratios significantly decreased in 3 planes in comparison with knee movement ratios.

CONCLUSIONS

Outcomes of this study illustrated that application of ankle KT leaves the individuals with a stiffer ankle joint, which increases the mechanical stresses to this joint and decreases its stiffness in absorbing the applied shocks. Further, ankle KT application resulted in more knee internal rotation moments and may increase the risk of knee injuries during landing after a long-term usage in patients with instability ankle sprain.

A real-time feedback method to reduce loading rate during running: Effect of combining direct and indirect feedback

Impact loading plays a key role in the pathophysiology of running-related injuries. Providing real-time feedback may be an effective strategy to reduce impact loading; however, it is currently unclear what an effective training method to help runners achieve a habitual low loading rate is. We subjected 20 healthy non-runners to a structured sequence of direct and indirect biofeedback designed to facilitate broader exploration of neuro-mechanical workspace for potential movement solutions (indirect feedback on cadence and foot-strike angle) and to refine and converge upon an optimal sub-set of that space to match the task goal (direct feedback on loading rate). While indirect biofeedback on foot-strike angle yielded a lower impact load than providing direct biofeedback on loading rate, compared to indirect biofeedback on foot-strike angle, providing direct feedback on loading rate statistically increased (+58%, p = 0.007) the range of goal-relevant solutions participants used to lower their impact loading. Results showed that structured feedback was effective in increasing the range of input parameters that match the task goal, hence expanding the size of goal-relevant solutions, which may benefit running performance under changing environmental constraints.