Tibial acceleration-based prediction of maximal vertical loading rate during overground running : a machine learning approach

Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical risk-factors of running related injuries or athletic performance during a running analysis. An interesting ground reaction force-derived variable to track is the maximal vertical instantaneous loading rate (VILR). This impact characteristic is traditionally derived from a fixed force platform, but wearable inertial sensors nowadays might approximate its magnitude while running outside the lab. The time-discrete axial peak tibial acceleration (APTA) has been proposed as a good surrogate that can be measured using wearable accelerometers in the field. This paper explores the hypothesis that applying machine learning to time continuous data (generated from bilateral tri-axial shin mounted accelerometers) would result in a more accurate estimation of the VILR. Therefore, the purpose of this study was to evaluate the performance of accelerometer-based predictions of the VILR with various machine learning models trained on data of 93 rearfoot runners. A subject-dependent gradient boosted regression trees (XGB) model provided the most accurate estimates (mean absolute error: 5.39 +/- 2.04 BW.s(-1), mean absolute percentage error: 6.08%). A similar subject-independent model had a mean absolute error of 12.41 +/- 7.90 BW.s(-1) (mean absolute percentage error: 11.09%). All of our models had a stronger correlation with the VILR than the APTA (p < 0.01), indicating that multiple 3D acceleration features in a learning setting showed the highest accuracy in predicting the lab-based impact loading compared to APTA

Exploring machine learning, real-time bio-feedback, and inertial sensor accuracy for the prevention of running-related injuries

Recreational running is popular, however, incident rates of running related injuries (RRIs) are very high. Predisposition to injury can be assessed through expensive, laboratory-based biomechanical screening. Wearable wireless inertial sensors offer a potential solution, but accurate orientation data are required. This thesis examined the prevention of RRIs, by aiming to improve sensor accuracy, and investigate applications of biofeedback and machine learning. This thesis explored improving (magnetometer-free) orientation accuracy during running, through examination of (i) Z-axis de-drifting, (ii) data-loss (iii) and modifications to the Madgwick filter. Despite some accuracy improvements (i, iii), overall errors were unsuitable for running based applications. Impact loading is associated with RRIs, with thigh angle (quasi-measure of knee-flexion) potentially important in load attenuation. Loading can be altered directly (loading-based biofeedback) or indirectly (technique-based biofeedback), these two types of biofeedback were compared. A mobile phone application was developed providing audio biofeedback to reduce impact accelerations and encourage a ‘softer’ running technique. Both types of feedback reduced loading at the tibia and sacrum, however, tibia loading reduced better with impact accelerations biofeedback, and sacrum loading with thigh angle biofeedback. It would be beneficial to identify runners who may be predisposed to injury. Seven supervised machine learning models were developed to identify runners who may be likely to sustain RRIs, using inertial, kinematic and clinical data collected on 150 prospectively tracked runners. These models resulted in weak predictive accuracy (0.58-0.61 AUC). As we cannot identify runners predisposed to injury, all runners must be recommended for injury prevention interventions. Orientation accuracy was found to be sufficient for relative measures of running technique in the biofeedback app. Future work could investigate biofeedback app use in relation to reduction of RRIs. Additionally, running injury prediction could be examined further with respect to extracting different features (continuous measures) or predicting specific injuries

The Use of Wearable Sensors for Preventing, Assessing, and Informing Recovery from Sport-Related Musculoskeletal Injuries: A Systematic Scoping Review

Wearable technologies are often indicated as tools that can enable the in-field collection of quantitative biomechanical data, unobtrusively, for extended periods of time, and with few spatial limitations. Despite many claims about their potential for impact in the area of injury prevention and management, there seems to be little attention to grounding this potential in biomechanical research linking quantities from wearables to musculoskeletal injuries, and to assessing the readiness of these biomechanical approaches for being implemented in real practice. We performed a systematic scoping review to characterise and critically analyse the state of the art of research using wearable technologies to study musculoskeletal injuries in sport from a biomechanical perspective. A total of 4952 articles were retrieved from the Web of Science, Scopus, and PubMed databases; 165 were included. Multiple study features—such as research design, scope, experimental settings, and applied context—were summarised and assessed. We also proposed an injury-research readiness classification tool to gauge the maturity of biomechanical approaches using wearables. Five main conclusions emerged from this review, which we used as a springboard to propose guidelines and good practices for future research and dissemination in the field

Predicting coordination variability of selected lower extremity couplings during a cutting movement:an investigation of deep neural networks with the LSTM structure

There are still few portable methods for monitoring lower limb joint coordination during the cutting movements (CM). This study aims to obtain the relevant motion biomechanical parameters of the lower limb joints at 90°, 135°, and 180° CM by collecting IMU data of the human lower limbs, and utilizing the Long Short-Term Memory (LSTM) deep neural-network framework to predict the coordination variability of selected lower extremity couplings at the three CM directions. There was a significant (p < 0.001) difference between the three couplings during the swing, especially at 90° vs the other directions. At 135° and 180°, t13-he coordination variability of couplings was significantly greater than at 90° (p < 0.001). It is important to note that the coordination variability of Hip rotation/Knee flexion-extension was significantly higher at 90° than at 180° (p < 0.001). By the LSTM, the CM coordination variability for 90° (CMC = 0.99063, RMSE = 0.02358), 135° (CMC = 0.99018, RMSE = 0.02465) and 180° (CMC = 0.99485, RMSE = 0.01771) were accurately predicted. The predictive model could be used as a reliable tool for predicting the coordination variability of different CM directions in patients or athletes and real-world open scenarios using inertial sensors

XXII International Conference on Mechanics in Medicine and Biology - Abstracts Book

This book contain the abstracts presented the XXII ICMMB, held in Bologna in September 2022. The abstracts are divided following the sessions scheduled during the conference

On the Impact and Detection of Biceps Muscle Fatigue in Wearable Sensors-Based Human Activity Recognition

Nowadays, modern sport and athletic training are very interested in wearable-based Human Activity Recognition (HAR) systems due to their cost-efficiency, portability, and convenience. However, this leads the developers to compete in developing the various HAR applications with little attention to HAR's-related problems such as fatigue. In this thesis, we select the bicep curls as an example of a HAR activity to study the fatigue problem in wearable-based HAR. We approach the fatigue problem through three studies: first, we study the impact of fatigue in wearable-based HAR. Second, we detect the presence of fatigue during human activity, e.g., biceps curls exercise. Third, we improve the performance of fatigue detection models while reducing the test's data consumption. Throughout our studies, we use our dataset, which consists of 3,750 repetitions of biceps curls from twenty-five volunteers between 20–46 years and with body mass index (BMI) between 24–46. Our first study on the impact of fatigue in wearable-based HAR shows that fatigue often occurs in later sets of biceps curls. During fatigue, the completion time of later sets extends by up to 31%, while muscular endurance decreases by 4.1%. Also, our study shows that changes in data patterns often occur during fatigue, turning some features to be statistically insignificant. This can lead to a substantial decrease in performance in both subject-specific and cross-subject models. In addition, muscle fatigue can lead to various injuries such as muscle strain and tendons rupture, which may require up to 22 weeks of treatment. Therefore, it is essential to be aware of fatigue during human activity, which we address in our second study. The second study proposes a wearable-based approach to detect fatigue in biceps curls. We provide a set of 16 most fatigue representative features from 33 extracted features. Then, we employ these features in five models to detect fatigue in biceps curls. Our study shows that a two-layer FNN achieves the highest accuracy of 98\% and 88\% for subject-specific and cross-subject models, respectively. We observe that the cross-subject models are preferable for a large crowd since these models can utilize crowd data. However, we observe that inter-subject data variability is usually high in the large crowd due to the physical differences among the individuals, resulting in different data patterns for the same activities. As a result, researchers may suggest using subject-specific models for each user in the crowd to achieve higher performance. Still, such a performance comes with a higher data cost of the user's subject-specific model; therefore, improving fatigue detection in cross-subject models is essential, which is the goal of our third study. In the third study, we propose a personalization approach as a solution to improve the cross-subject models' performance by utilizing data from the crowd based on similarities between the test subject and users from the crowd. We extract 11 hand-crafted features to measure the similarities between the test subject and the individuals in the crowd. Then, we employ these similarities to prioritize and select the training data from the crowd for two cross-subject models. Our study shows that the personalization approach improves the performance of the cross-subject models in terms of precision by up to 7.25%, recall by up to 5.69%, accuracy by up to 6.67%, and F1-measure by up to 6.52%. Furthermore, adding 20% of the test subject's data into the training dataset of the personalized cross-subject models can produce accurate results closer to the ones from subject-specific models

Towards an automated weight lifting coach: introducing LIFT

The fitness device market is young and rapidly growing. More people than ever before take count of how many steps they walk, how many calories they burn, their heart rate over time, and even their quality of sleep. New, and as of yet, unreleased fitness devices have promised the next evolution of functionality with exercise technique analysis. These next generation of fitness devices have wrist and armband style form factors, which may not be optimal for barbell exercises such as back squat, bench press, and overhead press where a sensor on one arm may not provide the most relevant data about a lift. Barbell path analysis is a well-known visual tool to help diagnose weightlifting technique deficiencies, but requires a camera pointed at the athlete that is integrated with motion-tracking software. This camera set up is not available at most gyms, so this motivates the use of a small, unobtrusive sensor to obtain data about an athlete\u27s weightlifting technique. Researchers have shown that an accelerometer attached to a barbell while the athlete is lifting yields just as accurate acceleration information as a camera. The LIFT (Leveraging Information For Training) automated weight lifting coach attempts to implement a simple, unobtrusive system for analyzing and providing feedback on barbell weight lifting technique