FEATURE SELECTION FOR THE APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN MOTION ANALYSIS

The application of IMUs and artificial neural networks have shown their potential in estimating joint moments in various motion tasks. In this study, IMU data collected with five sensors during gait was used as input data to estimate hip, knee and ankle joint moments using artificial neural networks. Additionally, the original 30 features of the sensors’ data were reduced to their ten most relevant principal components and also used as input to the neural networks to evaluate the influence of feature selection. The prediction accuracy of the networks was lower for the reduced dataset. Research with a larger dataset needs to be undertaken to further understand the influence of a reduced number of features on the prediction accuracy

Multidimensional ground reaction forces and moments from wearable sensor accelerations via deep learning

Objective: Monitoring athlete internal workload exposure, including prevention of catastrophic non-contact knee injuries, relies on the existence of a custom early-warning detection system. This system must be able to estimate accurate, reliable, and valid musculoskeletal joint loads, for sporting maneuvers in near real-time and during match play. However, current methods are constrained to laboratory instrumentation, are labor and cost intensive, and require highly trained specialist knowledge, thereby limiting their ecological validity and volume deployment. Methods: Here we show that kinematic data obtained from wearable sensor accelerometers, in lieu of embedded force platforms, can leverage recent supervised learning techniques to predict in-game near real-time multidimensional ground reaction forces and moments (GRF/M). Competing convolutional neural network (CNN) deep learning models were trained using laboratory-derived stance phase GRF/M data and simulated sensor accelerations for running and sidestepping maneuvers derived from nearly half a million legacy motion trials. Then, predictions were made from each model driven by five sensor accelerations recorded during independent inter-laboratory data capture sessions. Results: Despite adversarial conditions, the proposed deep learning workbench achieved correlations to ground truth, by GRF component, of vertical 0.9663, anterior 0.9579 (both running), and lateral 0.8737 (sidestepping). Conclusion: The lessons learned from this study will facilitate the use of wearable sensors in conjunction with deep learning to accurately estimate near real-time on-field GRF/M. Significance: Coaching, medical, and allied health staff can use this technology to monitor a range of joint loading indicators during game play, with the ultimate aim to minimize the occurrence of non-contact injuries in elite and community-level sports

機械学習による歩行中の下肢関節キネティクスの推定

歩行動作の分析において，下肢の関節トルクや関節トルクパワー（以下JT，JTP）はキネティクス分析に広く用いられている（Neckel, 2008; Rozumalski, 2011）．これらのキネティクス変量を算出するためには地面反力を必要とし，その計測には一般的にフォースプラットフォーム（以下FP）が用いられる．しかし，FPを用いた動作の計測は計測機器による制約を受けてしまう．そのため，歩行動作分析を行う際，FPを用いずにキネティクス分析が可能になることは重要である．そこでOhら（2013）やLimら（2019）はFPを用いずに歩行中のJTなどを推定する方法を検証した．両研究ともに，矢状面のJTはすべての関節において%RMSE10% 前後で推定されたと報告している．しかし，これらの研究は被験者数が非常に少なく妥当性の検証が不十分であることや，モデル設計の原理が曖昧であった．そこで本研究は，幅広い被験者に対して適用可能な歩行中の下肢JTおよびJTPの推定方法を検討し，推定精度を検証することを目的とした．本研究ではモデルの学習のため被験者300名計2909試技（通常歩行）のデータセットを用いた．また，モデルデータとは異なる環境で計測された74名148試技の外部データ1および12名95試技の外部データ2をモデルの精度検証のため用いた．また，JTの推定のため，セグメントの並進および角加速度を入力変数として用いたInverse Dynamics モデルと関節角度を入力変数として用いたJoint Angleモデルの2つの学習モデルを設計した．設計されたモデルにより推定されたJTは横断面における足関節のJTを除く全てのJTで真値との相関係数が0.90以上（ID:0.94~0.98，JA:0.93~0.99），矢状面におけるJTは%RMSE10%前後（ID:7.2~11.7%，JA:6.6~11.1%）であった．推定値により計算されたJTPは全て真値との相関係数が0.90以上（ID:0.93~0.98, JA:0.92~0.99），%RMSE10%前後（ID:5.7~10.1%，JA:5.5~9.9%）であった．また，外部データにおいて，特に矢状面のJTは一定以上の精度で推定可能であることが分かった．また，モデルデータとは異なる年齢層に対する推定精度に差はみられなかった．しかし，通常歩行以外の歩行速度の試技に対して適用する場合，股および膝関節トルクの推定精度が低下することが分かった．以上の結果より，幅広い被験者に対応できるモデルを設計したが，通常歩行とは異なる歩行速度に対しては注意が必要であることが示唆された．電気通信大学202

Patient Movement Monitoring Based on IMU and Deep Learning

Osteoarthritis (OA) is the leading cause of disability among the aging population in the United States and is frequently treated by replacing deteriorated joints with metal and plastic components. Developing better quantitative measures of movement quality to track patients longitudinally in their own homes would enable personalized treatment plans and hasten the advancement of promising new interventions. Wearable sensors and machine learning used to quantify patient movement could revolutionize the diagnosis and treatment of movement disorders. The purpose of this dissertation was to overcome technical challenges associated with the use of wearable sensors, specifically Inertial Measurement Units (IMUs), as a diagnostic tool for osteoarthritic (OA) and total knee replacement patients (TKR) through a detailed biomechanical assessment and development of machine learning algorithms. Specifically, the first study developed a relevant dataset consisting of IMU and associated biomechanical parameters of OA and TKR patients performing various activities, created a machine learning-based framework to accurately estimate spatiotemporal movement characteristics from IMU during level ground walking, and defined optimum sensor configuration associated with the patient population and activity. The second study designed a framework to generate synthetic kinematic and associated IMU data as well as investigated the influence of adding synthetic data into training-measured data on deep learning model performance. The third study investigated the kinematic variation between two patient’s population across various activities: stair ascent, stair descent, and gait using principle component analysis PCA. Additionally, PCA-based autoencoders were developed to generate synthetic kinematics data for each patient population and activity. The fourth study investigated the potential use of a universal deep learning model for the estimation of lower extremities’ kinematics across various activities. Therefore, this model can be used as a global model for transfer learning methods in future research. This line of study resulted in a machine-learning framework that can be used to estimate biomechanical movements based on a stream of signals emitted from low-cost and portable IMUs. Eventually, this could lead to a simple clinical tool for tracking patients\u27 movements in their own homes and translating those movements into diagnostic metrics that clinicians will be able to use to tailor treatment to each patient\u27s needs in the future

Exploring the aetiology of high burden lower limb injuries in male professional rugby union players

Abstract The lower limb is the most frequently injured location in Rugby Union, resulting in significant injury burden to professional teams. In order to inform risk mitigation strategies, investigation of the aetiology of high burden lower limb injuries is required. However, sports injury aetiology is a complex problem that is dependent on a multitude of causal factors. The aim of the thesis was to advance knowledge of high burden knee ligament and hamstring injury aetiology in male professional rugby union players, by prospectively exploring the association between injury and selected intrinsic and extrinsic variables. The first experimental study of the thesis (Chapter Three) illustrates that over a period of seven playing seasons, injuries to the lower limb placed the highest burden on the rugby team participating in the research project, compared to the upper limbs, trunk, head and neck. Of these injuries, the locations resulting in highest injury burden were to the knee ligaments and the hamstrings. Specifically, injuries to the anterior cruciate ligament (ACL) sustained during contact events were infrequent but resulted in the highest severity. Injuries to the medial collateral ligament (MCL) sustained during tackle events and rucking placed a high injury burden due to a high incidence rate combined with moderate magnitudes of injury severity. Finally, biceps femoris strain sustained during running was the most frequently occurring injury. The findings of the study provided a focus for the subsequent experimental chapters. Chapter Five demonstrates that isokinetic measures of hamstrings and quadriceps strength have poor predictive value in relation to hamstring strains (the highest AUC score being 0.57), despite being associated with an increased odds of sustaining semimembranosus and semitendinosus strains. Isokinetic strength variables were not associated with sustaining biceps femoris strains. Previous injury to the hamstrings was observed to be associated with an increased odds of subsequent hamstring injury when all muscles were pooled. Previous injury to either the MCL or lateral ankle ligaments was associated with an increased risk of biceps femoris injury and medial hamstring injury. This investigation also identified that chronic exposure of high magnitudes was associated with an increased risk of hamstring injuries sustained during running, specifically exposure to high-speed running over 14 and 21 days prior to the week in which the injury was sustained. Chapter 6 examined a variety of variables which were theoretically associated with contact ACL and MCL injury aetiology. The influence of previous injury history was examined in relation to contact MCL and ACL injury aetiology. Previous knee ligament injury not associated with an increased risk of sustaining a subsequent knee ligament injury during a contact event. However, previous hamstring and triceps surae muscle strains were associated with an increased risk of injury. Isokinetic assessment of both hamstring and quadriceps strength exhibited poor predictive ability in relation to contact knee ligament injury (highest AUC = 0.57). Chapter 6 also examined contact MCL and ACL injury aetiology in relation to lower limb biomechanics during a single-leg drop jump task. Both larger magnitudes of external knee abduction moment and hip adduction moment 50 ms post ground contact were associated with an increased risk of injury. The study highlighted the importance of modelling injury as a rare event in relation to analysis involving player workloads. A minority oversampling algorithm was used to mitigate the negative effects of class imbalance within the player-workloads data sets. Exposure to tackle events during a match was not related to sustaining an MCL or ACL injury from a tackle. When tackle and ruck exposure were combined, increased exposure (during 7 and 14 days preceding the injury) was associated with a decrease in the odds of sustaining a contact MCL or ACL injury where the inciting event involved a tackle or a ruck. Exposure to on-pitch physical activity (PlayerLoadTM) in relation to contact MCL and ACL was also explored. Acute increases in PlayerLoadTM (3-day EWMA and 7-day EWMA) were associated with an increased odds of sustaining MCL injury and ACL injury. Increased magnitudes of chronic PlayerLoadTM exposure during the previous 7-days with a 3-day lag as well as the previous 14-days were both associated with an increased odds of MCL as well as pooled MCL and ACL injury. In summary, the thesis explores the lower limb injuries which place a high burden on male professional rugby players in addition to a selection of variables that are associated with their aetiology. The injuries that placed the highest burden were knee ligament injuries (MCL and ACL) sustained during contact as well as hamstring strains. The experimental chapters reinforce the importance of previous injury history in relation to injury aetiology, with previous injury to the proximal and distal tissues of the injured area increasing subsequent injury risk, suggesting that a more universal approach to rehabilitation may be required. The findings also demonstrate that isokinetic assessment of hamstring and quadriceps strength exhibits poor classification performance in relation to hamstring, MCL and ACL injury and should not be used to infer the subsequent risk of these injuries. In contrast, joint moments occurring at the early stages of ground contact during a single-leg drop-jump task are better at classifying knee ligament injury suggesting more dynamic tasks are required when investigating sports injury aetiology. Finally, the thesis explores the influence of player workloads in relation to injury aetiology, and highlights that the differences in this relationship depending on the injured tissue type as well as the data collection methodology. The studies within the thesis are some of the first to be conducted within a rugby union setting, with this in mind, the work within the thesis provides a conduit between epidemiological and mechanistic studies in addition to providing practical applications for men’s’ professional rugby teams