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

The Effects of Running Kinematics on Peak Upper Trunk GPS-Measured Accelerations during Foot Contact at Different Running Speeds

The overall aim of this study was to determine the effects of running kinematics on the peak upper trunk segmental accelerations captured with an accelerometer embedded in a commonly used GPS device. Thirteen male participants (age: 27 ± 3.7 years, height: 1.81 ± 0.06 m, mass: 82.7 ± 6.2 kg) with extensive running experience completed a single trial of treadmill running (1 degree inclination) for 40 s at nine different speeds ranging from 10 to 18 km/h at 1 km/h increments. Three-dimensional peak upper trunk acceleration values were captured via a GPS device containing a tri-axial accelerometer. Participants’ running kinematics were calculated from the coordinate data captured by an 18-camera motion capture system. A series of generalized linear mixed models were employed to determine the effects of the kinematic variables on the accelerometer acceleration peaks across the key gait phases of foot contact. Results showed that running kinematics had significant effects on peak accelerometer-measured accelerations in all axes (p < 0.05). Overall, peak segment velocities had a larger effect than joint/segment kinematics on resultant (F values = 720.9/54.2), vertical (F values = 149.8/48.1) and medial–lateral (F values = 55.4/33.4) peak accelerometer accelerations. The largest effect on peak accelerometer accelerations were observed during the impact subphase of foot contact at the adduction/abduction velocity of the shank (F value = 129.2, coefficient = −0.03) and anterior/posterior velocity of the pelvis (F value = 58.9, coefficient = 0.01). Axis-dependent effects of running kinematics were also observed, specifically at the trunk segment in the vertical and anterior–posterior peak accelerometer accelerations. This study showed the intersegmental relationship between joint/segment kinematics, segment velocities and the resulting peak accelerations of the upper trunk during running over several speeds. These findings provide insights into the lower body’s GRF attenuation capacity and its contribution to trunk stability whilst running

Developing Muscle Synergy Functions For Remote Gait Analysis

Digital medicine promises to improve healthcare and enable its delivery to rural and underserved communities. A key component of digital medicine is accurate and robust remote patient monitoring. For example, remote monitoring of biomechanical measures of limb impairment during daily life could allow near real-time tracking of rehabilitation progress and personalization of rehabilitation paradigms in those recovering from orthopedic surgery. Wearable sensors have long been suggested as a means for quantifying muscle and joint loading, which can provide a direct measure of limb impairment. However, current approaches either do not provide these measures or require unwieldy wearable sensor arrays and/or in-person calibration activities that limit their use. In this thesis, I advance the use of muscle synergy functions, which leverage the synergistic relationship within a group of muscles, to reduce the complexity of wearable sensor arrays and overcome the current need for an in-person visit to a human performance laboratory for calibration. Surface electromyography (EMG) and kinematic data were recorded from leg muscles and segments of nine healthy subjects during walking. Subject-general muscle synergy models were validated using the leave-one-subject-out method for 4 different pairs of input muscle model sets using filtered EMG data. The effect of adding kinematic data (angular velocity) from thigh and shank segment locations was investigated. The average correlation between true and estimated excitations was 96% higher when angular velocity data was included in the 4-muscle input model set. The estimated excitations informed muscle activations with 6.7% mean absolute error (MAE) and 43% variance accounted for (VAF) averaged across all muscles when kinematic data was included in the model, and 7.3% MAE and 43% VAF without kinematic data. These results lay the groundwork for developing muscle synergy functions that no longer require in-person calibration, paving the way for completely remote studies of muscle and joint loading

IMU-to-Segment Assignment and Orientation Alignment for the Lower Body Using Deep Learning

Human body motion analysis based on wearable inertial measurement units (IMUs) receives a lot of attention from both the research community and the and industrial community. This is due to the significant role in, for instance, mobile health systems, sports and human computer interaction. In sensor based activity recognition, one of the major issues for obtaining reliable results is the sensor placement/assignment on the body. For inertial motion capture (joint kinematics estimation) and analysis, the IMU-to-segment (I2S) assignment and alignment are central issues to obtain biomechanical joint angles. Existing approaches for I2S assignment usually rely on hand crafted features and shallow classification approaches (e.g., support vector machines), with no agreement regarding the most suitable features for the assignment task. Moreover, estimating the complete orientation alignment of an IMU relative to the segment it is attached to using a machine learning approach has not been shown in literature so far. This is likely due to the high amount of training data that have to be recorded to suitably represent possible IMU alignment variations. In this work, we propose online approaches for solving the assignment and alignment tasks for an arbitrary amount of IMUs with respect to a biomechanical lower body model using a deep learning architecture and windows of 128 gyroscope and accelerometer data samples. For this, we combine convolutional neural networks (CNNs) for local filter learning with long-short-term memory (LSTM) recurrent networks as well as generalized recurrent units (GRUs) for learning time dynamic features. The assignment task is casted as a classification problem, while the alignment task is casted as a regression problem. In this framework, we demonstrate the feasibility of augmenting a limited amount of real IMU training data with simulated alignment variations and IMU data for improving the recognition/estimation accuracies. With the proposed approaches and final models we achieved 98.57% average accuracy over all segments for the I2S assignment task (100% when excluding left/right switches) and an average median angle error over all segments and axes of 2.91 ° for the I2S alignment task

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

Development of a Wearable Sensor-Based Framework for the Classification and Quantification of High Knee Flexion Exposures in Childcare

Repetitive cyclic and prolonged joint loading in high knee flexion postures has been associated with the progression of degenerative knee joint diseases and knee osteoarthritis (OA). Despite this association, high flexion postures, where the knee angle exceeds 120°, are commonly performed within occupational settings. While work related musculoskeletal disorders have been studied across many occupations, the risk of OA development associated with the adoption of high knee flexion postures in childcare workers has until recently been unexplored; and therefore, occupational childcare has not appeared in any systematic reviews seeking to prove a causal relationship between occupational exposures and the risk of knee OA development. Therefore, the overarching goal of this thesis was to explore the adoption of high flexion postures in childcare settings and to develop a means by which these could be measured using non-laboratory-based technologies. The global objectives of this thesis were to (i) identify the postural demands of occupational childcare as they relate to high flexion exposures at the knee, (ii) apply, extend, and validate sensor to segment alignment algorithms through which lower limb flexion-extension kinematics could be measured in multiple high knee flexion postures using inertial measurement units (IMUs), and (iii) develop a machine learning based classification model capable of identifying each childcare-inspired high knee flexion posture. In-line with these global objectives, four independent studies were conducted.   Study I – Characterization of Postures of High Knee Flexion and Lifting Tasks Associated with Occupational Childcare Background: High knee flexion postures, despite their association with increased incidences of osteoarthritis, are frequently adopted in occupational childcare. High flexion exposure thresholds (based on exposure frequency or cumulative daily exposure) that relate to increased incidences of OA have previously been proposed; yet our understanding of how the specific postural requirements of this childcare compare to these thresholds remains limited. Objectives: This study sought to define and quantify high flexion postures typically adopted in childcare to evaluate any increased likelihood of knee osteoarthritis development. Methods: Video data of eighteen childcare workers caring for infant, toddler, and preschool-aged children over a period of approximately 3.25 hours were obtained for this investigation from a larger cohort study conducted across five daycares in Kingston, Ontario, Canada. Each video was segmented to identify the start and end of potential high knee flexion exposures. Each identified posture was quantified by duration and frequency. An analysis of postural adoption by occupational task was subsequently performed to determine which task(s) might pose the greatest risk for cumulative joint trauma. Results: A total of ten postures involving varying degrees of knee flexion were identified, of which 8 involved high knee flexion. Childcare workers caring for children of all ages were found to adopt high knee flexion postures for durations of 1.45±0.15 hours and frequencies of 128.67±21.45 over the 3.25 hour observation period, exceeding proposed thresholds for incidences of knee osteoarthritis development. Structured activities, playing, and feeding tasks were found to demand the greatest adoption of high flexion postures. Conclusions: Based on the findings of this study, it is likely that childcare workers caring for children of all ages exceed cumulative exposure- and frequency-based thresholds associated with increased incidences of knee OA development within a typical working day. Study II – Evaluating the Robustness of Automatic IMU Calibration for Lower Extremity Motion Analysis in High Knee Flexion Postures Background: While inertial measurement units promise an out- of-the-box, minimally intrusive means of objectively measuring body segment kinematics in any setting, in practice their implementation requires complex calculations in order to align each sensor with the coordinate system of the segment to which they are attached. Objectives: This study sought to apply and extend previously proposed alignment algorithms to align inertial sensors with the segments on which they are attached in order to calculate flexion-extension angles for the ankle, knee, and hip during multiple childcare-inspired postures. Methods: The Seel joint axis algorithm and the Constrained Seel Knee Axis (CSKA) algorithm were implemented for the sensor to segment calibration of acceleration and angular velocity data from IMUs mounted on the lower limbs and pelvis, based on a series of calibration movements about each joint. Further, the Iterative Seel spherical axis (ISSA) extension to this implementation was proposed for the calibration of sensors about the ankle and hip. The performance of these algorithms was validated across fifty participants during ten childcare-inspired movements performed by comparing IMU- and gold standard optical-based flexion-extension angle estimates. Results: Strong correlations between the IMU- and optical-based angle estimates were reported for all joints during each high flexion motion with the exception of a moderate correlation reported for the ankle angle estimate during child chair sitting. Mean RMSE between protocols were found to be 6.61° ± 2.96° for the ankle, 7.55° ± 5.82° for the knee, and 14.64° ± 6.73° for the hip. Conclusions: The estimation of joint kinematics through the IMU-based CSKA and ISSA algorithms presents an effective solution for the sensor to segment calibration of inertial sensors, allowing for the calculation of lower limb flexion-extension kinematics in multiple childcare-inspired high knee flexion postures. Study III – A Multi-Dimensional Dynamic Time Warping Distance-Based Framework for the Recognition of High Knee Flexion Postures in Inertial Sensor Data Background: The interpretation of inertial measures as they relate to occupational exposures is non-trivial. In order to relate the continuously collected data to the activities or postures performed by the sensor wearer, pattern recognition and machine learning based algorithms can be applied. One difficulty in applying these techniques to real-world data lies in the temporal and scale variability of human movements, which must be overcome when seeking to classify data in the time-domain. Objectives: The objective of this study was to develop a sensor-based framework for the detection and measurement of isolated childcare-specific postures (identified in Study I). As a secondary objective, the classification accuracy movements performed under loaded and unloaded conditions were compared in order to assess the sensitivity of the developed model to potential postural variabilities accompanying the presence of a load. Methods: IMU-based joint angle estimates for the ankle, knee, and hip were time and scale normalized prior to being input to a multi-dimensional Dynamic Time Warping (DTW) distance-based Nearest Neighbour algorithm for the identification of twelve childcare inspired postures. Fifty participants performed each posture, when possible, under unloaded and loaded conditions. Angle estimates from thirty-five participants were divided into development and testing data, such that 80% of the trials were segmented into movement templates and the remaining 20% were left as continuous movement sequences. These data were then included in the model building and testing phases while the accuracy of the model was validated based on novel data from fifteen participants. Results: Overall accuracies of 82.3% and 55.6% were reached when classifying postures on testing and validation data respectively. When adjusting for the imbalances between classification groups, mean balanced accuracies increased to 86% and 74.6% for testing and validation data respectively. Sensitivity and specificity values revealed the highest rates of misclassifications occurred between flatfoot squatting, heels-up squatting, and stooping. It was also found that the model was not capable of identifying sequences of walking data based on a single step motion template. No significant differences were found between the classification of loaded and unloaded motion trials. Conclusions: A combination of DTW distances calculated between motion templates and continuous movement sequences of lower limb flexion-extension angles was found to be effective in the identification of isolated postures frequently performed in childcare. The developed model was successful at classifying data from participants both included and precluded from the algorithm building dataset and insensitive to postural variability which might be caused by the presence of a load. Study IV – Evaluating the Feasibility of Applying the Developed Multi-Dimensional Dynamic Time Warping Distance-Based Framework to the Measurement and Recognition of High Knee Flexion Postures in a Simulated Childcare Environment Background: While the simulation of high knee flexion postures in isolation (in Study III) provided a basis for the development of a multi-dimensional Dynamic Time Warping based nearest neighbour algorithm for the identification of childcare-inspired postures, it is unlikely that the postures adopted in childcare settings would be performed in isolation. Objectives: This study sought to explore the feasibility of extending the developed classification algorithm to identify and measure postures frequently adopted when performing childcare specific tasks within a simulated childcare environment. Methods: Lower limb inertial motion data was recorded from twelve participants as they interacted with their child during a series of tasks inspired by those identified in Study I as frequently occurring in childcare settings. In order to reduce the error associated with gyroscopic drift over time, joint angles for each trial were calculated over 60 second increments and concatenated across the duration of each trial. Angle estimates from ten participants were time windowed in order to create the inputs for the development and testing of two model designs wherein: (A) the model development data included all templates generated from Study III as well as continuous motion windows here collected, or (B) only the model development data included only windows of continuous motion data. The division of data into the development and testing datasets for each 5-fold cross-validated classification model was performed in one of two ways wherein the data was divided: (a) through stratified randomized partitioning of windows such that 80% were assigned to model development and the remaining 20% were reserved for testing, or (b) by partitioning all windows from a single trial of a single participant for testing while all remaining windows were assigned to the model development dataset. When the classification of continuously collected windows was tested (using division strategy b), a logic-based correction module was introduced to eliminate any erroneous predictions. Each model design (A and B) was developed and tested using both data division strategies (a and b) and subsequently their performance was evaluated based on the classification of all data windows from the two subjects reserved for validation. Results: Classification accuracies of 42.2% and 42.5% were achieved when classifying the testing data separated through stratified random partitioning (division strategy a) using models that included (model A, 159 classes) or excluded (model B, 149 classes) the templates generated from Study III, respectively. This classification accuracy was found to decrease when classifying a test partition which included all windows of a single trial (division strategy b) to 35.4% when using model A (where templates from Study III were included in the model development dataset); however, this same trial was classified with an accuracy of 80.8% when using model B (whose development dataset included only windows of continuous motion data). This accuracy was however found to be highly dependent on the motions performed in a given trial and logic-based corrections were not found to improve classification accuracies. When validating each model by identifying postures performed by novel subjects, classification accuracies of 24.0% and 26.6% were obtained using development data which included (model A) and excluded (model B) templates from Study III, respectively. Across all novel data, the highest classification accuracies were observed when identifying static postures, which is unsurprising given that windows of these postures were most prevalent in the model development datasets. Conclusions: While classification accuracies above those achievable by chance were achieved, the classification models evaluated in this study were incapable of accurately identifying the postures adopted during simulated childcare tasks to a level that could be considered satisfactory to accurately report on the postures assumed in a childcare environment. The success of the classifier was highly dependent on the number of transitions occurring between postures while in high flexion; therefore, more classifier development data is needed to create templates for these novel transition movements. Given the high variability in postural adoption when caring for and interacting with children, additional movement templates based on continuously collected data would be required for the successful identification of postures in occupational settings. Global Conclusions Childcare workers exceed previously reported thresholds for high knee flexion postures based on cumulative exposure and frequency of adoption associated with increased incidences of knee OA development within a typical working day. Inertial measurement units provide a unique means of objectively measuring postures frequently adopted when caring for children which may ultimately permit the quantification of high knee flexion exposures in childcare settings and further study of the relationship between these postures and the risk of OA development in occupational childcare. While the results of this thesis demonstrate that IMU based measures of lower limb kinematics can be used to identify these postures in isolation, further work is required to expand the classification model and enable the identification of such postures from continuously collected data