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

Machine learning in orthopedics: a literature review

In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles\u2019 content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance

Exploring the Application of Wearable Movement Sensors in People with Knee Osteoarthritis

People with knee osteoarthritis have difficulty with functional activities, such as walking or get into/out of a chair. This thesis explored the clinical relevance of biomechanics and how wearable sensor technology may be used to assess how people move when their clinician is unable to directly observe them, such as at home or work. The findings of this thesis suggest that artificial intelligence can be used to process data from sensors to provide clinically important information about how people perform troublesome activities

Stratification of patellofemoral pain using clinical, biomechanical and imaging features

Patellofemoral pain (PFP) is a common musculoskeletal complaint and the efficacy of current therapies aimed at PFP is limited. The aetiology of PFP is widely considered to be multifactorial and as a result the clinical presentation is often heterogeneous. In an attempt to address this issue, an international PFP consensus statement, published in 2013, highlighted the need to sub-group patients with PFP to enable more stratified interventions. A multi-methodological approach was used in this thesis. A systematic review of the existing imaging literature in PFP demonstrated that PFP is associated with a number of imaging features in particular MRI bisect offset and CT congruence angle and that some of these features should be modifiable with conservative treatment. A retrospective analysis investigating the overall 3D shape and 3D equivalents of commonly used PFJ imaging features demonstrated no differences between a group with and without PFP, challenging the current perceptions on the structural associations to PFP. A cross-sectional cluster analysis using modifiable clinical, biomechanical and imaging features identified four subgroups that are present in PFP cohort with a Weak group showing the worst prognosis at 12 months. Lastly, a pragmatic randomised controlled feasibility study comparing matched treatment to usual care management showed that matching treatment to a specific subgroup is feasible in terms of adherence, retention and conversion to consent. In summary, the findings of this thesis improves our understanding of the structural associations to PFP; the subgroups that exist within the PFP population; the natural prognosis of these PFP subgroups; and the feasibility of targeting treatment at PFP subgroups within a clinical trial

Machine Learning in Orthopedics: A Literature Review

In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles' content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance

Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs

There is a growing body of studies on applying deep learning to biometrics analysis. Certain circumstances, however, could impair the objective measures and accuracy of the proposed biometric data analysis methods. For instance, people with chronic pain (CP) unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities in this study and classified pain level and pain-related behaviour in the EmoPain database. To achieve this, we proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders using a shared training framework. This architecture is fed by multidimensional data collected from inertial measurement unit (IMU) and surface electromyography (sEMG) sensors. Furthermore, to compensate for variations in the temporal dimension that may not be perfectly represented in the latent space of s-RNNs, we fused hand-crafted features derived from information-theoretic approaches with represented features in the shared hidden state. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour

Pain Level and Pain-Related Behaviour Classification Using GRU-Based Sparsely-Connected RNNs

There is a growing body of studies on applying deep learning to biometrics analysis. Certain circumstances, however, could impair the objective measures and accuracy of the proposed biometric data analysis methods. For instance, people with chronic pain (CP) unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities in this study and classified pain level and pain-related behaviour in the EmoPain database. To achieve this, we proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders using a shared training framework. This architecture is fed by multidimensional data collected from inertial measurement unit (IMU) and surface electromyography (sEMG) sensors. Furthermore, to compensate for variations in the temporal dimension that may not be perfectly represented in the latent space of s-RNNs, we fused hand-crafted features derived from information-theoretic approaches with represented features in the shared hidden state. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme, with grant agreement No. 101002711

Biomechanical Measures to Assess Recovery from Anterior Cruciate Ligament Injury and Reconstructive Surgery

Anterior cruciate ligament (ACL) injuries are a debilitating injury resulting in abnormal biomechanics. Treatment commonly involves reconstructive surgery, however the tools used to assess the changes in biomechanics due to this procedure may fail to assess movement deficiencies. Therefore, the aim of this thesis was to explore what biomechanical variables are affected by ACL injury and reconstructive surgery and to assess their worth in the monitoring of recovery from ACL injuries and reconstructive surgery. A systematic review of the changes in lower limb biomechanics that occur due to ACL reconstruction identified 51 articles that presented evidence on balance, joint position sense, gait, pivoting, stair ambulation, and landing tasks. Despite trends in certain variables, such as increased knee flexion excursion, there were inconsistencies between articles in presented changes of gait, pivoting, and landing movements. Tasks that related to the proprioceptive function of the limb exhibited consistent improvements due to surgery. This was the first review to provide a synthesis of the evidence around biomechanical changes due to ACL reconstruction and supported the exploration of variables related to the proprioceptive capacity of the injured limb for the use in assessing function. Balance data were collected for eight ACL injured participants before and after surgery, and 45 uninjured participants using collection methods that were integrated into clinical practice. The two samples were similar in age, anthropometrics, and sex. Linear measures of the centre of pressure (CoP) provided a measure of balance performance, and complexity at varying timescales calculated using multiscale sample entropy, an approach that had yet to be explored in ACL injured participants, and complexity index, a summary statistic of the sample entropy at numerous timescales, provided details on the non-linear characteristics of the CoP. Despite previous evidence linking ACL injuries to a reduction in balance performance, the data did not support the use of linear measures. Linear measures had greater variation in uninjured participants than non-linear measures (e.g. coefficient of variation; CoP path length: 16%; mediolateral CoP complexity index: 10%). No trends, supported by a lack of statistical significance, between the involved and comparison limbs were identified (mean±SD pre-surgery CoP path length; ACL involved: 76±19 cm; ACL uninvolved: 87±27 cm; uninjured controls: 93±28 cm). No significant differences were observed due to surgery (mean±SD post-surgery CoP path length; ACL involved: 79±27 cm). Complexity of the CoP, in addition to having a reduced variation in uninjured participants, supported that ACL injury was related to a loss of complexity (mean±SD pre-surgery mediolateral complexity index; ACL involved: 4.9±1.3; uninjured controls: 6.0±0.9) and that reconstructive surgery was able to restore this loss (mean±SD ACL involved mediolateral sample entropy at 6.7 Hz; pre-surgery: 0.9±0.3; 19 weeks post-surgery: 1.2±0.2). The findings provide new evidence to support that ACL injury results in a loss of complexity and that the multiscale sample entropy of the CoP may provide an insight into the changes in lower limb biomechanics that occur due to ACL injury and reconstructive surgery. Comparison of the magnitude of changes in complexity due to ACL reconstructive surgery to uninjured participants, supported that increased complexity may be clinically meaningful. The link between increased complexity and functional outcomes however, is not understood and therefore further research is required to understand this link to establish the usability of complexity as a clinical measure