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

CAN INERTIAL MEASUREMENT UNITS BE USED TO VALIDLY MEASURE PELVIS AND THORAX MOTION DURING CRICKET BOWLING?

Identifying lumbar injury risk amongst cricket bowlers is a challenge to those involved in the sport. Bowling technique injury risk factors concerning thoracic and pelvic motion have been identified by previous research that used three-dimensional (3D) retro-reflective (RR) motion analysis. Inertial measurement units (IMUs) are considered a feasible and more portable means of 3D motion analysis. However, the validity of IMU measurement of thorax and pelvis movement during bowling has not yet been fully determined. This study aimed to achieve this by comparing concurrent IMU and RR angle outputs. Results suggest that when RR coordinate systems are aligned with IMUs’ there are no significant differences in cricket bowling relevant angle outputs. However, some differences arise when IMUs are compared to the anatomically derived RR angle outputs typically used in 3D analysis

Human activity recognition for people with knee osteoarthritis—A proof‐of‐concept

Clinicians lack objective means for monitoring if their knee osteoarthritis patients are improving outside of the clinic (e.g., at home). Previous human activity recognition (HAR) models using wearable sensor data have only used data from healthy people and such models are typically imprecise for people who have medical conditions affecting movement. HAR models designed for people with knee osteoarthritis have classified rehabilitation exercises but not the clinically relevant activities of transitioning from a chair, negotiating stairs and walking, which are commonly monitored for improvement during therapy for this condition. Therefore, it is unknown if a HAR model trained on data from people who have knee osteoarthritis can be accurate in classifying these three clinically relevant activities. Therefore, we collected inertial measurement unit (IMU) data from 18 participants with knee osteoarthritis and trained convolutional neural network models to identify chair, stairs and walking activities, and phases. The model accuracy was 85% at the first level of classification (activity), 89–97% at the second (direction of movement) and 60–67% at the third level (phase). This study is the first proof‐of‐concept that an accurate HAR system can be developed using IMU data from people with knee osteoarthritis to classify activities and phases of activities

Predicting Knee Joint Kinematics from Wearable Sensor Data in People with Knee Osteoarthritis and Clinical Considerations for Future Machine Learning Models

Deep learning models developed to predict knee joint kinematics are usually trained on inertial measurement unit (IMU) data from healthy people and only for the activity of walking. Yet, people with knee osteoarthritis have difficulties with other activities and there are a lack of studies using IMU training data from this population. Our objective was to conduct a proof-of-concept study to determine the feasibility of using IMU training data from people with knee osteoarthritis performing multiple clinically important activities to predict knee joint sagittal plane kinematics using a deep learning approach. We trained a bidirectional long short-term memory model on IMU data from 17 participants with knee osteoarthritis to estimate knee joint flexion kinematics for phases of walking, transitioning to and from a chair, and negotiating stairs. We tested two models, a double-leg model (four IMUs) and a single-leg model (two IMUs). The single-leg model demonstrated less prediction error compared to the double-leg model. Across the different activity phases, RMSE (SD) ranged from 7.04° (2.6) to 11.78° (6.04), MAE (SD) from 5.99° (2.34) to 10.37° (5.44), and Pearson’s R from 0.85 to 0.99 using leave-one-subject-out cross-validation. This study demonstrates the feasibility of using IMU training data from people who have knee osteoarthritis for the prediction of kinematics for multiple clinically relevant activities

Implementation of Questionnaire-Based Risk Profiling for Clients in a Workers’ Compensation Environment: An Example in Australian Physiotherapy Practice

Purpose This study investigated the implementation of a risk profiling process for physiotherapy clients with a compensable musculoskeletal problem. Implementation targeted personal (clinician) and external (organisational) factors to facilitate behavioural change with regard to the use of formal, questionnaire-based risk profiling. Methods A theoretical construct was developed for formal questionnaire-based screening to be implemented across 12 private, metropolitan physiotherapy clinics. To target personal (clinician) factors, a multimodal educational procedure was developed focused on use of the ten-item Orebro Musculoskeletal Pain Screening Questionnaire (OMPSQ-10). To target external (organisational) factors, an administrative process was enacted to ensure routine completion of the OMPSQ-10 by compensable clients. Global practice behaviour with regard to the use of formal risk profiling was complete pre- and post-implementation. Results Pre-implementation physiotherapists understood the potential usefulness of formal risk profiling, but the large majority did not routinely have clients complete these types of questionnaires. Post-implementation there was a significant positive shift in behaviour to more frequent use the OMPSQ-10 for new compensable clients. Conclusions The results provide initial support for the use of a framework to develop an implementation strategy to increase physiotherapist adherence to the use of guideline recommended risk profiling questionnaires in clinical practice

Search for heavy resonances decaying into a vector boson and a Higgs boson in final states with charged leptons, neutrinos, and b quarks

A search for heavy resonances decaying to a Higgs boson and a vector boson is presented. The analysis is performed using data samples collected in 2015 by the CMS experiment at the LHC in proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to integrated luminosities of 2.2-2.5 inverse femtobarns. The search is performed in channels in which the vector boson decays into leptonic final states ($\mathrm{Z} \to \nu\nu$, $\mathrm{W}\to \ell \nu$, and $\mathrm{Z} \to \ell \ell$, with $\ell = \mathrm{e}$, $\mu$), while the Higgs boson decays to collimated b quark pairs detected as a single massive jet. The discriminating power of a jet mass requirement and a b jet tagging algorithm are exploited to suppress the standard model backgrounds. The event yields observed in data are consistent with the background expectation. In the context of a theoretical model with a heavy vector triplet, a resonance with mass less than 2 TeV is excluded at 95% confidence level. The results are also interpreted in terms of limits on the parameters of the model, improving on the reach of previous searches

Search for heavy resonances decaying into a vector boson and a Higgs boson in final states with charged leptons, neutrinos, and b quarks

A search for heavy resonances decaying to a Higgs boson and a vector boson is presented. The analysis is performed using data samples collected in 2015 by the CMS experiment at the LHC in proton–proton collisions at a center-of-mass energy of 13 TeV, corresponding to integrated luminosities of 2.2–2.5 fb$^{−1}$ . The search is performed in channels in which the vector boson decays into leptonic final states (Z → $\nu\nu$ , W → ℓ $\nu$, and Z → ℓℓ , with ℓ = e, $\mu$), while the Higgs boson decays to collimated b quark pairs detected as a single massive jet. The discriminating power of a jet mass requirement and a b jet tagging algorithm are exploited to suppress the standard model backgrounds. The event yields observed in data are consistent with the background expectation. In the context of a theoretical model with a heavy vector triplet, a resonance with mass less than 2 TeV is excluded at 95% confidence level. The results are also interpreted in terms of limits on the parameters of the model, improving on the reach of previous searches