In-clinic Functional Measurement and Analysis of Knee Osteoarthritis Patients Undergoing Total Knee Replacement

Prevalence of osteoarthritis is increasing as individuals are remaining active later in life. Since the knee is one of the most commonly affected joints and is involved in almost all daily activities, functional impairment has a substantial impact on overall health. Despite this increase, there currently exists no disease modifying drugs or treatments. Mild cases are managed with physiotherapeutic exercises and common anti-inflammatories but surgical intervention is required for more severe disease progression. Total knee replacement as a treatment for osteoarthritis is a highly successful surgery that is effective at restoring knee function and reducing pain but still requires further refinement. Over 70,000 of these surgeries are performed annually in Canada with 99% for the treatment of degenerative arthritis. Despite improvements to surgical technique and implant designs, studies report up to 20% of patients remain dissatisfied with their knee replacement up to the point of not undergoing the surgery again if it were an option. A singular cause for this dissatisfaction has not been pinpointed but strong influencers are pain, low functional improvement, and unmet expectations. Early detection of functional problems permits further intervention through targeted physiotherapy or additional surgeries before problems escalate and cause patient dissatisfaction or implant revision. Current methods of patient evaluation rely on self-reported measures, which suffer from ceiling and floor effects often masking inter-patient differences. These measures are also influenced from patient expectations and what a patient reports they can\u27\u27 do, is not always representative of their true functional ability. Wearable sensors permit objective functional measurement of the knee as a supplement to patient-reported measures. Instrumented performance tests can measure patient function and compare to similar recoveries to highlight deficiencies or positive recovery traits. This thesis outlines the development of such a wearable system for in-clinic measurement and the extraction of functional parameters to predict future outcomes and give surgeons the earliest indications for intervention. This information can also help surgeons realistically adjust patient expectations for recovery, even before undergoing surgery. It is expected that these individualized assessments to set expectations before surgical intervention will help address the persistently high patient dissatisfaction

Machine Learning Groups Patients by Early Functional Improvement Likelihood Based on Wearable Sensor Instrumented Preoperative Timed-Up-and-Go Tests

© 2019 The Author(s) Background: Wearable sensors permit efficient data collection and unobtrusive systems can be used for instrumenting knee patients for objective assessment. Machine learning can be leveraged to parse the abundant information these systems provide and segment patients into relevant groups without specifying group membership criteria. The objective of this study is to examine functional parameters influencing favorable recovery outcomes by separating patients into functional groups and tracking them through clinical follow-ups. Methods: Patients undergoing primary unilateral total knee arthroplasty (n = 68) completed instrumented timed-up-and-go tests preoperatively and at their 2-, 6-, and 12-week follow-up appointments. A custom wearable system extracted 55 metrics for analysis and a K-means algorithm separated patients into functionally distinguished groups based on the derived features. These groups were analyzed to determine which metrics differentiated most and how each cluster improved during early recovery. Results: Patients separated into 2 clusters (n = 46 and n = 22) with significantly different test completion times (12.6 s vs 21.6 s, P \u3c .001). Tracking the recovery of both groups to their 12-week follow-ups revealed 64% of one group improved their function while 63% of the other maintained preoperative function. The higher improvement group shortened their test times by 4.94 s, (P = .005) showing faster recovery while the other group did not improve above a minimally important clinical difference (0.87 s, P = .07). Features with the largest effect size between groups were distinguished as important functional parameters. Conclusion: This work supports using wearable sensors to instrument functional tests during clinical visits and using machine learning to parse complex patterns to reveal clinically relevant parameters

Using gradient boosting regression to improve ambient solar wind model predictions

Studying the ambient solar wind, a continuous pressure‐driven plasma flow emanating from our Sun, is an important component of space weather research. The ambient solar wind flows in interplanetary space determine how solar storms evolve through the heliosphere before reaching Earth, and especially during solar minimum are themselves a driver of activity in the Earth’s magnetic field. Accurately forecasting the ambient solar wind flow is therefore imperative to space weather awareness. Here we present a machine learning approach in which solutions from magnetic models of the solar corona are used to output the solar wind conditions near the Earth. The results are compared to observations and existing models in a comprehensive validation analysis, and the new model outperforms existing models in almost all measures. In addition, this approach offers a new perspective to discuss the role of different input data to ambient solar wind modeling, and what this tells us about the underlying physical processes. The final model discussed here represents an extremely fast, well‐validated and open‐source approach to the forecasting of ambient solar wind at Earth

Machine Learning Predicts the Fall Risk of Total Hip Arthroplasty Patients Based on Wearable Sensor Instrumented Performance Tests

© 2020 Elsevier Inc. Background: The prevalence of falls affects the wellbeing of aging adults and places an economic burden on the healthcare system. Integration of wearable sensors into existing fall risk assessment tools enables objective data collection that describes the functional ability of patients. In this study, supervised machine learning was applied to sensor-derived metrics to predict the fall risk of patients following total hip arthroplasty. Methods: At preoperative, 2-week, and 6-week postoperative appointments, patients (n = 72) were instrumented with sensors while they performed the timed-up-and-go walking test. Preoperative and 2-week postoperative data were used to form the feature sets and 6-week total times were used as labels. Support vector machine and linear discriminant analysis classifier models were developed and tested on various combinations of feature sets and feature reduction schemes. Using a 10-fold leave-some-subjects-out testing scheme, the accuracy, sensitivity, specificity, and area under the receiver-operator curve (AUC) were evaluated for all models. Results: A high performance model (accuracy = 0.87, sensitivity = 0.97, specificity = 0.46, AUC = 0.82) was obtained with a support vector machine classifier using sensor-derived metrics from only the preoperative appointment. An overall improved performance (accuracy = 0.90, sensitivity = 0.93, specificity = 0.59, AUC = 0.88) was achieved with a linear discriminant analysis classifier when 2-week postoperative data were added to the preoperative data. Conclusion: The high accuracy of the fall risk prediction models is valuable for patients, clinicians, and the healthcare system. High-risk patients can implement preventative measures and low-risk patients can be directed to enhanced recovery care programs

Peak shift following simultaneous discriminations

Pigeons were exposed to stimuli presented on two keys. For some birds, the stimuli varied in a dimension of visual flicker-rate, and for others they varied in visual intensity. During differential training, concurrent schedules operated, with one stimulus correlated with one schedule and another stimulus correlated with a second schedule that arranged a lower, or zero, rate of reinforcement. The stimuli were alternated randomly on the two keys. Generalization tests were given in which the original two, and seven other stimuli lying in the same dimension, were presented in pairs on the two keys in various combinations. In the generalization test given after differential training, each bird showed peak shift. The data did not support explanation for peak shift that gave critical emphasis to whether stimuli were presented simultaneously or successively during differential training