Can a Power Training Program Reduce Fall Risk Factors in Parkinson\u27s Disease?

Introduction: Frequent falls in Parkinson’s disease (PD) are likely partially due to impaired muscle function in PD (i.e. greater coactivation and decreased magnitude of activation in agonists) compared to older adults without PD. Reduced muscle strength and power (ability to generate force rapidly) are also risk factors and are likely occurring due to deficits in muscle parameters. Muscle parameters include: i) the amount of coactivation of antagonist muscles; ii) latency to onset of activation in agonist and antagonist muscles and; iii) the magnitude of activation of agonist and antagonist muscles. Rehabilitation should aim to improve impaired muscle parameters to reduce fall risk in PD. Therefore, two experiments were designed to address this gap in PD literature. Experiment one aimed to identify specific muscle parameters distinguishing fall status in PD, thus providing parameters that can be used to identify if a rehabilitation will be effective in reducing fall risk. Experiment two investigated whether power training (PWR) was more effective than strength training (ST) or a non-exercise control group (CTRL) at improving muscle parameters distinguishing fallers in experiment one. Methods: Experiment one - Forty-six individuals with PD were categorized based on fall status. A fall-like situation (lean and release) was used and electromyography (EMG) data was collected from muscles in both legs (stepping and stance leg): tibialis anterior (TA), lateral gastrocnemius (LG), biceps (BF) and rectus femoris (RF). Results: A Receiver Operating Characteristic (ROC) curve identified fallers vs. non-fallers by EMG measures in the stepping leg; an increased onset latency of LG and a greater TA activation. As well, in the stance limb, an increased coactivation of TA and a larger TA activation identified fallers. Experiment two- Forty-four individuals with PD were randomized to PWR or ST groups, and seventeen individuals with PD volunteered for the CTRL group. Training occurred twice weekly for 12-weeks, where PWR completed the concentric part of the movements rapidly. All groups completed the fall situation (at baseline, one to two weeks prior to the intervention, and one to two weeks after the intervention was complete) while muscle parameters were measured along with muscle strength and muscle power, disease severity and a weekly falls diaries. Results: No differences in muscle parameters were present at post-testing between groups. However, PWR and ST significantly improved muscle strength, and components of muscle power compared to CTRL. Disease severity was improved in PWR at post-testing. Conclusion: Muscle parameters distinguishing PD fallers were identified. As well, PWR and ST improved aspects of risk factors for falls similarly, providing two feasible rehabilitation strategies for PD

Objective Assessment of Neurological Conditions using Machine Learning

Movement disorders are a subset of neurological conditions that are responsible for a significant decline in the health of the world’s population, having multiple negative impacts on the lives of patients, their families, societies and countries’ economy. Parkinson’s disease (PD), the most common of all movement disorders, remains idiopathic (of unknown cause), is incurable, and without any confirmed pathological marker that can be extracted from living patients. As a degenerative condition, early and accurate diagnosis is critical for effective disease management in order to preserve a good quality of life. It also requires an in-depth understanding of clinical symptoms to differentiate the disease from other movement disorders. Unfortunately, clinical diagnosis of PD and other movement disorders is subject to the subjective interpretation of clinicians, resulting in a high rate of misdiagnosis of up to 25%. However, computerised methods can support clinical diagnosis through objective assessment. The major focus of this study is to investigate the use of machine learning approaches, specifically evolutionary algorithms, to diagnose, differentiate and characterise different movement disorders, namely PD, Huntington disease (HD) and Essential Tremor (ET). In the first study, movement features of three standard motor tasks from Unified Parkinson’s Disease Rating Scale (UPDRS), finger tapping, hand opening-closing and hand pronation-supination, were used to evolve the high-performance classifiers. The results obtained for these conditions are encouraging, showing differences between the groups of healthy controls, PD, HD and ET patients. Findings on the most discriminating features of the best classifiers provide insight into different characteristics of the neurological disorders under consideration. The same algorithm has also been applied in the second study on Dystonia patients. A differential classification between Organic Dystonia and Functional Dystonia patients is less convincing, but positive enough to recommend future studies

On the filtering and smoothing of biomechanical data

This paper deals with the issue of filtering and smoothing biomechanical data for the purpose of implementing the so-called inverse solution. First, the filtering method using a low-pass zero-phase-shift Butterworth filter is discussed, covering key points like the phase shift, the choice of the order and the cutoff frequency using a residual analysis. Next, a new method for smoothing biomechanical data is proposed by combining an adaptive cubic spline and least squares algorithm. The performance of the proposed smoothing technique is compared to that of the Butterworth filter using experimental data