The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control

Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions—including Parkinson’s disease, ataxia, and dementia— we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel ‘big data’ approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction

Fifteen years of wireless sensors for balance assessment in neurological disorders

Balance impairment is a major mechanism behind falling along with environmental hazards. Under physiological conditions, ageing leads to a progressive decline in balance control per se. Moreover, various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to balance. Over the last 15 years, significant advancements in technology have provided wearable solutions for balance evaluation and the management of postural instability in patients with neurological disorders. This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes. The review discusses the physiological and pathophysiological bases of balance in neurological disorders as well as the traditional and innovative instruments currently available for balance assessment. The technical and clinical perspectives of wearable technologies, as well as current challenges in the field of teleneurology, are also examined

Identifying Gait Deficits in Stroke Patients Using Inertial Sensors

Falls remain a significant problem for stroke patients. Tripping, the main cause of falls, occurs when there is insufficient clearance between the foot and ground. Based on an individual’s gait deficits, different joint angles and coordination patterns are necessary to achieve adequate foot clearance during walking. However, gait deficits are typically only quantified in a research or clinical setting, and it would be helpful to use wearable devices – such as accelerometers – to quantify gait disorders in real-world situations. Therefore, the objective of this project was to understand gait characteristics that influence the risk of tripping, and to detect these characteristics using accelerometers. Thirty-five participants with a range of walking abilities performed normal walking and attempted to avoid tripping on an unexpected object while gait characteristics were quantified using motion capture techniques and accelerometers. Multiple regression was used to identify the relationship between joint coordination and foot clearance, and multiple analysis of variance was used to determine characteristics of gait that differ between demographic groups, as well as those that enable obstacle avoidance. Machine learning techniques were employed to detect joint angles and the risk of tripping from patterns in accelerometer signals. Measures of foot clearance that represent toe height throughout swing instead of at a single time point are more sensitive to changes in joint coordination, with hip-knee coordination during midswing having the greatest effect. Participants with a history of falls or stroke perform worse than older non-fallers and young adults on many factors related to falls risk, however, there are no differences in the ability to avoid an unexpected obstacle between these groups. Individuals with an inability to avoid an obstacle have lower scores on functional evaluations, exhibit limited sagittal plane joint range of motion during swing, and adopt a conservative walking strategy. Machine learning processes can be used to predict knee range of motion and classify individuals at risk for tripping based on an ankle-worn accelerometer. This work is significant because a portable device that detects gait characteristics relevant to the risk of tripping without expensive motion capture technology may reduce the risk of falls for stroke patients

Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders

The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders

Gait Impairment in Traumatic Brain Injury: A Systematic Review

Introduction: Gait impairment occurs across the spectrum of traumatic brain injury (TBI); from mild (mTBI) to moderate (modTBI), to severe (sevTBI). Recent evidence suggests that objective gait assessment may be a surrogate marker for neurological impairment such as TBI. However, the most optimal method of objective gait assessment is still not well understood due to previous reliance on subjective assessment approaches. The purpose of this review was to examine objective assessment of gait impairments across the spectrum of TBI. Methods: PubMed, AMED, OVID and CINAHL databases were searched with a search strategy containing key search terms for TBI and gait. Original research articles reporting gait outcomes in adults with TBI (mTBI, modTBI, sevTBI) were included. Results: 156 citations were identified from the search, of these, 13 studies met the initial criteria and were included into the review. The findings from the reviewed studies suggest that gait is impaired in mTBI, modTBI and sevTBI (in acute and chronic stages), but methodological limitations were evident within all studies. Inertial measurement units were most used to assess gait, with single-task, dual-task and obstacle crossing conditions used. No studies examined gait across the full spectrum of TBI and all studies differed in their gait assessment protocols. Recommendations for future studies are provided. Conclusion: Gait was found to be impaired in TBI within the reviewed studies regardless of severity level (mTBI, modTBI, sevTBI), but methodological limitations of studies (transparency and reproducibility) limit clinical application. Further research is required to establish a standardised gait assessment procedure to fully determine gait impairment across the spectrum of TBI with comprehensive outcomes and consistent protocols

Backward Walking: A Novel Marker Of Fall Risk, Cognitive Dysfunction, And Myelin Damage In Persons With Multiple Sclerosis

Multiple sclerosis (MS) is a progressive, neurologic disease of the central nervous system that causes debilitating motor, sensory and cognitive impairments. As a result, persons with MS are at an increased risk for falls and falls represent a serious public health concern for the MS population. The current clinical measures used to assess fall risk in MS patients lack sensitivity and predictive validity for falls and are limited in their ability to capture to multiple functional domains (i.e., motor, sensory, cognitive and pathological domains) that are impaired by MS. Backward walking sensitively detects falls in the elderly and other neurologic diseases. However, backward walking and falls has never been explored in the MS population and the underlying reasons as to why backward walking sensitively detects falls remains unknown. Identification of a quick, simply and clinically feasible fall risk measures related to multiple functions impacted by MS and related to fall risk, which can detect falls before they occur is critical for fall prevention and timely and targeted intervention. Therefore, this dissertation examines backward walking as a novel marker of fall risk and its cognitive and pathological underpinnings to support its clinical utility. Our results indicate that backward walking is a sensitive marker of fall risk in the MS population, regardless of co-morbid cognitive deficits, and that examining underlying brain regions likely to contribute to backward walking performance including the corticospinal tract, corpus callosum and cerebellum, with neuroimaging tools sensitive to myelin (i.e., Myelin Water Imaging) demonstrate potential to identify underlying mechanisms of backward walking performance in the MS population. This work is the critical first step in establishing backward walking as a sensitive marker of fall risk for the MS population and leads the way to more personalized fall prevention therapies and interventions to improve clinical outcomes and decrease fall rates in the MS population

Diagnosis of Neurogenetic Disorders: Contribution of Next Generation Sequencing and Deep Phenotyping

The contribution of genomic variants to the aetiopathogenesis of both paediatric and adult neurological disease is being increasingly recognized. The use of next-generation sequencing has led to the discovery of novel neurodevelopmental disorders, as exemplified by the deciphering developmental disorders (DDD) study, and provided insight into the aetiopathogenesis of common adult neurological diseases. Despite these advances, many challenges remain. Correctly classifying the pathogenicity of genomic variants from amongst the large number of variants identified by next-generation sequencing is recognized as perhaps the major challenge facing the field. Deep phenotyping (e.g., imaging, movement analysis) techniques can aid variant interpretation by correctly classifying individuals as affected or unaffected for segregation studies. The lack of information on the clinical phenotype of novel genetic subtypes of neurological disease creates limitations for genetic counselling. Both deep phenotyping and qualitative studies can capture the clinical and patient’s perspective on a disease and provide valuable information. This Special Issue aims to highlight how next-generation sequencing techniques have revolutionised our understanding of the aetiology of brain disease and describe the contribution of deep phenotyping studies to a variant interpretation and understanding of natural history

Biofeedback Based Physical Rehabilitation in Parkinson's Disease Aimed at Self-Enhancement

Parkinson’s disease (PD) is a progressive neuromotor disorder that results in a progressive deterioration of balance and motor abilities with a consequent increase of the risk of falls and a reduction of quality of life. Physical therapy revealed to be fit for the symptomatic treatment of the disease and the adoption of biofeedback signals showed to be effective in prolonging the benefits of the therapy. Thus, this doctoral project has been designed to assess the benefits that wearable technologies for biofeedback generation could have in physical therapy. To further improve the developed biofeedback-based system, the assessment of new methods for the objective evaluation of balance control was included into the study. The dissertation is divided into three different set of studies, respectively aimed at: 1) presenting new wearable systems specifically designed for biofeedback-based rehabilitation; 2) assessing proprioceptive impairments in PD subjects through the adoption of a robotic platform to destabilize the base of support; 3) discussing new methods for the evaluation of balance preceding the execution of voluntary movements. The efficacy of the main proposed solution was assessed in a 6-months RCT study by comparison of subjects with PD trained with the biofeedback system and patients that received usual care. Both clinical and instrumental outcomes supported the higher efficacy of the biofeedback-based approach. The developed instrumented tests showed good sensitivity in discriminating patients and in detecting changes induced by physical therapy. The results reported in this thesis lead to the conclusion that the adoption of biofeedback based physical rehabilitation systems is promising in the treatment of Parkinson’s disease. The availability of a set of fast, easy-to-manage tests for the evaluation of balance and motor control might be useful in the design of home-delivered, user-tailored exercises for both healthy elderly and neurological subjects