Performance of a Mobile 3D Camera to Evaluate Simulated Pathological Gait in Practical Scenarios

Three-dimensional (3D) cameras used for gait assessment obviate the need for bodily markers or sensors, making them particularly interesting for clinical applications. Due to their limited field of view, their application has predominantly focused on evaluating gait patterns within short walking distances. However, assessment of gait consistency requires testing over a longer walking distance. The aim of this study is to validate the accuracy for gait assessment of a previously developed method that determines walking spatiotemporal parameters and kinematics measured with a 3D camera mounted on a mobile robot base (ROBOGait). Walking parameters measured with this system were compared with measurements with Xsens IMUs. The experiments were performed on a non-linear corridor of approximately 50 m, resembling the environment of a conventional rehabilitation facility. Eleven individuals exhibiting normal motor function were recruited to walk and to simulate gait patterns representative of common neurological conditions: Cerebral Palsy, Multiple Sclerosis, and Cerebellar Ataxia. Generalized estimating equations were used to determine statistical differences between the measurement systems and between walking conditions. When comparing walking parameters between paired measures of the systems, significant differences were found for eight out of 18 descriptors: range of motion (ROM) of trunk and pelvis tilt, maximum knee flexion in loading response, knee position at toe-off, stride length, step time, cadence; and stance duration. When analyzing how ROBOGait can distinguish simulated pathological gait from physiological gait, a mean accuracy of 70.4%, a sensitivity of 49.3%, and a specificity of 74.4% were found when compared with the Xsens system. The most important gait abnormalities related to the clinical conditions were successfully detected by ROBOGait. The descriptors that best distinguished simulated pathological walking from normal walking in both systems were step width and stride length. This study underscores the promising potential of 3D cameras and encourages exploring their use in clinical gait analysis.Biomechatronics & Human-Machine Contro

Performance of a Mobile 3D Camera to Evaluate Simulated Pathological Gait in Practical Scenarios

Three-dimensional (3D) cameras used for gait assessment obviate the need for bodily markers or sensors, making them particularly interesting for clinical applications. Due to their limited field of view, their application has predominantly focused on evaluating gait patterns within short walking distances. However, assessment of gait consistency requires testing over a longer walking distance. The aim of this study is to validate the accuracy for gait assessment of a previously developed method that determines walking spatiotemporal parameters and kinematics measured with a 3D camera mounted on a mobile robot base (ROBOGait). Walking parameters measured with this system were compared with measurements with Xsens IMUs. The experiments were performed on a non-linear corridor of approximately 50 m, resembling the environment of a conventional rehabilitation facility. Eleven individuals exhibiting normal motor function were recruited to walk and to simulate gait patterns representative of common neurological conditions: Cerebral Palsy, Multiple Sclerosis, and Cerebellar Ataxia. Generalized estimating equations were used to determine statistical differences between the measurement systems and between walking conditions. When comparing walking parameters between paired measures of the systems, significant differences were found for eight out of 18 descriptors: range of motion (ROM) of trunk and pelvis tilt, maximum knee flexion in loading response, knee position at toe-off, stride length, step time, cadence; and stance duration. When analyzing how ROBOGait can distinguish simulated pathological gait from physiological gait, a mean accuracy of 70.4%, a sensitivity of 49.3%, and a specificity of 74.4% were found when compared with the Xsens system. The most important gait abnormalities related to the clinical conditions were successfully detected by ROBOGait. The descriptors that best distinguished simulated pathological walking from normal walking in both systems were step width and stride length. This study underscores the promising potential of 3D cameras and encourages exploring their use in clinical gait analysis.</p

Motor patterns evaluation of people with neuromuscular disorders for biomechanical risk management and job integration/reintegration

Neurological diseases are now the most common pathological condition and the leading cause of disability, progressively worsening the quality of life of those affected. Because of their high prevalence, they are also a social issue, burdening both the national health service and the working environment. It is therefore crucial to be able to characterize altered motor patterns in order to develop appropriate rehabilitation treatments with the primary goal of restoring patients' daily lives and optimizing their working abilities. In this thesis, I present a collection of published scientific articles I co-authored as well as two in progress in which we looked for appropriate indices for characterizing motor patterns of people with neuromuscular disorders that could be used to plan rehabilitation and job accommodation programs. We used instrumentation for motion analysis and wearable inertial sensors to compute kinematic, kinetic and electromyographic indices. These indices proved to be a useful tool for not only developing and validating a clinical and ergonomic rehabilitation pathway, but also for designing more ergonomic prosthetic and orthotic devices and controlling collaborative robots

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

Mechanisms of Sensorimotor Impairment in Multiple Sclerosis

Sensorimotor impairments in people with multiple sclerosis (MS) might alter coordination and balance strategy during functional movements. People with MS often have symptoms such as weakness and discoordination in the lower limbs, resulting in poor walking and balance function. This decrease in function can result in falls, decreased community activity, unemployment, and reduced quality of life. As MS is a progressive disease resulting in a range of dysfunction, the amount of lower limb impairment can cause changes to walking and balance strategies to maintain functional performance. The overall objective of this dissertation was to quantify the impairment at the hip and ankle, and characterize the effects of impairment on walking and balance in MS. To quantify the lower limb impairment, a custom-built robot was used to impose movement to the legs about the hip and ankle joint separately. Joint torque and work done were used as quantitative measures of strength during isometric contraction and coordination during subject assisted leg movements in MS and healthy control subjects. To characterize the effect of impairment on functional movements, motion analysis was used to record kinematic and kinetic parameters during overground walking and during a challenging arm tracking task in standing. Hip and ankle sagittal moments were used to quantify the contribution of each joint to functional movement. The findings from these studies suggest that there is a greater sensorimotor impairment at the ankle than the hip in MS, resulting in a reduced reliance on the ankle during walking and an increased hip versus ankle strategy during upper body movements. This was observed by increased negative work at the ankle during assisted bilateral leg movements, reduced ankle moments during stance in gait, and increased hip versus ankle contribution during arm tracking movements in standing. These results indicate that differential impairment between the hip and ankle can drive changes to walking and balance strategy to maintain functional performance, highlighting the importance of joint specific rehabilitation methods in improving function in MS

Effects of demographic factors for fatigue detection in manufacturing

Over the years, advancement in automation technology is allowed the increased integration of humans and machines in a manufacturing environment, these days fewer humans. The use of Knowledge-based Systems in improving and converting human overall performance has been restrained in truth because of a lack of expertise of the way an individual’s overall performance deteriorates with fatigue buildup, which may range from employees to the work environment. As a result, the performance benefits of increased automation in a manufacturing environment, as well as the impact of human factors, must be taken into account. To predict fatigue in physically demanding tasks, this study takes a data-driven strategy. The influence of demographic characteristics, their physical fatigue states, detected workloads, and reactivity to physiological changes are investigated through sensors (Inertial Measurement Unit; IMU and Heart Rate Variability; HRV) in this paper. A framework is established for the selection of key features, machine learning algorithms, and evaluating subjective measures. To attain that, specific application scenarios of the framework are shown, each for different sorts of manufacturing-related tasks

The influence of the context on mobility in neurological disorders: a wearable technology approach

The evaluation of mobility of patients with neurodegenerative diseases is crucial for healthcare professionals to tailor individual treatments and track disease progression. More individualized treatment has high potential to improve quality of life and mobility, and decrease fall risk. Nowadays mobility is mainly assessed during clinical examinations. However, with the rise of digital wearable technology, it has become possible to quantify mobility objectively in different settings. It is however unclear how mobility data collected in different settings, or more general different contexts, are associated with each other. Therefore, the aim of this dissertation was to understand the influence of context on mobility in older adults and patients with neurodegenerative disorders. The first study revealed that supervised capacity and unsupervised performance measures can substantially differ from each other. Consequently, both measures provide complementary information that can be used to gain a better understanding of daily function. In the second study an algorithm to quantify arm swing was developed and validated. This algorithm was used in the third study, which showed that the effect of dopaminergic medication on arm swing in patients with PD is influenced by medication state and task complexity. We therefore highly recommend to assess patients in different contexts to get a better understanding of the effect of treatment or the disease progression. To be able to assess mobility of patients in different context more wearable sensor-based algorithms are required. With the dataset introduced in the last paper, an indefinite number of additional movement and mobility algorithms can be developed and validated. The development and validation of these algorithms can further move our understanding of the influence of context on mobility forward

Prediction of Fall Risk Among Community-Dwelling Older Adults Using a Wearable System

Falls are among the most common cause of decreased mobility and independence in older adults and rank as one of the most severe public health problems with frequent fatal consequences. In the present study, gait characteristics from 171 community-dwelling older adults were evaluated to determine their predictive ability for future falls using a wearable system. Participants wore a wearable sensor (inertial measurement unit, IMU) affixed to the sternum and performed a 10-m walking test. Measures of gait variability, complexity, and smoothness were extracted from each participant, and prospective fall incidence was evaluated over the following 6-months. Gait parameters were refined to better represent features for a random forest classifier for the fall-risk classification utilizing three experiments. The results show that the best-trained model for faller classification used both linear and nonlinear gait parameters and achieved an overall 81.6 ± 0.7% accuracy, 86.7 ± 0.5% sensitivity, 80.3 ± 0.2% specificity in the blind test. These findings augment the wearable sensor\u27s potential as an ambulatory fall risk identification tool in community-dwelling settings. Furthermore, they highlight the importance of gait features that rely less on event detection methods, and more on time series analysis techniques. Fall prevention is a critical component in older individuals’ healthcare, and simple models based on gait-related tasks and a wearable IMU sensor can determine the risk of future falls

Wearables for Movement Analysis in Healthcare

Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes