Applications of MEMS Gyroscope for Human Gait Analysis

After decades of development, quantitative instruments for human gait analysis have become an important tool for revealing underlying pathologies manifested by gait abnormalities. However, the gold standard instruments (e.g., optical motion capture systems) are commonly expensive and complex while needing expert operation and maintenance and thereby be limited to a small number of specialized gait laboratories. Therefore, in current clinical settings, gait analysis still mainly relies on visual observation and assessment. Due to recent developments in microelectromechanical systems (MEMS) technology, the cost and size of gyroscopes are decreasing, while the accuracy is being improved, which provides an effective way for qualifying gait features. This chapter aims to give a close examination of human gait patterns (normal and abnormal) using gyroscope-based wearable technology. Both healthy subjects and hemiparesis patients participated in the experiment, and experimental results show that foot-mounted gyroscopes could assess gait abnormalities in both temporal and spatial domains. Gait analysis systems constructed of wearable gyroscopes can be more easily used in both clinical and home environments than their gold standard counterparts, which have few requirements for operation, maintenance, and working environment, thereby suggesting a promising future for gait analysis

Objective assessment of motor and gait parameters of patients with multiple sclerosis

Multiple Sclerosis (MS) is a chronic inflammatory disease of the central nervous system. It affects approximately 400.000 individuals in Europe and about 2.5 million worldwide. Clinical symptoms of MS are highly variable and depend on the localization of lesions in the brain and spinal cord. Patients with chronic progressive neurological diseases such as MS typically show a decrease of physical activity as compared with healthy individuals. Approximately 75 to 80 percent of patients with MS (PwMS) experience walking and physical activity impairment in early stages of the disease. Therefore, walking impairment is considered as a hallmark symptom as this may have a significant impact on different daily activities. Moreover, an indirect association between overall MS symptoms and physical activity was found. Several studies investigated the walking ability and physical activity under free-living conditions in PwMS, as this may provide significant information to predict the patient’s health status. Different methods have been used for this purpose, including subjective approaches like self-report, questionnaires or diary methods. Although these methods are inexpensive and can easily be employed preferably in large scale studies, they are prone to error due to memory failure and other kind of misreporting. For many years, laboratory analysis systems have been considered to be the “gold standard” for physical activity and walking ability assessment. Nevertheless, these methods require extensive technical support and are unable to assess unconstrained physical activities in free-living situations. Thus, there is increasing interest in ambulatory assessment methods that provide objective measures of physical activity and gait parameters. Therefore, this thesis takes a different approach and investigate the usage of an objective monitoring system to early detect the slightly changes in disease-related walking ability and gait abnormality using one accelerometer. Moreover, this work aims to classify the derived acceleration data regarding their response to a certain intervention and treatment. In doing so, first of all, different algorithms were developed to extract activity and gait parameters in time, frequency and time-frequency domain. Then a Home-based system was developed and provided to help doctors monitor the changes in the ambulatory physical activity of PwMS objectively. The developed system was applied in two different studies over long period of time (one year) to assess changes in physical activity and gait behavior of PwMS and to classify their response to medical treatment. The aim of the first study was to investigate the ability of the developed parameters to objectively capture the changes in motor and walking ability in PwMS. Moreover, the objective was to provide additional evidence from long-term design study that support the association between changes in physical activity and walking ability and disease progression over time. The aim of the second study was to investigate the effectiveness of the medication treatment using the developed gait parameters and the assessment system developed in this work. The result of the study was compared to those assessed in the clinic. Comprehensive analysis of gait features in frequency and time-frequency domain can provide complementary information to understand gait patterns. Therefore, in this study, the parameters peak frequency and energy concentration were integrated along with time-domain parameters, such as step counts and walking speed. In case of chronic diseases, such as MS, medical benefit is the main factor to accept new technology. Thus, the developed system should be advantageous for diagnosis and therapy of MS. Moreover, it is important for the physician to be able to get better overview of the medical data about the disease course and health condition of their patients. Therefore, many critical factors regarding medical, technical and user specific aspects were considered in this work while developing the ambulatory assessment system. To assess the acceptance of the system a questionnaire was designed with main focus on two factors; usefulness and ease-of-use. The questionnaire was based on the Technology Acceptance Model (TAM). As a result, the design, validation and clinical application of Home-based monitoring system and algorithmic methods developed in this thesis offer the opportunity to comprehensively and objectively assess the pattern of behavioral change in physical activity and walking ability using one sensor across prolonged periods of time. The derived information may assist in the process of clinical decision making in the context of neurological rehabilitation and intervention (evaluation of medication or physiotherapy effects) and thus help to eventually improve the patients’ quality of life. In this work the focus was on patients with multiple sclerosis, however the developed and evaluated system can be adapted to other chronic diseases with physical activity disorders and impairment of gait

Sit-to-Stand Phases Detection by Inertial Sensors

The Sit-to-Stand(STS) is defined as the transition from the sitting to standing position. It is commonly adopted in clinical practice because musculoskeletal or neurological degenerative disorders, as well as the natural process of ageing, deter-mine an increased difficulty in rising up from a seated position. This study aimed to detect the Sit To Stand phases using data from inertial sensors. Due to the high variability of this movement, and, consequently the difficulty to define events by thresholds, we used the machine learning. We collected data from 27 participants (13 females,24.37\ub13.32 years old). They wore 10 Inertial Sensors placed on: trunk,back(L4-L5),left and right thigh, tibia, and ankles. The par-ticipants were asked to stand from an height adjustable chair for 10 times. The STS exercises were recorded separately. The starting and ending points of each phase were identified by key events. The pre-processing included phases splitting in epochs. The features extracted were: mean, standard deviation, RMS, Max and min, COV and first derivative. The features were on the epochs for each sensor. To identify the most fitting classifier, two classifier algorithms,K-nearest Neighbours( KNN) and Support Vector Machine (SVM) were trained. From the data recorded, four dataset were created varying the epochs duration, the number of sensors. The validation model used to train the classifier. As validation model, we compared the results of classifiers trained using Kfold and Leave One Subject out (LOSO) models. The classifier performances were evaluated by confusion matrices and the F1 scores. The classifiers trained using LOSO technique as validation model showed higher values of predictive accuracy than the ones trained using Kfold. The predictive accuracy of KNN and SVM were reported below: \u2022 KFold \u2013 mean of overall predictive accuracy KNN: 0.75; F1 score: REST 0.86, TRUNK LEANING 0.35,STANDING 0.60,BALANCE 0.54, SITTING 0.55 \u2013 mean of overall predictive accuracy SVM: 0.75; F1 score: REST 0.89, TRUNK LEANING 0.48,STANDING 0.48,BALANCE 0.59, SITTING 0.62 \u2022 LOSO \u2013 mean of overall predictive accuracy KNN: 0.93; F1 score: REST 0.96, TRUNK LEANING 0.79,STANDING 0.89,BALANCE 0.95, SITTING 0.88 \u2013 mean of overall predictive accuracy SVM: 0.95; F1 score phases: REST 0.98, TRUNK LEANING 0.86,STANDING 0.91,BALANCE 0.98, SIT-TING 0.9

The use of inertial measurement units for the determination of gait spatio-temporal parameters

The aim of this work was to develop a methodology whereby inertial measurement units (IMUs) could be used to obtain accurate and objective gait parameters within typical developed adults (TDA) and Parkinson’s disease (PD). The thesis comprised four studies, the first establishing the validity of the IMU method when measuring the vertical centre of mass (CoM) acceleration, velocity and position versus an optical motion capture system (OMCS) in TDA. The second study addressed the validity of the IMU and inverted pendulum model measurements within PD and also explored the inter-rater reliability of the measurement. In the third study the optimisation of the inverted pendulum model driven by IMU data was explored when comparing to standardised clinical tests within TDA and PD, and the fourth explored a novel phase plot analysis applied to CoM movement to explore gait in more detail. The validity study showed no significant difference for vertical acceleration and position between IMU and OMCS measurements within TDA. Vertical velocity however did show a significant difference, but the error was still less than 2.5%. ICCs for all three parameters ranged from 0.782 to 0.952, indicating an adequate test-retest reliability. Within PD there was no significant difference found for vertical CoM acceleration, velocity and position. ICCs for all three parameters ranged from 0.77 to 0.982. In addition, the reliability calculations found no difference for step time, stride length and walking speed for people with PD. Inter-rater reliability was found not to be different for the same parameters. The optimisation of the correction factor when using the inverted pendulum model showed no significant difference between TDA and PD. Furthermore the correction factor was found not to be related to walking speed. The fourth and final study found that phase plot analysis of variability could be performed on CoM vertical excursion. TDA and PD were shown to have, on average, different characteristics. This thesis demonstrated that CoM motion can be objectively measured within a clinical setting in people with PD by utilizing IMUs. Furthermore, in depth gait variability analysis can be performed by utilizing a phase plot method

Objective assessment of movement disabilities using wearable sensors

The research presents a series of comprehensive analyses based on inertial measurements obtained from wearable sensors to quantitatively describe and assess human kinematic performance in certain tasks that are most related to daily life activities. This is not only a direct application of human movement analysis but also very pivotal in assessing the progression of patients undergoing rehabilitation services. Moreover, the detailed analysis will provide clinicians with greater insights to capture movement disorders and unique ataxic features regarding axial abnormalities which are not directly observed by the clinicians

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

Parkinson's Disease Management through ICT

Parkinson's Disease (PD) is a neurodegenerative disorder that manifests with motor and non-motor symptoms. PD treatment is symptomatic and tries to alleviate the associated symptoms through an adjustment of the medication. As the disease is evolving and this evolution is patient specific, it could be very difficult to properly manage the disease.The current available technology (electronics, communication, computing, etc.), correctly combined with wearables, can be of great use for obtaining and processing useful information for both clinicians and patients allowing them to become actively involved in their condition.Parkinson's Disease Management through ICT: The REMPARK Approach presents the work done, main results and conclusions of the REMPARK project (2011 – 2015) funded by the European Union under contract FP7-ICT-2011-7-287677. REMPARK system was proposed and developed as a real Personal Health Device for the Remote and Autonomous Management of Parkinson’s Disease, composed of different levels of interaction with the patient, clinician and carers, and integrating a set of interconnected sub-systems: sensor, auditory cueing, Smartphone and server. The sensor subsystem, using embedded algorithmics, is able to detect the motor symptoms associated with PD in real time. This information, sent through the Smartphone to the REMPARK server, is used for an efficient management of the disease

Inertial Measurement Unit-Based Gait Event Detection in Healthy and Neurological Cohorts: A Walk in the Dark

A deep learning (DL)-based network is developed to determine gait events from IMU data from a shank- or foot-worn device. The DL network takes as input the raw IMU data and predicts for each time step the probability that it corresponds to an initial or final contact. The algorithm is validated for walking at different self-selected speeds across multiple neurological diseases and both in clinical research settings and the habitual environment. The algorithms shows a high detection rate for initial and final contacts, and a small time error when compared to reference events obtained with an optical motion capture system or pressure insoles. Based on the excellent performance, it is concluded that the DL algorithm is well suited for continuous long-term monitoring of gait in the habitual environment

Parkinson's Disease Management through ICT

Parkinson's Disease (PD) is a neurodegenerative disorder that manifests with motor and non-motor symptoms. PD treatment is symptomatic and tries to alleviate the associated symptoms through an adjustment of the medication. As the disease is evolving and this evolution is patient specific, it could be very difficult to properly manage the disease.The current available technology (electronics, communication, computing, etc.), correctly combined with wearables, can be of great use for obtaining and processing useful information for both clinicians and patients allowing them to become actively involved in their condition.Parkinson's Disease Management through ICT: The REMPARK Approach presents the work done, main results and conclusions of the REMPARK project (2011 – 2015) funded by the European Union under contract FP7-ICT-2011-7-287677. REMPARK system was proposed and developed as a real Personal Health Device for the Remote and Autonomous Management of Parkinson’s Disease, composed of different levels of interaction with the patient, clinician and carers, and integrating a set of interconnected sub-systems: sensor, auditory cueing, Smartphone and server. The sensor subsystem, using embedded algorithmics, is able to detect the motor symptoms associated with PD in real time. This information, sent through the Smartphone to the REMPARK server, is used for an efficient management of the disease