248 research outputs found
Validity and Reliability of a Smartphone App for Gait and Balance Assessment
Advances in technology provide an opportunity to enhance the accuracy of gait and balance assessment, improving the diagnosis and rehabilitation processes for people with acute or chronic health conditions. This study investigated the validity and reliability of a smartphone-based application to measure postural stability and spatiotemporal aspects of gait during four static balance and two gait tasks. Thirty healthy participants (aged 20–69 years) performed the following tasks: (1) standing on a firm surface with eyes opened, (2) standing on a firm surface with eyes closed, (3) standing on a compliant surface with eyes open, (4) standing on a compliant surface with eyes closed, (5) walking in a straight line, and (6) walking in a straight line while turning their head from side to side. During these tasks, the app quantified the participants’ postural stability and spatiotemporal gait parameters. The concurrent validity of the smartphone app with respect to a 3D motion capture system was evaluated using partial Pearson’s correlations (r(p)) and limits of the agreement (LoA%). The within-session test–retest reliability over three repeated measures was assessed with the intraclass correlation coefficient (ICC) and the standard error of measurement (SEM). One-way repeated measures analyses of variance (ANOVAs) were used to evaluate responsiveness to differences across tasks and repetitions. Periodicity index, step length, step time, and walking speed during the gait tasks and postural stability outcomes during the static tasks showed moderate-to-excellent validity (0.55 ≤ r(p) ≤ 0.98; 3% ≤ LoA% ≤ 12%) and reliability scores (0.52 ≤ ICC ≤ 0.92; 1% ≤ SEM% ≤ 6%) when the repetition effect was removed. Conversely, step variability and asymmetry parameters during both gait tasks generally showed poor validity and reliability except step length asymmetry, which showed moderate reliability (0.53 ≤ ICC ≤ 0.62) in both tasks when the repetition effect was removed. Postural stability and spatiotemporal gait parameters were found responsive (p < 0.05) to differences across tasks and test repetitions. Along with sound clinical judgement, the app can potentially be used in clinical practice to detect gait and balance impairments and track the effectiveness of rehabilitation programs. Further evaluation and refinement of the app in people with significant gait and balance deficits is needed
Neurological Tremor: Sensors, Signal Processing and Emerging Applications
Neurological tremor is the most common movement disorder, affecting more than 4% of elderly people. Tremor is a non linear and non stationary phenomenon, which is increasingly recognized. The issue of selection of sensors is central in the characterization of tremor. This paper reviews the state-of-the-art instrumentation and methods of signal processing for tremor occurring in humans. We describe the advantages and disadvantages of the most commonly used sensors, as well as the emerging wearable sensors being developed to assess tremor instantaneously. We discuss the current limitations and the future applications such as the integration of tremor sensors in BCIs (brain-computer interfaces) and the need for sensor fusion approaches for wearable solutions
Rehabilitation Engineering in Parkinson's disease
Impairment of postural control is a common consequence of Parkinson's disease (PD) that becomes more and more critical with the progression of the disease, in spite of the available medications. Postural instability is one of the most disabling features of PD and induces difficulties with postural transitions, initiation of movements, gait disorders, inability to live independently at home, and is the major cause of falls. Falls are frequent (with over 38% falling each year) and may induce adverse consequences like soft tissue injuries, hip fractures, and immobility due to fear of falling. As the disease progresses, both postural instability and fear of falling worsen, which leads patients with PD to become increasingly immobilized.
The main aims of this dissertation are to: 1) detect and assess, in a quantitative way, impairments of postural control in PD subjects, investigate the central mechanisms that control such motor performance, and how these mechanism are affected by levodopa; 2) develop and validate a protocol, using wearable inertial sensors, to measure postural sway and postural transitions prior to step initiation; 3) find quantitative measures sensitive to impairments of postural control in early stages of PD and quantitative biomarkers of disease progression; and 4) test the feasibility and effects of a recently-developed audio-biofeedback system in maintaining balance in subjects with PD.
In the first set of studies, we showed how PD reduces functional limits of stability as well as the magnitude and velocity of postural preparation during voluntary, forward and backward leaning while standing. Levodopa improves the limits of stability but not the postural strategies used to achieve the leaning. Further, we found a strong relationship between backward voluntary limits of stability and size of automatic postural response to backward perturbations in control subjects and in PD subjects ON medication. Such relation might suggest that the central nervous system presets postural response parameters based on perceived maximum limits and this presetting is absent in PD patients OFF medication but restored with levodopa replacement.
Furthermore, we investigated how the size of preparatory postural adjustments (APAs) prior to step initiation depend on initial stance width. We found that patients with PD did not scale up the size of their APA with stance width as much as control subjects so they had much more difficulty initiating a step from a wide stance than from a narrow stance. This results supports the hypothesis that subjects with PD maintain a narrow stance as a compensation for their inability to sufficiently increase the size of their lateral APA to allow speedy step initiation in wide stance.
In the second set of studies, we demonstrated that it is possible to use wearable accelerometers to quantify postural performance during quiet stance and step initiation balance tasks in healthy subjects. We used a model to predict center of pressure displacements associated with accelerations at the upper and lower back and thigh. This approach allows the measurement of balance control without the use of a force platform outside the laboratory environment.
We used wearable accelerometers on a population of early, untreated PD patients, and found that postural control in stance and postural preparation prior to a step are impaired early in the disease when the typical balance and gait intiation symptoms are not yet clearly manifested. These novel results suggest that technological measures of postural control can be more sensitive than clinical measures. Furthermore, we assessed spontaneous sway and step initiation longitudinally across 1 year in patients with early, untreated PD. We found that changes in trunk sway, and especially movement smoothness, measured as Jerk, could be used as an objective measure of PD and its progression.
In the third set of studies, we studied the feasibility of adapting an existing audio-biofeedback device to improve balance control in patients with PD. Preliminary results showed that PD subjects found the system easy-to-use and helpful, and they were able to correctly follow the audio information when available. Audiobiofeedback improved the properties of trunk sway during quiet stance.
Our results have many implications for i) the understanding the central mechanisms that control postural motor performance, and how these mechanisms are affected by levodopa; ii) the design of innovative protocols for measuring and remote monitoring of motor performance in the elderly or subjects with PD; and iii) the development of technologies for improving balance, mobility, and consequently quality of life in patients with balance disorders, such as PD patients with augmented biofeedback paradigms
A wearable biofeedback device to improve motor symptoms in Parkinson’s disease
Dissertação de mestrado em Engenharia BiomédicaThis dissertation presents the work done during the fifth year of the course Integrated Master’s in
Biomedical Engineering, in Medical Electronics. This work was carried out in the Biomedical & Bioinspired
Robotic Devices Lab (BiRD Lab) at the MicroElectroMechanics Center (CMEMS) established at the
University of Minho. For validation purposes and data acquisition, it was developed a collaboration with
the Clinical Academic Center (2CA), located at Braga Hospital.
The knowledge acquired in the development of this master thesis is linked to the motor
rehabilitation and assistance of abnormal gait caused by a neurological disease. Indeed, this dissertation
has two main goals: (1) validate a wearable biofeedback system (WBS) used for Parkinson's disease
patients (PD); and (2) develop a digital biomarker of PD based on kinematic-driven data acquired with the
WBS. The first goal aims to study the effects of vibrotactile biofeedback to play an augmentative role to
help PD patients mitigate gait-associated impairments, while the second goal seeks to bring a step
advance in the use of front-end algorithms to develop a biomarker of PD based on inertial data acquired
with wearable devices. Indeed, a WBS is intended to provide motor rehabilitation & assistance, but also
to be used as a clinical decision support tool for the classification of the motor disability level. This system
provides vibrotactile feedback to PD patients, so that they can integrate it into their normal physiological
gait system, allowing them to overcome their gait difficulties related to the level/degree of the disease.
The system is based on a user- centered design, considering the end-user driven, multitasking and less
cognitive effort concepts.
This manuscript presents all steps taken along this dissertation regarding: the literature review and
respective critical analysis; implemented tech-based procedures; validation outcomes complemented with
results discussion; and main conclusions and future challenges.Esta dissertação apresenta o trabalho realizado durante o quinto ano do curso Mestrado
Integrado em Engenharia Biomédica, em Eletrónica Médica. Este trabalho foi realizado no Biomedical &
Bioinspired Robotic Devices Lab (BiRD Lab) no MicroElectroMechanics Center (CMEMS) estabelecido na
Universidade do Minho. Para efeitos de validação e aquisição de dados, foi desenvolvida uma colaboração
com Clinical Academic Center (2CA), localizado no Hospital de Braga.
Os conhecimentos adquiridos no desenvolvimento desta tese de mestrado estĂŁo ligados Ă
reabilitação motora e assistência de marcha anormal causada por uma doença neurológica. De facto,
esta dissertação tem dois objetivos principais: (1) validar um sistema de biofeedback vestĂvel (WBS)
utilizado por doentes com doença de Parkinson (DP); e (2) desenvolver um biomarcador digital de PD
baseado em dados cinemáticos adquiridos com o WBS. O primeiro objetivo visa o estudo dos efeitos do
biofeedback vibrotáctil para desempenhar um papel de reforço para ajudar os pacientes com PD a mitigar
as deficiências associadas à marcha, enquanto o segundo objetivo procura trazer um avanço na utilização
de algoritmos front-end para biomarcar PD baseado em dados inerciais adquiridos com o dispositivos
vestĂvel. De facto, a partir de um WBS pretende-se fornecer reabilitação motora e assistĂŞncia, mas
tambĂ©m utilizá-lo como ferramenta de apoio Ă decisĂŁo clĂnica para a classificação do nĂvel de deficiĂŞncia
motora. Este sistema fornece feedback vibrotáctil aos pacientes com PD, para que possam integrá-lo no
seu sistema de marcha fisiolĂłgica normal, permitindo-lhes ultrapassar as suas dificuldades de marcha
relacionadas com o nĂvel/grau da doença. O sistema baseia-se numa conceção centrada no utilizador,
considerando o utilizador final, multitarefas e conceitos de esforço menos cognitivo.
Portanto, este manuscrito apresenta todos os passos dados ao longo desta dissertação
relativamente a: revisĂŁo da literatura e respetiva análise crĂtica; procedimentos de base tecnolĂłgica
implementados; resultados de validação complementados com discussão de resultados; e principais
conclusões e desafios futuros
Technology in Parkinson's disease:challenges and opportunities
The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capture of more and previously inaccessible phenomena in Parkinson's disease (PD). However, more information has not translated into a greater understanding of disease complexity to satisfy diagnostic and therapeutic needs. Challenges include noncompatible technology platforms, the need for wide-scale and long-term deployment of sensor technology (among vulnerable elderly patients in particular), and the gap between the "big data" acquired with sensitive measurement technologies and their limited clinical application. Major opportunities could be realized if new technologies are developed as part of open-source and/or open-hardware platforms that enable multichannel data capture sensitive to the broad range of motor and nonmotor problems that characterize PD and are adaptable into self-adjusting, individualized treatment delivery systems. The International Parkinson and Movement Disorders Society Task Force on Technology is entrusted to convene engineers, clinicians, researchers, and patients to promote the development of integrated measurement and closed-loop therapeutic systems with high patient adherence that also serve to (1) encourage the adoption of clinico-pathophysiologic phenotyping and early detection of critical disease milestones, (2) enhance the tailoring of symptomatic therapy, (3) improve subgroup targeting of patients for future testing of disease-modifying treatments, and (4) identify objective biomarkers to improve the longitudinal tracking of impairments in clinical care and research. This article summarizes the work carried out by the task force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and the quality of life of individuals with PD. © 2016 International Parkinson and Movement Disorder Society
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
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
Exploration of digital biomarkers in chronic low back pain and Parkinson’s disease
Chronic pain and Parkinson’s disease are illnesses with personal disease progression, symptoms, and the experience of these. The ability to measure and monitor the symptoms by digitally and remotely is still limited. The aim was to study the usability and feasibility of real-world data from wearables, mobile devices, and patients in exploring digital biomarkers in these diseases. The key hypothesis was that this allows us to measure, analyse and detect clinically valid digital signals in movement, heart rate and skin conductance data.
The laboratory grade data in chronic pain were collected in an open feasibility study by using a program and built-in sensors in virtual reality devices. The real-world data were collected with a randomized clinical study by clinical assessments, built-in sensors, and two wearables. The laboratory grade dataset in Parkinson’s disease was obtained from Michael J. Fox Foundation. It contained sensor data from three wearables with clinical assessments. The real-world data were collected with a clinical study by clinical assessments, a wearable, and a mobile application. With both diseases the laboratory grade data were first explored, before the real-world data were analyzed.
The classification of chronic pain patients with the laboratory grade movement data was possible with a high accuracy. A novel real-world digital signal that correlates with clinical outcomes was found in chronic low back pain patients. A model that was able to detect different movement states was developed with laboratory grade Parkinson’s disease data. A detection of these states followed by the quantification of symptoms was found to be a potential method for the future. The usability of data collection methods in both diseases were found promising.
In the future the analyses of movement data in these diseases could be further researched and validated as a movement based digital biomarkers to be used as a surrogate or additional endpoint. Combining the data science with the optimal usability enables the exploitation of digital biomarkers in clinical trials and treatment.Digitaalisten biomarkkereiden tunnistaminen kroonisessä alaselkäkivussa ja Parkinsonin taudissa
Krooninen kipu ja Parkinsonin tauti ovat oireiden, oirekokemuksen sekä taudin kehittymisen osalta yksilöllisiä sairauksia. Kyky mitata ja seurata oireita etänä on vielä alkeellista. Väitöskirjassa tutkittiin kaupallisten mobiili- ja älylaitteiden hyödyntämistä digitaalisten biomarkkereiden löytämisessä näissä taudeissa. Pääolettamus oli, että kaupallisten älylaitteiden avulla kyetään tunnistamaan kliinisesti hyödyllisiä digitaalisia signaaleja.
Kroonisen kivun laboratorio-tasoinen data kerättiin tätä varten kehitettyä ohjelmistoa sekä kaupallisia antureita käyttäen. Reaaliaikainen kipudata kerättiin erillisen hoito-ohjelmiston tehoa ja turvallisuutta mitanneessa kliinisessä tutkimuksessa sekä kliinisiä arviointeja että anturidataa hyödyntäen. Laboratorio-tasoinena datana Parkinsonin taudissa käytettiin Michael J. Fox Foundationin kolmella eri älylaitteella ja kliinisin arvioinnein kerättyä dataa. Reaaliaikainen data kerättiin käyttäen kliinisia arviointeja, älyranneketta ja mobiilisovellusta. Molempien indikaatioiden kohdalla laboratoriodatalle tehtyä eksploratiivista analyysia hyödynnettiin itse reaaliaikaisen datan analysoinnissa.
Kipupotilaiden tunnistaminen laboratorio-tasoisesta liikedatasta oli mahdollista korkealla tarkkuudella. Reaaliaikaisesta liikedatasta löytyi uusi kliinisten arviointien kanssa korreloiva digitaalinen signaali. Parkinsonin taudin datasta kehitettiin uusi liiketyyppien tunnistamiseen tarkoitettu koneoppimis-malli. Sen hyödyntäminen liikedatan liiketyyppien tunnistamisessa ennen varsinaista oireiden mittausta on lupaava menetelmä. Käytettävyys molempien tautien reaaliaikaisissa mittausmenetelmissä havaittiin toimivaksi. Reaaliaikaiseen, kaupallisin laittein kerättävään liikedataan pohjautuvat digitaaliset biomarkkerit ovat lupaava kohde jatkotutkimukselle. Uusien analyysimenetelmien yhdistäminen optimaaliseen käytettävyyteen mahdollistaa tulevaisuudessa digitaalisten biomarkkereiden hyödyntämisen sekä kroonisten tautien kliinisessä tutkimuksessa että itse hoidossa
Smart-device based motor function battery, A
2018 Fall.Includes bibliographical references.Growth in the older population will increase the overall impact of age-related neurological disorders. Aging and neurological conditions share features such as impaired motor function and physical dysfunction including reduced muscle strength and power, slowness of movement, increased movement variability and balance dysfunction. Successful performance of daily activities and maintenance of mobility is key to independence and quality of life. Therefore, tracking changes in physical function is critical in gauging quality of life. However truly quantitative measures of physical capacity often require the use of expensive, lab-based equipment. Smart devices contain sensitive tri-axial accelerometers and gyroscopes that measure acceleration and rotation and offer a more cost-effective, portable yet still quantitative means of physical assessment. The purpose is to describe an iPod Touch-instrumented test battery designed to assess features of physical and motor function often shared by normal aging and age-related movement disorders. We have been assessing the correlation between measures taken from expensive lab devices and the iPod Touch smart device for a variety of movements. We developed and tested a multi-item smart device-based battery of motor tasks that addresses motor variability, slowness and postural instability across a range of young, healthy college students. By changing the location of the device we can assess upper and lower limb movement speed and power, hand tremor, or postural control. We have also used previously validated lab devices concurrently with the smart device, which allows us to correlate the results between devices to assess the extent of the association between devices. Outcomes such as peak acceleration and variability of movements can be obtained. Generally, the smart device demonstrated strong correlations with the lab grade sensors for all motor tasks. Furthermore, the smart device was also correlated with the accelerometer across a large range of speed and variability. Strong correlations were seen in ballistic arm and leg tasks, tremor, and postural control assessments. This finding suggests that the smart device can sufficiently assess a broad range of functional capacity. This battery can then be used to study populations exhibiting motor impairment, ranging from older adults, to neurological patients. Using the sensors on the smart device, this testing can be administered remotely and inexpensively by non-experts, providing cost-effective, mobile, user- and patient-friendly physical function testing. More importantly, accessibility of testing is increased while retaining quantitative precision. This should aid in quantifying disease progression and response to pharmacological or exercise/rehabilitative intervention, with the goal of improved function and quality of life in those with impairment
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