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https://www.readbyqxmd.com/read/29769594/touchscreen-typing-pattern-analysis-for-detecting-fine-motor-skills-decline-in-early-stage-parkinson-s-disease
#1
Dimitrios Iakovakis, Stelios Hadjidimitriou, Vasileios Charisis, Sevasti Bostantzopoulou, Zoe Katsarou, Leontios J Hadjileontiadis
Parkinson's disease (PD) is a degenerative movement disorder causing progressive disability that severely affects patients' quality of life. While early treatment can produce significant benefits for patients, the mildness of many early signs combined with the lack of accessible high-frequency monitoring tools may delay clinical diagnosis. To meet this need, user interaction data from consumer technologies have recently been exploited towards unsupervised screening for PD symptoms in daily life. Similarly, this work proposes a method for detecting fine motor skills decline in early PD patients via analysis of patterns emerging from finger interaction with touchscreen smartphones during natural typing...
May 16, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29708870/validation-of-freezing-of-gait-monitoring-using-smartphone
#2
Han Byul Kim, Hong Ji Lee, Woong Woo Lee, Sang Kyong Kim, Hyo Seon Jeon, Hye Young Park, Chae Won Shin, Won Jin Yi, Beomseok Jeon, Kwang S Park
BACKGROUND: Freezing of gait (FOG) is a commonly observed motor symptom for patients with Parkinson's disease (PD). The symptoms of FOG include reduced step lengths or motor blocks, even with an evident intention of walking. FOG should be monitored carefully because it not only lowers the patient's quality of life, but also significantly increases the risk of injury. INTRODUCTION: In previous studies, patients had to wear several sensors on the body and another computing device was needed to run the FOG detection algorithm...
April 30, 2018: Telemedicine Journal and E-health: the Official Journal of the American Telemedicine Association
https://www.readbyqxmd.com/read/29701258/evaluation-of-smartphone-based-testing-to-generate-exploratory-outcome-measures-in-a-phase-1-parkinson-s-disease-clinical-trial
#3
Florian Lipsmeier, Kirsten I Taylor, Timothy Kilchenmann, Detlef Wolf, Alf Scotland, Jens Schjodt-Eriksen, Wei-Yi Cheng, Ignacio Fernandez-Garcia, Juliane Siebourg-Polster, Liping Jin, Jay Soto, Lynne Verselis, Frank Boess, Martin Koller, Michael Grundman, Andreas U Monsch, Ronald B Postuma, Anirvan Ghosh, Thomas Kremer, Christian Czech, Christian Gossens, Michael Lindemann
BACKGROUND: Ubiquitous digital technologies such as smartphone sensors promise to fundamentally change biomedical research and treatment monitoring in neurological diseases such as PD, creating a new domain of digital biomarkers. OBJECTIVES: The present study assessed the feasibility, reliability, and validity of smartphone-based digital biomarkers of PD in a clinical trial setting. METHODS: During a 6-month, phase 1b clinical trial with 44 Parkinson participants, and an independent, 45-day study in 35 age-matched healthy controls, participants completed six daily motor active tests (sustained phonation, rest tremor, postural tremor, finger-tapping, balance, and gait), then carried the smartphone during the day (passive monitoring), enabling assessment of, for example, time spent walking and sit-to-stand transitions by gyroscopic and accelerometer data...
April 27, 2018: Movement Disorders: Official Journal of the Movement Disorder Society
https://www.readbyqxmd.com/read/29695949/quantifying-motor-impairment-in-movement-disorders
#4
REVIEW
James J FitzGerald, Zhongjiao Lu, Prem Jareonsettasin, Chrystalina A Antoniades
Until recently the assessment of many movement disorders has relied on clinical rating scales that despite careful design are inherently subjective and non-linear. This makes accurate and truly observer-independent quantification difficult and limits the use of sensitive parametric statistical methods. At last, devices capable of measuring neurological problems quantitatively are becoming readily available. Examples include the use of oculometers to measure eye movements and accelerometers to measure tremor...
2018: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/29671754/a-review-of-physiological-and-behavioral-monitoring-with-digital-sensors-for-neuropsychiatric-illnesses
#5
Erik Reinertsen, Gari D Clifford
Physiological, behavioral, and psychological changes associated with neuropsychiatric illness are reflected in several related signals, including actigraphy, location, word sentiment, voice tone, social activity, heart rate, and responses to standardized questionnaires. These signals can be passively monitored using sensors in smartphones, wearable accelerometers, Holter monitors, and multimodal sensing approaches that fuse multiple data types. Connection of these devices to the internet has made large scale studies feasible and is enabling a revolution in neuropsychiatric monitoring...
May 15, 2018: Physiological Measurement
https://www.readbyqxmd.com/read/29617340/robust-stride-segmentation-of-inertial-signals-based-on-local-cyclicity-estimation
#6
Sebastijan Šprager, Matjaž B Jurič
A novel approach for stride segmentation, gait sequence extraction, and gait event detection for inertial signals is presented. The approach operates by combining different local cyclicity estimators and sensor channels, and can additionally employ a priori knowledge on the fiducial points of gait events. The approach is universal as it can work on signals acquired by different inertial measurement unit (IMU) sensor types, is template-free, and operates unsupervised. A thorough evaluation was performed with two datasets: our own collected FRIgait dataset available for open use, containing long-term inertial measurements collected from 57 subjects using smartphones within the span of more than one year, and an FAU eGait dataset containing inertial data from shoe-mounted sensors collected from three cohorts of subjects: healthy, geriatric, and Parkinson’s disease patients...
April 4, 2018: Sensors
https://www.readbyqxmd.com/read/29582075/using-smartphones-and-machine-learning-to-quantify-parkinson-disease-severity-the-mobile-parkinson-disease-score
#7
Andong Zhan, Srihari Mohan, Christopher Tarolli, Ruth B Schneider, Jamie L Adams, Saloni Sharma, Molly J Elson, Kelsey L Spear, Alistair M Glidden, Max A Little, Andreas Terzis, E Ray Dorsey, Suchi Saria
Importance: Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings. Objectives: To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy...
March 26, 2018: JAMA Neurology
https://www.readbyqxmd.com/read/29516871/big-data-in-parkinson-s-disease-using-smartphones-to-remotely-detect-longitudinal-disease-phenotypes
#8
John Prince, Siddharth Arora, Maarten de Vos
OBJECTIVE: To better understand the longitudinal characteristics of Parkinson's disease (PD) through the analysis of finger tapping and memory tests collected remotely using smartphones. APPROACH: Using a large cohort (312 PD subjects and 236 controls) of participants in the mPower study, we extract clinically validated features from a finger tapping and memory test to monitor the longitudinal behaviour of study participants. We investigate any discrepancy in learning rates associated with motor and non-motor tasks between PD subjects and healthy controls...
April 26, 2018: Physiological Measurement
https://www.readbyqxmd.com/read/29486775/neural-predictors-of-gait-stability-when-walking-freely-in-the-real-world
#9
Sara Pizzamiglio, Hassan Abdalla, Usman Naeem, Duncan L Turner
BACKGROUND: Gait impairments during real-world locomotion are common in neurological diseases. However, very little is currently known about the neural correlates of walking in the real world and on which regions of the brain are involved in regulating gait stability and performance. As a first step to understanding how neural control of gait may be impaired in neurological conditions such as Parkinson's disease, we investigated how regional brain activation might predict walking performance in the urban environment and whilst engaging with secondary tasks in healthy subjects...
February 27, 2018: Journal of Neuroengineering and Rehabilitation
https://www.readbyqxmd.com/read/29297138/design-of-a-noninvasive-and-smart-hand-tremor-attenuation-system-with-active-control-a-simulation-study
#10
Mahdi Abbasi, Aref Afsharfard, Roya Arasteh, Javad Safaie
This paper presents the design and simulation of a handheld device for people with hand tremor, such as Parkinson's and essential tremor patients. This device can be used as a pen for smartphones or as a spoon. The designed system includes two links, which are connected to two servomotors, which are mounted in orthogonal directions. To attenuate the effect of hand tremor on the tip of device, PID and computed torque methods are used to actively control the system. These controllers are used to control the rotation of the motors for moving the links in opposite directions of the hand tremor...
January 3, 2018: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/29261709/feasibility-of-large-scale-deployment-of-multiple-wearable-sensors-in-parkinson-s-disease
#11
Ana Lígia Silva de Lima, Tim Hahn, Luc J W Evers, Nienke M de Vries, Eli Cohen, Michal Afek, Lauren Bataille, Margaret Daeschler, Kasper Claes, Babak Boroojerdi, Dolors Terricabras, Max A Little, Heribert Baldus, Bastiaan R Bloem, Marjan J Faber
Wearable devices can capture objective day-to-day data about Parkinson's Disease (PD). This study aims to assess the feasibility of implementing wearable technology to collect data from multiple sensors during the daily lives of PD patients. The Parkinson@home study is an observational, two-cohort (North America, NAM; The Netherlands, NL) study. To recruit participants, different strategies were used between sites. Main enrolment criteria were self-reported diagnosis of PD, possession of a smartphone and age≥18 years...
2017: PloS One
https://www.readbyqxmd.com/read/29152558/erratum-author-correction-using-a-smartphone-based-self-management-platform-to-support-medication-adherence-and-clinical-consultation-in-parkinson-s-disease
#12
Rashmi Lakshminarayana, Duolao Wang, David Burn, K Ray Chaudhuri, Clare Galtrey, Natalie Valle Guzman, Bruce Hellman, Ben James, Suvankar Pal, Jon Stamford, Malcolm Steiger, R W Stott, James Teo, Roger A Barker, Emma Wang, Bastiaan R Bloem, Martijn van der Eijk, Lynn Rochester, Adrian Williams
[This corrects the article DOI: 10.1038/s41531-016-0003-z.].
2017: NPJ Parkinson's Disease
https://www.readbyqxmd.com/read/29075547/can-a-smartphone-diagnose-parkinson-disease-a-deep-neural-network-method-and-telediagnosis-system-implementation
#13
Y N Zhang
Parkinson's disease (PD) is primarily diagnosed by clinical examinations, such as walking test, handwriting test, and MRI diagnostic. In this paper, we propose a machine learning based PD telediagnosis method for smartphone. Classification of PD using speech records is a challenging task owing to the fact that the classification accuracy is still lower than doctor-level. Here we demonstrate automatic classification of PD using time frequency features, stacked autoencoders (SAE), and K nearest neighbor (KNN) classifier...
2017: Parkinson's Disease
https://www.readbyqxmd.com/read/29063366/time-measurement-characterization-of-stand-to-sit-and-sit-to-stand-transitions-by-using-a-smartphone
#14
Hernán A González Rojas, Pedro Chaná Cuevas, Enrique E Zayas Figueras, Salvador Cardona Foix, Antonio J Sánchez Egea
The aim of this study is to analyze a common method to measure the acceleration of a daily activity pattern by using a smartphone. In this sense, a numerical approach is proposed to transform the relative acceleration signal, recorded by a triaxial accelerometer, into an acceleration referred to an inertial reference. The integration of this acceleration allows to determine the velocity and position with respect to an inertial reference. Two different kinematic parameters are suggested to characterize the profile of the velocity during the sit-to-stand and stand-to-sit transitions for Parkinson and control subjects...
May 2018: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/29027941/verification-of-a-method-for-measuring-parkinson-s-disease-related-temporal-irregularity-in-spiral-drawings
#15
Somayeh Aghanavesi, Mevludin Memedi, Mark Dougherty, Dag Nyholm, Jerker Westin
Parkinson's disease (PD) is a progressive movement disorder caused by the death of dopamine-producing cells in the midbrain. There is a need for frequent symptom assessment, since the treatment needs to be individualized as the disease progresses. The aim of this paper was to verify and further investigate the clinimetric properties of an entropy-based method for measuring PD-related upper limb temporal irregularities during spiral drawing tasks. More specifically, properties of a temporal irregularity score (TIS) for patients at different stages of PD, and medication time points were investigated...
October 13, 2017: Sensors
https://www.readbyqxmd.com/read/28841694/differential-diagnosis-between-parkinson-s-disease-and-essential-tremor-using-the-smartphone-s-accelerometer
#16
Sergi Barrantes, Antonio J Sánchez Egea, Hernán A González Rojas, Maria J Martí, Yaroslau Compta, Francesc Valldeoriola, Ester Simo Mezquita, Eduard Tolosa, Josep Valls-Solè
BACKGROUND: The differential diagnosis between patients with essential tremor (ET) and those with Parkinson's disease (PD) whose main manifestation is tremor may be difficult unless using complex neuroimaging techniques such as 123I-FP-CIT SPECT. We considered that using smartphone's accelerometer to stablish a diagnostic test based on time-frequency differences between PD an ET could support the clinical diagnosis. METHODS: The study was carried out in 17 patients with PD, 16 patients with ET, 12 healthy volunteers and 7 patients with tremor of undecided diagnosis (TUD), who were re-evaluated one year after the first visit to reach the definite diagnosis...
2017: PloS One
https://www.readbyqxmd.com/read/28706727/pd_manager-an-mhealth-platform-for-parkinson-s-disease-patient-management
#17
Kostas M Tsiouris, Dimitrios Gatsios, George Rigas, Dragana Miljkovic, Barbara Koroušić Seljak, Marko Bohanec, Maria T Arredondo, Angelo Antonini, Spyros Konitsiotis, Dimitrios D Koutsouris, Dimitrios I Fotiadis
PD_Manager is a mobile health platform designed to cover most of the aspects regarding the management of Parkinson's disease (PD) in a holistic approach. Patients are unobtrusively monitored using commercial wrist and insole sensors paired with a smartphone, to automatically estimate the severity of most of the PD motor symptoms. Besides motor symptoms monitoring, the patient's mobile application also provides various non-motor self-evaluation tests for assessing cognition, mood and nutrition to motivate them in becoming more active in managing their disease...
June 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28688983/subjective-perception-of-sleep-benefit-in-parkinson-s-disease-valid-or-irrelevant
#18
Will Lee, Andrew Evans, David R Williams
INTRODUCTION: The phenomenon of sleep benefit (SB) in Parkinson's disease (PD), whereby waking motor function is improved despite no dopaminergic treatment overnight, is controversial. Previous studies suggested a significant discrepancy between subjective functional and objective motor improvement. The aim of this study was to determine how well subjective reporting of SB correlates with objective measures and if true motor improvement can be predicted by a standardized questionnaire...
September 2017: Parkinsonism & related Disorders
https://www.readbyqxmd.com/read/28653995/nutrinet-a-deep-learning-food-and-drink-image-recognition-system-for-dietary-assessment
#19
Simon Mezgec, Barbara Koroušić Seljak
Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet...
June 27, 2017: Nutrients
https://www.readbyqxmd.com/read/28649602/using-a-smartphone-based-self-management-platform-to-support-medication-adherence-and-clinical-consultation-in-parkinson-s-disease
#20
Rashmi Lakshminarayana, Duolao Wang, David Burn, K Ray Chaudhuri, Clare Galtrey, Natalie Valle Guzman, Bruce Hellman, Ben James, Suvankar Pal, Jon Stamford, Malcolm Steiger, R W Stott, James Teo, Roger A Barker, Emma Wang, Bastiaan R Bloem, Martijn van der Eijk, Lynn Rochester, Adrian Williams
The progressive nature of Parkinson's disease, its complex treatment regimens and the high rates of comorbid conditions make self-management and treatment adherence a challenge. Clinicians have limited face-to-face consultation time with Parkinson's disease patients, making it difficult to comprehensively address non-adherence. Here we share the results from a multi-centre (seven centres) randomised controlled trial conducted in England and Scotland to assess the impact of using a smartphone-based Parkinson's tracker app to promote patient self-management, enhance treatment adherence and quality of clinical consultation...
2017: NPJ Parkinson's Disease
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