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https://www.readbyqxmd.com/read/28814034/automated-assessment-of-symptom-severity-changes-during-deep-brain-stimulation-dbs-therapy-for-parkinson-s-disease
#1
Paolo Angeles, Yen Tai, Nicola Pavese, Samuel Wilson, Ravi Vaidyanathan
Deep brain stimulation (DBS) is currently being used as a treatment for symptoms of Parkinson's disease (PD). Tracking symptom severity progression and deciding the optimal stimulation parameters for people with PD is extremely difficult. This study presents a sensor system that can quantify the three cardinal motor symptoms of PD - rigidity, bradykinesia and tremor. The first phase of this study assesses whether data recorded from the system during physical examinations can be used to correlate to clinician's severity score using supervised machine learning (ML) models...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28814032/studying-the-implementation-of-iterative-impedance-control-for-assistive-hand-rehabilitation-using-an-exoskeleton
#2
T Martineau, R Vaidyanathan
A positive training synergy can be obtained when two individuals attempt to learn the same motor task while mechanically coupled to one another. In this paper, we have studied how mimicking this interaction through impedance control can be exploited to improve assistance delivered by hand exoskeleton devices during rehabilitation. In this context, the machine and user take complementary roles akin to two coupled individuals. We present the derivation of a dynamic model of the human hand for the purpose of controller development for new hand exoskeleton platforms...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28814023/representing-high-dimensional-data-to-intelligent-prostheses-and-other-wearable-assistive-robots-a-first-comparison-of-tile-coding-and-selective-kanerva-coding
#3
Jaden B Travnik, Patrick M Pilarski
Prosthetic devices have advanced in their capabilities and in the number and type of sensors included in their design. As the space of sensorimotor data available to a conventional or machine learning prosthetic control system increases in dimensionality and complexity, it becomes increasingly important that this data be represented in a useful and computationally efficient way. Well structured sensory data allows prosthetic control systems to make informed, appropriate control decisions. In this study, we explore the impact that increased sensorimotor information has on current machine learning prosthetic control approaches...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813990/a-learning-based-agent-for-home-neurorehabilitation
#4
Andreas Lydakis, Yuanliang Meng, Christopher Munroe, Yi-Ning Wu, Momotaz Begum
This paper presents the iterative development of an artificially intelligent system to promote home-based neurorehabilitation. Although proper, structured practice of rehabilitation exercises at home is the key to successful recovery of motor functions, there is no home-program out there which can monitor a patient's exercise-related activities and provide corrective feedback in real time. To this end, we designed a Learning from Demonstration (LfD) based home-rehabilitation framework that combines advanced robot learning algorithms with commercially available wearable technologies...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813969/prediction-of-user-preference-over-shared-control-paradigms-for-a-robotic-wheelchair
#5
Ahmetcan Erdogan, Brenna D Argall
The design of intelligent powered wheelchairs has traditionally focused heavily on providing effective and efficient navigation assistance. Significantly less attention has been given to the end-user's preference between different assistance paradigms. It is possible to include these subjective evaluations in the design process, for example by soliciting feedback in post-experiment questionnaires. However, constantly querying the user for feedback during real-world operation is not practical. In this paper, we present a model that correlates objective performance metrics and subjective evaluations of autonomous wheelchair control paradigms...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813938/online-sparse-gaussian-process-based-human-motion-intent-learning-for-an-electrically-actuated-lower-extremity-exoskeleton
#6
Yi Long, Zhi-Jiang Du, Chao-Feng Chen, Wei Dong, Wei-Dong Wang
The most important step for lower extremity exoskeleton is to infer human motion intent (HMI), which contributes to achieve human exoskeleton collaboration. Since the user is in the control loop, the relationship between human robot interaction (HRI) information and HMI is nonlinear and complicated, which is difficult to be modeled by using mathematical approaches. The nonlinear approximation can be learned by using machine learning approaches. Gaussian Process (GP) regression is suitable for high-dimensional and small-sample nonlinear regression problems...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813903/let-s-do-this-together-bi-manu-interact-a-novel-device-for-studying-human-haptic-interactive-behavior
#7
Ekaterina Ivanova, Axel Krause, Marie Schalicke, Franziska Schellhardt, Natalie Jankowski, Josy Achner, Henning Schmidt, Michael Joebges, Jorg Kruger
Our area of interest is robotic-based rehabilitation after stroke, and our goal is to help patients achieve optimal motor learning during high-intensity repetitive movement training through the assistance of robots. It is important, that the robotic assistance is adapted to the patients' abilities, thereby ensuring that the device is only supporting the patient as necessary ("assist-as-needed"). We hypothesize that natural and learning-effective human-machine interaction can be achieved by programming the robot's control, so that it emulates how a physiotherapist adaptively supports the patients' limb movement during stroke rehabilitation...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813854/automated-stand-up-and-sit-down-detection-for-robot-assisted-body-weight-support-training-with-the-float
#8
Mathias Bannwart, Dominique Emst, Chris Easthope, Marc Bolliger, Georg Rauter
Patients with impaired walking function are often dependent on assistive devices to retrain gait and regain independence in life. To provide adequate support, gait rehabilitation devices have to be manually set to the correct support mode or have to recognize the type and starting point of a certain motion automatically. For automated motion type detection, machine learning-based classification algorithms using sensor signals from different body parts can achieve robust performance. However, until today, there is only little work available to detect motion onset...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813804/leap-motion-evaluation-for-assessment-of-upper-limb-motor-skills-in-parkinson-s-disease
#9
A H Butt, E Rovini, C Dolciotti, P Bongioanni, G De Petris, F Cavallo
The main goal of this study is to investigate the potential of the Leap Motion Controller (LMC) for the objective assessment of motor dysfunctioning in patients with Parkinson's disease (PwPD). The most relevant clinical signs in Parkinson's Disease (PD), such as slowness of movements, frequency variation, amplitude variation, and speed, were extracted from the recorded LMC data. Data were clinically quantified using the LMC software development kit (SDK). In this study, 16 PwPD subjects and 12 control healthy subjects were involved...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813689/large-scale-machine-learning-of-media-outlets-for-understanding-public-reactions-to-nation-wide-viral-infection-outbreaks
#10
Sungwoon Choi, Jangho Lee, Min-Gyu Kang, Hyeyoung Min, Yoon-Seok Chang, Sungroh Yoon
From May to July 2015, there was a nation-wide outbreak of Middle East respiratory syndrome (MERS) in Korea. MERS is caused by MERS-CoV, an enveloped, positive-sense, single stranded RNA virus belonging to the family Coronaviridae. Despite expert opinions that the danger of MERS might be exaggerated, there was an overreaction by the public according to the Korean mass media, which led to a noticeable reduction in social and economic activities during the outbreak. To explain this phenomenon, we presumed that machine learning-based analysis of media outlets would be helpful and collected a number of Korean mass media articles and short-text comments produced during the 10-week outbreak...
August 13, 2017: Methods: a Companion to Methods in Enzymology
https://www.readbyqxmd.com/read/28813537/metabolite-patterns-predicting-sex-and-age-in-participants-of-the-karlsruhe-metabolomics-and-nutrition-karmen-study
#11
Manuela J Rist, Alexander Roth, Lara Frommherz, Christoph H Weinert, Ralf Krüger, Benedikt Merz, Diana Bunzel, Carina Mack, Björn Egert, Achim Bub, Benjamin Görling, Pavleta Tzvetkova, Burkhard Luy, Ingrid Hoffmann, Sabine E Kulling, Bernhard Watzl
Physiological and functional parameters, such as body composition, or physical fitness are known to differ between men and women and to change with age. The goal of this study was to investigate how sex and age-related physiological conditions are reflected in the metabolome of healthy humans and whether sex and age can be predicted based on the plasma and urine metabolite profiles. In the cross-sectional KarMeN (Karlsruhe Metabolomics and Nutrition) study 301 healthy men and women aged 18-80 years were recruited...
2017: PloS One
https://www.readbyqxmd.com/read/28813454/applying-machine-learning-to-identify-autistic-adults-using-imitation-an-exploratory-study
#12
Baihua Li, Arjun Sharma, James Meng, Senthil Purushwalkam, Emma Gowen
Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repetitive motor movements are considered during diagnosis, quantitative measures that identify kinematic characteristics in the movement patterns of autistic individuals are poorly studied, preventing advances in understanding the aetiology of motor impairment, or whether a wider range of motor characteristics could be used for diagnosis. The aim of this study was to investigate whether data-driven machine learning based methods could be used to address some fundamental problems with regard to identifying discriminative test conditions and kinematic parameters to classify between ASC and neurotypical controls...
2017: PloS One
https://www.readbyqxmd.com/read/28813438/heritability-estimation-using-a-regularized-regression-approach-herra-applicable-to-continuous-dichotomous-or-age-at-onset-outcome
#13
Malka Gorfine, Sonja I Berndt, Jenny Chang-Claude, Michael Hoffmeister, Loic Le Marchand, John Potter, Martha L Slattery, Nir Keret, Ulrike Peters, Li Hsu
The popular Genome-wide Complex Trait Analysis (GCTA) software uses the random-effects models for estimating the narrow-sense heritability based on GWAS data of unrelated individuals without knowing and identifying the causal loci. Many methods have since extended this approach to various situations. However, since the proportion of causal loci among the variants is typically very small and GCTA uses all variants to calculate the similarities among individuals, the estimation of heritability may be unstable, resulting in a large variance of the estimates...
2017: PloS One
https://www.readbyqxmd.com/read/28812881/predicting-microbial-fuel-cell-biofilm-communities-and-bioreactor-performance-using-artificial-neural-networks
#14
Keaton Larson Lesnik, Hong Liu
The complex interactions that occur in mixed-species bioelectrochemical reactors, like microbial fuel cells (MFCs), make accurate predictions of performance outcomes under untested conditions difficult. While direct correlations between any individual waste stream characteristic or microbial community structure and reactor performance have not been able to be directly established, the increase in sequencing data and readily available computational power enables the development of alternate approaches. In the current study, 33 MFCs were evaluated under a range of conditions including 8 separate substrates and 3 different wastewaters...
August 16, 2017: Environmental Science & Technology
https://www.readbyqxmd.com/read/28812294/teaching-to-see-behaviors-using-machine-learning
#15
EDITORIAL
Alan K Louie, Richard Balon, Eugene V Beresin, John H Coverdale, Adam M Brenner, Anthony P S Guerrero, Laura Weiss Roberts
No abstract text is available yet for this article.
August 15, 2017: Academic Psychiatry
https://www.readbyqxmd.com/read/28812204/multivariable-adaptive-artificial-pancreas-system-in-type-1-diabetes
#16
REVIEW
Ali Cinar
PURPOSE OF REVIEW: The review summarizes the current state of the artificial pancreas (AP) systems and introduces various new modules that should be included in future AP systems. RECENT FINDINGS: A fully automated AP must be able to detect and mitigate the effects of meals, exercise, stress and sleep on blood glucose concentrations. This can only be achieved by using a multivariable approach that leverages information from wearable devices that provide real-time streaming data about various physiological variables that indicate imminent changes in blood glucose concentrations caused by meals, exercise, stress and sleep...
August 15, 2017: Current Diabetes Reports
https://www.readbyqxmd.com/read/28812013/intelligent-techniques-using-molecular-data-analysis-in-leukaemia-an-opportunity-for-personalized-medicine-support-system
#17
REVIEW
Haneen Banjar, David Adelson, Fred Brown, Naeem Chaudhri
The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28811508/discrimination-of-plant-root-zone-water-status-in-greenhouse-production-based-on-phenotyping-and-machine-learning-techniques
#18
Doudou Guo, Jiaxiang Juan, Liying Chang, Jingjin Zhang, Danfeng Huang
Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all...
August 15, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28810905/omni-polya-a-method-and-tool-for-accurate-recognition-of-poly-a-signals-in-human-genomic-dna
#19
Arturo Magana-Mora, Manal Kalkatawi, Vladimir B Bajic
BACKGROUND: Polyadenylation is a critical stage of RNA processing during the formation of mature mRNA, and is present in most of the known eukaryote protein-coding transcripts and many long non-coding RNAs. The correct identification of poly(A) signals (PAS) not only helps to elucidate the 3'-end genomic boundaries of a transcribed DNA region and gene regulatory mechanisms but also gives insight into the multiple transcript isoforms resulting from alternative PAS. Although progress has been made in the in-silico prediction of genomic signals, the recognition of PAS in DNA genomic sequences remains a challenge...
August 15, 2017: BMC Genomics
https://www.readbyqxmd.com/read/28809811/mutation-clusters-from-cancer-exome
#20
Zura Kakushadze, Willie Yu
We apply our statistically deterministic machine learning/clustering algorithm *K-means (recently developed in https://ssrn.com/abstract=2908286) to 10,656 published exome samples for 32 cancer types. A majority of cancer types exhibit a mutation clustering structure. Our results are in-sample stable. They are also out-of-sample stable when applied to 1389 published genome samples across 14 cancer types. In contrast, we find in- and out-of-sample instabilities in cancer signatures extracted from exome samples via nonnegative matrix factorization (NMF), a computationally-costly and non-deterministic method...
August 15, 2017: Genes
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