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Machine learning in physical activity

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https://www.readbyqxmd.com/read/28419025/ensemble-methods-for-classification-of-physical-activities-from-wrist-accelerometry
#1
Alok Kumar Chowdhury, Dian Tjondronegoro, Vinod Chandran, Stewart G Trost
PURPOSE: To investigate whether the use of ensemble learning algorithms improve physical activity recognition accuracy compared to the single classifier algorithms, and to compare the classification accuracy achieved by three conventional ensemble machine learning methods (bagging, boosting, random forest) and a custom ensemble model comprising four algorithms commonly used for activity recognition (binary decision tree, k nearest neighbour, support vector machine, and neural network)...
April 18, 2017: Medicine and Science in Sports and Exercise
https://www.readbyqxmd.com/read/28378815/prediction-of-oxygen-uptake-dynamics-by-machine-learning-analysis-of-wearable-sensors-during-activities-of-daily-living
#2
T Beltrame, R Amelard, A Wong, R L Hughson
Currently, oxygen uptake () is the most precise means of investigating aerobic fitness and level of physical activity; however, can only be directly measured in supervised conditions. With the advancement of new wearable sensor technologies and data processing approaches, it is possible to accurately infer work rate and predict during activities of daily living (ADL). The main objective of this study was to develop and verify the methods required to predict and investigate the dynamics during ADL. The variables derived from the wearable sensors were used to create a predictor based on a random forest method...
April 5, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28269818/a-two-dimensional-matrix-image-based-feature-extraction-method-for-classification-of-semg-a-comparative-analysis-based-on-svm-knn-and-rbf-nn
#3
Tingxi Wen, Zhongnan Zhang, Ming Qiu, Ming Zeng, Weizhen Luo
BACKGROUND: The computer mouse is an important human-computer interaction device. But patients with physical finger disability are unable to operate this device. Surface EMG (sEMG) can be monitored by electrodes on the skin surface and is a reflection of the neuromuscular activities. Therefore, we can control limbs auxiliary equipment by utilizing sEMG classification in order to help the physically disabled patients to operate the mouse. OBJECTIVE: To develop a new a method to extract sEMG generated by finger motion and apply novel features to classify sEMG...
2017: Journal of X-ray Science and Technology
https://www.readbyqxmd.com/read/28269690/smartsock-a-wearable-platform-for-context-aware-assessment-of-ankle-edema
#4
Ramin Fallahzadeh, Mahdi Pedram, Hassan Ghasemzadeh
Ankle edema an important symptom for monitoring patients with chronic systematic diseases. It is an important indicator of onset or exacerbation of a variety of diseases that disturb cardiovascular, renal, or hepatic system such as heart, liver, and kidney failure, diabetes, etc. The current approaches toward edema assessment are conducted during clinical visits. In-clinic assessments, in addition to being burdensome and expensive, are sometimes not reliable and neglect important contextual factors such as patient's physical activity level and body posture...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269004/activity-recognition-in-patients-with-lower-limb-impairments-do-we-need-training-data-from-each-patient
#5
Luca Lonini, Aakash Gupta, Konrad Kording, Arun Jayaraman
Machine learning allows detecting specific physical activities using data from wearable sensors. Such a quantification of patient mobility over time promises to accurately inform clinical decisions for physical rehabilitation. There are two strategies of setting up the machine learning problem: detect one patient's activities using data from the same patient (personal model) or detect their activities using data from other patients (global model), and we currently do not know if personal models are necessary...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268757/transferring-knowledge-during-dyadic-interaction-the-role-of-the-expert-in-the-learning-process
#6
Edwin Johnatan Avila Mireles, Dalia De Santis, Pietro Morasso, Jacopo Zenzeri
Physical interaction between man and machines is increasing the interest of the research as well as the industrial community. It is known that physical coupling between active persons can be beneficial and increase the performance of the dyad compared to an individual. However, the factors that may result in performance benefits are still poorly understood. The aim of this work is to investigate how the different initial skill levels of the interacting partners influence the learning of a stabilization task...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227951/smartsock-a-wearable-platform-for-context-aware-assessment-of-ankle-edema
#7
Ramin Fallahzadeh, Mahdi Pedram, Hassan Ghasemzadeh, Ramin Fallahzadeh, Mahdi Pedram, Hassan Ghasemzadeh
Ankle edema an important symptom for monitoring patients with chronic systematic diseases. It is an important indicator of onset or exacerbation of a variety of diseases that disturb cardiovascular, renal, or hepatic system such as heart, liver, and kidney failure, diabetes, etc. The current approaches toward edema assessment are conducted during clinical visits. In-clinic assessments, in addition to being burdensome and expensive, are sometimes not reliable and neglect important contextual factors such as patient's physical activity level and body posture...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227214/activity-recognition-in-patients-with-lower-limb-impairments-do-we-need-training-data-from-each-patient
#8
Luca Lonini, Aakash Gupta, Konrad Kording, Arun Jayaraman, Luca Lonini, Aakash Gupta, Konrad Kording, Arun Jayaraman, Luca Lonini, Konrad Kording, Arun Jayaraman, Aakash Gupta
Machine learning allows detecting specific physical activities using data from wearable sensors. Such a quantification of patient mobility over time promises to accurately inform clinical decisions for physical rehabilitation. There are two strategies of setting up the machine learning problem: detect one patient's activities using data from the same patient (personal model) or detect their activities using data from other patients (global model), and we currently do not know if personal models are necessary...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226945/transferring-knowledge-during-dyadic-interaction-the-role-of-the-expert-in-the-learning-process
#9
Edwin Johnatan Avila Mireles, Dalia De Santis, Pietro Morasso, Jacopo Zenzeri, Edwin Johnatan Avila Mireles, Dalia De Santis, Pietro Morasso, Jacopo Zenzeri, Jacopo Zenzeri, Pietro Morasso, Edwin Johnatan Avila Mireles, Dalia De Santis
Physical interaction between man and machines is increasing the interest of the research as well as the industrial community. It is known that physical coupling between active persons can be beneficial and increase the performance of the dyad compared to an individual. However, the factors that may result in performance benefits are still poorly understood. The aim of this work is to investigate how the different initial skill levels of the interacting partners influence the learning of a stabilization task...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28222058/classifiers-for-accelerometer-measured-behaviors-in-older-women
#10
Dori Rosenberg, Suneeta Godbole, Katherine Ellis, Chongzhi DI, Andrea Lacroix, Loki Natarajan, Jacqueline Kerr
PURPOSE: Machine learning methods could better improve the detection of specific types of physical activities and sedentary behaviors from accelerometer data. No studies in older populations have developed and tested algorithms for walking and sedentary time in free-living daily life. Our goal was to rectify this gap by leveraging access to data from two studies in older women. METHODS: In study 1, algorithms were developed and tested in a sample of older women (N = 39, age range = 55-96 yr) in the field...
March 2017: Medicine and Science in Sports and Exercise
https://www.readbyqxmd.com/read/28126242/artificial-intelligence-in-medicine
#11
Pavel Hamet, Johanne Tremblay
Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation...
April 2017: Metabolism: Clinical and Experimental
https://www.readbyqxmd.com/read/28042838/a-comparison-study-of-classifier-algorithms-for-cross-person-physical-activity-recognition
#12
Yago Saez, Alejandro Baldominos, Pedro Isasi
Physical activity is widely known to be one of the key elements of a healthy life. The many benefits of physical activity described in the medical literature include weight loss and reductions in the risk factors for chronic diseases. With the recent advances in wearable devices, such as smartwatches or physical activity wristbands, motion tracking sensors are becoming pervasive, which has led to an impressive growth in the amount of physical activity data available and an increasing interest in recognizing which specific activity a user is performing...
December 30, 2016: Sensors
https://www.readbyqxmd.com/read/27973671/feasibility-of-obtaining-measures-of-lifestyle-from-a-smartphone-app-the-myheart-counts-cardiovascular-health-study
#13
Michael V McConnell, Anna Shcherbina, Aleksandra Pavlovic, Julian R Homburger, Rachel L Goldfeder, Daryl Waggot, Mildred K Cho, Mary E Rosenberger, William L Haskell, Jonathan Myers, Mary Ann Champagne, Emmanuel Mignot, Martin Landray, Lionel Tarassenko, Robert A Harrington, Alan C Yeung, Euan A Ashley
Importance: Studies have established the importance of physical activity and fitness, yet limited data exist on the associations between objective, real-world physical activity patterns, fitness, sleep, and cardiovascular health. Objectives: To assess the feasibility of obtaining measures of physical activity, fitness, and sleep from smartphones and to gain insights into activity patterns associated with life satisfaction and self-reported disease. Design, Setting, and Participants: The MyHeart Counts smartphone app was made available in March 2015, and prospective participants downloaded the free app between March and October 2015...
January 1, 2017: JAMA Cardiology
https://www.readbyqxmd.com/read/27932294/large-scale-structure-based-prediction-and-identification-of-novel-protease-substrates-using-computational-protein-design
#14
Manasi A Pethe, Aliza B Rubenstein, Sagar D Khare
Characterizing the substrate specificity of protease enzymes is critical for illuminating the molecular basis of their diverse and complex roles in a wide array of biological processes. Rapid and accurate prediction of their extended substrate specificity would also aid in the design of custom proteases capable of selectively and controllably cleaving biotechnologically or therapeutically relevant targets. However, current in silico approaches for protease specificity prediction, rely on, and are therefore limited by, machine learning of sequence patterns in known experimental data...
December 6, 2016: Journal of Molecular Biology
https://www.readbyqxmd.com/read/27870246/materials-informatics-statistical-modeling-in-material-science
#15
REVIEW
Abraham Yosipof, Klimentiy Shimanovich, Hanoch Senderowitz
Material informatics is engaged with the application of informatic principles to materials science in order to assist in the discovery and development of new materials. Central to the field is the application of data mining techniques and in particular machine learning approaches, often referred to as Quantitative Structure Activity Relationship (QSAR) modeling, to derive predictive models for a variety of materials-related "activities". Such models can accelerate the development of new materials with favorable properties and provide insight into the factors governing these properties...
December 2016: Molecular Informatics
https://www.readbyqxmd.com/read/27755355/comparison-of-accelerometry-methods-for-estimating-physical-activity
#16
Jacqueline Kerr, Catherine R Marinac, Katherine Ellis, Suneeta Godbole, Aaron Hipp, Karen Glanz, Jonathan Mitchell, Francine Laden, Peter James, David Berrigan
PURPOSE: This study aimed to compare physical activity estimates across different accelerometer wear locations, wear time protocols, and data processing techniques. METHODS: A convenience sample of middle-age to older women wore a GT3X+ accelerometer at the wrist and hip for 7 d. Physical activity estimates were calculated using three data processing techniques: single-axis cut points, raw vector magnitude thresholds, and machine learning algorithms applied to the raw data from the three axes...
March 2017: Medicine and Science in Sports and Exercise
https://www.readbyqxmd.com/read/27752272/predicting-metabolic-syndrome-using-decision-tree-and-support-vector-machine-methods
#17
Farzaneh Karimi-Alavijeh, Saeed Jalili, Masoumeh Sadeghi
BACKGROUND: Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome...
May 2016: ARYA Atherosclerosis
https://www.readbyqxmd.com/read/27751984/building-a-national-neighborhood-dataset-from-geotagged-twitter-data-for-indicators-of-happiness-diet-and-physical-activity
#18
Quynh C Nguyen, Dapeng Li, Hsien-Wen Meng, Suraj Kath, Elaine Nsoesie, Feifei Li, Ming Wen
BACKGROUND: Studies suggest that where people live, play, and work can influence health and well-being. However, the dearth of neighborhood data, especially data that is timely and consistent across geographies, hinders understanding of the effects of neighborhoods on health. Social media data represents a possible new data resource for neighborhood research. OBJECTIVE: The aim of this study was to build, from geotagged Twitter data, a national neighborhood database with area-level indicators of well-being and health behaviors...
October 17, 2016: JMIR Public Health and Surveillance
https://www.readbyqxmd.com/read/27746849/one-class-classification-based-real-time-activity-error-detection-in-smart-homes
#19
Barnan Das, Diane J Cook, Narayanan C Krishnan, Maureen Schmitter-Edgecombe
Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions...
August 2016: IEEE Journal of Selected Topics in Signal Processing
https://www.readbyqxmd.com/read/27733922/predicting-adherence-of-patients-with-hf-through-machine-learning-techniques
#20
Georgia Spiridon Karanasiou, Evanthia Eleftherios Tripoliti, Theofilos Grigorios Papadopoulos, Fanis Georgios Kalatzis, Yorgos Goletsis, Katerina Kyriakos Naka, Aris Bechlioulis, Abdelhamid Errachid, Dimitrios Ioannis Fotiadis
Heart failure (HF) is a chronic disease characterised by poor quality of life, recurrent hospitalisation and high mortality. Adherence of patient to treatment suggested by the experts has been proven a significant deterrent of the above-mentioned serious consequences. However, the non-adherence rates are significantly high; a fact that highlights the importance of predicting the adherence of the patient and enabling experts to adjust accordingly patient monitoring and management. The aim of this work is to predict the adherence of patients with HF, through the application of machine learning techniques...
September 2016: Healthcare Technology Letters
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