Pub Date : 2017-01-01Epub Date: 2017-04-24DOI: 10.4172/2573-0312.1000137
Masil George, Gohar Azhar, Amanda Pangle, Eric Peeler, Amanda Dawson, Robert Coker, Kellie S Coleman, Amy Schrader, Jeanne Wei
Objective: Cardiac cachexia is a condition associated with heart failure, particularly in the elderly, and is characterized by loss of muscle mass with or without the loss of fat mass. Approximately 15% of elderly heart failure patients will eventually develop cardiac cachexia; such a diagnosis is closely associated with high morbidity and increased mortality. While the mechanism(s) involved in the progression of cardiac cachexia is incompletely established, certain factors appear to be contributory. Dietary deficiencies, impaired bowel perfusion, and metabolic dysfunction all contribute to reduced muscle mass, increased muscle wasting, increased protein degradation, and reduced protein synthesis. Thus slowing or preventing the progression of cardiac cachexia relies heavily on dietary and exercise-based interventions in addition to standard heart failure treatments and medications.
Methods: The aim of the present study was to test the feasibility of an at-home exercise and nutrition intervention program in a population of elderly with heart failure, in an effort to determine whether dietary protein supplementation and increased physical activity may slow the progression, or prevent the onset, of cardiac cachexia. Frail elderly patients over the age of 55 with symptoms of heart failure from UAMS were enrolled in one of two groups, intervention or control. To assess the effect of protein supplementation and exercise on the development of cardiac cachexia, data on various measures of muscle quality, cardiovascular health, mental status, and quality of life were collected and analyzed from the two groups at the beginning and end of the study period.
Results: More than 50% of those who were initially enrolled actually completed the 6-month study. While both groups showed some improvement in their study measures, the protein and exercise group showed a greater tendency to improve than the control group by the end of the six months.
Conclusion: These findings suggest that with a larger cohort, this intervention may show significant positive effects for elderly patients who are at risk of developing cardiac cachexia.
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Pub Date : 2016-10-11DOI: 10.4172/2573-0312.1000118
Aleksandar Vakanski, J. Ferguson, S. Lee
OBJECTIVE The objective of the proposed research is to develop a methodology for modeling and evaluation of human motions, which will potentially benefit patients undertaking a physical rehabilitation therapy (e.g., following a stroke or due to other medical conditions). The ultimate aim is to allow patients to perform home-based rehabilitation exercises using a sensory system for capturing the motions, where an algorithm will retrieve the trajectories of a patient's exercises, will perform data analysis by comparing the performed motions to a reference model of prescribed motions, and will send the analysis results to the patient's physician with recommendations for improvement. METHODS The modeling approach employs an artificial neural network, consisting of layers of recurrent neuron units and layers of neuron units for estimating a mixture density function over the spatio-temporal dependencies within the human motion sequences. Input data are sequences of motions related to a prescribed exercise by a physiotherapist to a patient, and recorded with a motion capture system. An autoencoder subnet is employed for reducing the dimensionality of captured sequences of human motions, complemented with a mixture density subnet for probabilistic modeling of the motion data using a mixture of Gaussian distributions. RESULTS The proposed neural network architecture produced a model for sets of human motions represented with a mixture of Gaussian density functions. The mean log-likelihood of observed sequences was employed as a performance metric in evaluating the consistency of a subject's performance relative to the reference dataset of motions. A publically available dataset of human motions captured with Microsoft Kinect was used for validation of the proposed method. CONCLUSION The article presents a novel approach for modeling and evaluation of human motions with a potential application in home-based physical therapy and rehabilitation. The described approach employs the recent progress in the field of machine learning and neural networks in developing a parametric model of human motions, by exploiting the representational power of these algorithms to encode nonlinear input-output dependencies over long temporal horizons.
{"title":"Mathematical Modeling and Evaluation of Human Motions in Physical Therapy Using Mixture Density Neural Networks.","authors":"Aleksandar Vakanski, J. Ferguson, S. Lee","doi":"10.4172/2573-0312.1000118","DOIUrl":"https://doi.org/10.4172/2573-0312.1000118","url":null,"abstract":"OBJECTIVE\u0000The objective of the proposed research is to develop a methodology for modeling and evaluation of human motions, which will potentially benefit patients undertaking a physical rehabilitation therapy (e.g., following a stroke or due to other medical conditions). The ultimate aim is to allow patients to perform home-based rehabilitation exercises using a sensory system for capturing the motions, where an algorithm will retrieve the trajectories of a patient's exercises, will perform data analysis by comparing the performed motions to a reference model of prescribed motions, and will send the analysis results to the patient's physician with recommendations for improvement.\u0000\u0000\u0000METHODS\u0000The modeling approach employs an artificial neural network, consisting of layers of recurrent neuron units and layers of neuron units for estimating a mixture density function over the spatio-temporal dependencies within the human motion sequences. Input data are sequences of motions related to a prescribed exercise by a physiotherapist to a patient, and recorded with a motion capture system. An autoencoder subnet is employed for reducing the dimensionality of captured sequences of human motions, complemented with a mixture density subnet for probabilistic modeling of the motion data using a mixture of Gaussian distributions.\u0000\u0000\u0000RESULTS\u0000The proposed neural network architecture produced a model for sets of human motions represented with a mixture of Gaussian density functions. The mean log-likelihood of observed sequences was employed as a performance metric in evaluating the consistency of a subject's performance relative to the reference dataset of motions. A publically available dataset of human motions captured with Microsoft Kinect was used for validation of the proposed method.\u0000\u0000\u0000CONCLUSION\u0000The article presents a novel approach for modeling and evaluation of human motions with a potential application in home-based physical therapy and rehabilitation. The described approach employs the recent progress in the field of machine learning and neural networks in developing a parametric model of human motions, by exploiting the representational power of these algorithms to encode nonlinear input-output dependencies over long temporal horizons.","PeriodicalId":91760,"journal":{"name":"Journal of physiotherapy & physical rehabilitation","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82235827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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