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longitudinal evaluation of Hemiplegic Ankle Rehabilitation Efficacy by Wearable Inertial Sensor Systems with an Assortment of Machine Learning Algorithms

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Journal of Biomedical Science and Engineering 2023年第9期16卷 121-131页

作者： Robert LeMoyne Timothy Mastroianni Department of Biological Sciences Northern Arizona University Flagstaff USA Department of Biomedical Technology Czech Technical University in Prague Kladno Czech Republic Cognition Engineering Pittsburgh USA

With the amalgamation of wearable systems equipped with inertial sensors, such as a gyroscope, and machine learning a therapy regimen can be objectively quantified, and then the initial phase and final phase of a one year therapy regimen can be distinguished through machine learning. In the context of rehabilitation of a hemiplegic ankle, a longitudinal therapy regimen incorporating stretching and then a series of repetitions for raising and lowering the foot of the hemiplegic ankle can be applied over the course of a year. Using a smartphone equipped with an application to function as a wearable and wireless gyroscope platform mounted to the dorsum of the foot by an armband, the initial phase and final phase of a one year longitudinally applied therapy regimen can be objectively quantified and recorded for subsequent machine learning. Considerable classification accuracy is attained to distinguish between the initial phase and final phase by a support vector machine for a one year longitudinally applied hemiplegic ankle therapy regimen based on the gyroscope signal data obtained by a smartphone functioning as a wearable and wireless inertial sensor system. .

关键词： Smartphone Gyroscope Machine Learning Hemiparesis Rehabilitation Ankle longitudinal evaluation

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Clinical longitudinal evaluation of COVID-19 patients and prediction of organ-specific recovery using artificial intelligence

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Precision Clinical Medicine 2021年第1期4卷 62-69页

作者： Winston T.Wang Charlotte L.Zhang Kang Wei Ye Sang Jun Shen Guangyu Wang Alexander X.Lozano Department of Materials Science&Engineering Stanford UniversityStanfordCA 94305USA Mayo Clinic Alix school of Medicine ScottsdaleAZ 85259USA Guangzhou Women and Children’s Medical Center Guangzhou Medical UniversityGuangzhou 510623China The First College of Clinical Medical Science China Three Gorges UniversityYichang 443000China Department of Radiology Sun Yat-Sen Memorial HospitalSun Yat-Sen UniversityGuangzhou 510120China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

Within COVID-19 there is an urgent unmet need to predict at the time of hospital admission which COVID-19 patients will recover from the disease,and how fast they recover to deliver personalized treatments and to properly allocate hospital resources so that healthcare systems do not become *** this end,we have combined clinically salient CT imaging data synergistically with laboratory testing data in an integrative machine learning model to predict organ-specific recovery of patients from *** trained and validated our model in 285 patients on each separate major organ system impacted by COVID-19 including the renal,pulmonary,immune,cardiac,and hepatic *** greatly enhance the speed and utility of our model,we applied an artificial intelligence method to segment and classify regions on CT imaging,from which interpretable data could be directly fed into the predictive machine learning model for overall *** all organ systems we achieved validation set area under the receiver operator characteristic curve(AUC)values for organ-specific recovery ranging from 0.80 to 0.89,and significant overall recovery prediction in Kaplan-Meier *** demonstrates that the synergistic use of an artificial intelligence(AI)framework applied to CT lung imaging and a machine learning model that integrates laboratory test data with imaging data can accurately predict the overall recovery of COVID-19 patients from baseline characteristics.

关键词： artificial intelligence medical image COVID-19 longitudinal evaluation

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longitudinal evaluation Nocturnal Sleep Behaviors of Healthy and Well Developed Children Born Prematurely

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Open Journal of Pediatrics 2016年第1期6卷 48-52页

作者： Yoko Asaka Satoshi Takada Faculty of Health Sciences Hokkaido University Sapporo Japan Kobe University Graduate School of Health Sciences Kobe Japan

The aim of the study is to examine nocturnal sleep measures of healthy and well developed pre-term infants by birth weight when they were averaged 15 corrected months and became elementary school children. Sleep measures were collected by actigraphs (Micro-mini RC, Ambulatory Monitoring Inc., New York, USA). Nocturnal sleep quality of children born as extremely low birth weight (ELBW) was significantly inferior with that of children born as very low birth weight (VLBW) at 15 corrected months [number of night waking: 6.0 (4.5 - 8.0), ELBW 8.5 (7.0 - 10.3) (Z = 2.47, p < 0.01), wake after sleep onset: VLBW 99.0 (73.0 - 115.0) min, ELBW 146.5 (94.8 - 171.3) min (Z = 2.89, p < 0.01)]. However, these measures did not show any significant differences by birth weight at second data collection period [number of night waking: VLBW 0.0 (0.0-1.0), ELBW 1.0 (0.0 - 2.5) (Z = —0.62, n.s.), wake after sleep onset: VLBW 16.0 (8.0 - 27.0) min, ELBW 15.0 (6.0 - 32.5) min (Z = 0.00, n.s.)]. It was assumed that nocturnal measures were affected by the birth weight at the age of 15 corrected months in ELBW infants, but could not predict the future sleep problems at this point.

关键词： Nocturnal Sleep longitudinal evaluation Extremely Low Birth Weight Infant Actigraph

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