13 research outputs found
Man-machine interface prptotype for realtime prediction of motion of exoskeleton's users
No full textLAUREA MAGISTRALEIn the United States, over 282.000 people today are affected by Spinal Cord Injury, which is a traumatic damage to the spinal cord that leads to a partial or total body paralysis. At the UC Berkeley Human Robotics & Engineering Laboratory has been developed the Phoenix Exoskeleton which aims to reduce the impediments due to this physical damage by replacing the wheelchair in order to give to the patients the opportunity of changing their quality life, by improving overall health and mobility. This work introduces an innovative approach for replacing the current user interface, allowing to the user to have an interaction with the device totally free of active inputs, while walking, through a safe system which predicts in real time the users intentions. The software prototype is designed for providing an MMI (Man-Machine Interface) able to reduce the time gap and the balance variation between the moment when the intention of walking presents itself and the trigger of the swing really happens, according to the empiric hypothesis formulated by the author which assumes that exoskeletons users have involuntary body movements which differentiate their motion desires. The system has been developed using a machine learning model fed with post- processed data collected by two encoders and one IMU, which describe the users gait. It has been called Body Motion Triggering.
The efficiency of the prototype has been tested in laboratory by the pilot: the system turned out to be totally safe; the predicting outcomes still need to be widely improved but the achieved results are encouraging and have demonstrated the validity of the formulated hypothesis, opening a new potential field of research.
The hope behind the analysis carried out is to have been useful in order to improve the lives of people that need it
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A Novel Approach to Medical Exoskeleton Stabilization
No full textIn the year 2016, it is estimated that 282,000 people were living with a spinal cord injury (SCI) in the United States. Furthermore, it is estimated that 17,000 new SCI cases arise each year in the United States. As a result of their injuries, those affected by SCIs are commonly subjected to a plethora of physical, psychological, and financial hardships. These hardships can be so severe that, on average, individuals affected by SCIs experience significantly lower life expectancies than those without SCIs. In an attempt to improve the quality and span of life of individuals with SCIs, there has been an increased interest in pursuing technologies such as the exoskeleton - a wearable robotic device which provides support and bipedal locomotion for individuals with mobility disorders. Despite the promise of exoskeletons, there are still clear technical inadequacies that exist in the field itself. One of the most glaring inadequacies pertains to user safety and the prevention of falls while wearing an exoskeleton. To maintain stability, an exoskeleton user will almost always walk with the assistance of crutches. While the use of crutches clearly provides some degree of stability, crutch usage inherently places substantial responsibility on the exoskeleton user to manipulate said crutches correctly. With one small misplacement of the crutches as the prerequisite for an adverse event, even the most experienced exoskeleton users run a high risk of falling at all times. Thus, in the opinion of the author, crutches are not an acceptable solution for stable exoskeleton locomotion.In this thesis, the primary objective is to provide locomotion in an exoskeleton with a reduced chance of falling (compared to locomotion in an exoskeleton with crutches). To satisfy said objective, an exoskeleton support mechanism (ESM) is proposed by the author. This thesis provides a thorough explanation of the background theory, system architecture, and design analysis that went into making the ESM a reality. Additionally, through a combination of experimentation and anecdotal findings (described within), use of the ESM in combination with a walker is shown to provide locomotion in an exoskeleton with a reduced chance of falling - thereby satisfying the primary objective. The exoskeleton support mechanism presented in this thesis clearly advances the state of the art in medical exoskeleton stabilization while also acting as a critical puzzle piece in providing a better quality of life for those affected by spinal cord injuries
BMC Med Inform Decis Mak
No full textBackgroundSurveillance of univariate syndromic data as a means of potential indicator of developing public health conditions has been used extensively. This paper aims to improve the performance of detecting outbreaks by using a background forecasting algorithm based on the adaptive recursive least squares method combined with a novel treatment of the Day of the Week effect.MethodsPrevious work by the first author has suggested that univariate recursive least squares analysis of syndromic data can be used to characterize the background upon which a prediction and detection component of a biosurvellance system may be built. An adaptive implementation is used to deal with data non-stationarity. In this paper we develop and implement the RLS method for background estimation of univariate data. The distinctly dissimilar distribution of data for different days of the week, however, can affect filter implementations adversely, and so a novel procedure based on linear transformations of the sorted values of the daily counts is introduced. Seven-days ahead daily predicted counts are used as background estimates. A signal injection procedure is used to examine the integrated algorithm's ability to detect synthetic anomalies in real syndromic time series. We compare the method to a baseline CDC forecasting algorithm known as the W2 method.ResultsWe present detection results in the form of Receiver Operating Characteristic curve values for four different injected signal to noise ratios using 16 sets of syndromic data. We find improvements in the false alarm probabilities when compared to the baseline W2 background forecasts.ConclusionThe current paper introduces a prediction approach for city-level biosurveillance data streams such as time series of outpatient clinic visits and sales of over-the-counter remedies. This approach uses RLS filters modified by a correction for the weekly patterns often seen in these data series, and a threshold detection algorithm from the residuals of the RLS forecasts. We compare the detection performance of this algorithm to the W2 method recently implemented at CDC. The modified RLS method gives consistently better sensitivity at multiple background alert rates, and we recommend that it should be considered for routine application in bio-surveillance systems.1-R01-PH000024-01/PH/PHPPO CDC HHS/United State
BMC Med Inform Decis Mak
No full textBackgroundSurveillance of univariate syndromic data as a means of potential indicator of developing public health conditions has been used extensively. This paper aims to improve the performance of detecting outbreaks by using a background forecasting algorithm based on the adaptive recursive least squares method combined with a novel treatment of the Day of the Week effect.MethodsPrevious work by the first author has suggested that univariate recursive least squares analysis of syndromic data can be used to characterize the background upon which a prediction and detection component of a biosurvellance system may be built. An adaptive implementation is used to deal with data non-stationarity. In this paper we develop and implement the RLS method for background estimation of univariate data. The distinctly dissimilar distribution of data for different days of the week, however, can affect filter implementations adversely, and so a novel procedure based on linear transformations of the sorted values of the daily counts is introduced. Seven-days ahead daily predicted counts are used as background estimates. A signal injection procedure is used to examine the integrated algorithm's ability to detect synthetic anomalies in real syndromic time series. We compare the method to a baseline CDC forecasting algorithm known as the W2 method.ResultsWe present detection results in the form of Receiver Operating Characteristic curve values for four different injected signal to noise ratios using 16 sets of syndromic data. We find improvements in the false alarm probabilities when compared to the baseline W2 background forecasts.ConclusionThe current paper introduces a prediction approach for city-level biosurveillance data streams such as time series of outpatient clinic visits and sales of over-the-counter remedies. This approach uses RLS filters modified by a correction for the weekly patterns often seen in these data series, and a threshold detection algorithm from the residuals of the RLS forecasts. We compare the detection performance of this algorithm to the W2 method recently implemented at CDC. The modified RLS method gives consistently better sensitivity at multiple background alert rates, and we recommend that it should be considered for routine application in bio-surveillance systems
Design of Miniaturized Multiband Filters Using Zero Order Resonators for WLAN Applications
No full textabstract: The objective of this paper is to design miniaturized narrow- and dual-band filters for WLAN application using zero order resonators by the method of least squares. The miniaturization of the narrow-band filter is up to 70% and that of the dual-band filter is up to 64% compared to the available models in the literature. Two prototype models of the narrow-band and dual-band filters are fabricated and measured, which verify the proposed structure for the filter and its design by the presented method, using an equivalent circuit model.View the article as published at https://www.hindawi.com/journals/ijmst/2015/345326
Some Upper Bounds for the Dimension of the c-Nilpotent Multiplier of a Pair of Lie Algebras
No full textThe notion of the Schur multiplier of a Lie algebra L was introduced by Batten in 1996. Recently, the first author introduced the concept of the cnilpotent multiplier of a pair of Lie algebras and gave some exact sequences for the c-nilpotent multiplier of a pair of Lie algebras. The purpose of this paper is to derive some inequalities for dimension of the c-nilpotent multiplier of a pair of Lie algebras
Recursive least squares background prediction of univariate syndromic surveillance data
No full textAbstract Background Surveillance of univariate syndromic data as a means of potential indicator of developing public health conditions has been used extensively. This paper aims to improve the performance of detecting outbreaks by using a background forecasting algorithm based on the adaptive recursive least squares method combined with a novel treatment of the Day of the Week effect. Methods Previous work by the first author has suggested that univariate recursive least squares analysis of syndromic data can be used to characterize the background upon which a prediction and detection component of a biosurvellance system may be built. An adaptive implementation is used to deal with data non-stationarity. In this paper we develop and implement the RLS method for background estimation of univariate data. The distinctly dissimilar distribution of data for different days of the week, however, can affect filter implementations adversely, and so a novel procedure based on linear transformations of the sorted values of the daily counts is introduced. Seven-days ahead daily predicted counts are used as background estimates. A signal injection procedure is used to examine the integrated algorithm's ability to detect synthetic anomalies in real syndromic time series. We compare the method to a baseline CDC forecasting algorithm known as the W2 method. Results We present detection results in the form of Receiver Operating Characteristic curve values for four different injected signal to noise ratios using 16 sets of syndromic data. We find improvements in the false alarm probabilities when compared to the baseline W2 background forecasts. Conclusion The current paper introduces a prediction approach for city-level biosurveillance data streams such as time series of outpatient clinic visits and sales of over-the-counter remedies. This approach uses RLS filters modified by a correction for the weekly patterns often seen in these data series, and a threshold detection algorithm from the residuals of the RLS forecasts. We compare the detection performance of this algorithm to the W2 method recently implemented at CDC. The modified RLS method gives consistently better sensitivity at multiple background alert rates, and we recommend that it should be considered for routine application in bio-surveillance systems.</p
Examining the latent structure of Sense of Social and Academic Fit
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Previous issue date: 2021-07-08Embargo set by: Seth Robbins for item 121076
Lift date: 2024-01-12T22:35:30Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I OnlySense of belonging is a psychological construct that has been theorized to be a fundamental human need and shown to have important implications in many domains of life, including academic achievement. The Sense of Social and Academic Fit Scale (Walton & Cohen, 2007) has been widely used to assess sense of college belonging among undergraduate college students, particularly to explore academic disparities across lines of gender, social class, and race/ethnicity. Despite its wide use, the instrument’s latent factor structure and measurement invariance properties have not been reported in the published literature to date. Moreover, the need for short measures has led researchers to use subsets of the scale’s items without an established theoretical or statistical basis. Thus, I sought to examine and validate the factor structure of this scale, to analyze its measurement invariance properties across the demographic categories listed above, and to derive and validate a brief measure of college belonging using a subset of the original scale’s items. Single-factor models of the full measure showed poor fit to the data, and inspection of local misfit suggested the presence of multiple correlated factors. Exploratory factor analyses recovered a four-factor model with the following latent factors: Institutional Belonging, Cultural Match, Social Acceptance, and Social Capital. Additionally, a brief five-item measure was derived that matched the Institutional Belonging factor of the full measure. This brief measure explained a substantial amount of the variance from the full measure and showed added benefits, such as external validity in an independent dataset, strict invariance across social class and race/ethnicity, and metric invariance across gender. The other models did not achieve measurement invariance in any category, nor did they show acceptable fit with an independent dataset. Implications and suggestions for future research are discussed.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01The student, Amir Maghsoodi, accepted the attached license on 2021-07-07 at 16:30.The student, Amir Maghsoodi, submitted this Thesis for approval on 2021-07-07 at 16:42.This Thesis was approved for publication on 2021-07-08 at 14:04
Considerations on a revision of the quality factor
No full textA modified analytical expression is proposed for the revised quality factor that has been suggested by a liaison group of ICRP and ICRU. With this modification one obtains, for sparsely ionizing radiation, a quality factor which is proportional to the dose average of lineal energy, y. It is shown that the proposed relation between the quality factor and lineal energy can be translated into a largely equivalent dependence on LET. The choice between the reference parameters LET or y is therefore a secondary problem in an impending revision of the quality factor
Effects of humor therapy on fatigue and depression of multiple sclerosis (MS) patients
No full textEffects of humor therapy on fatigue and depression of multiple sclerosis (MS) patients Moshtagh Eshgh, Z.1* (MSN); Naghavi, B.2 (MS); Rashvand, F.3 (MSN); Alavi Majd, H.4 (PhD), Bana Derakhshan, H. (MSN)5 1. Lecturer, Dept. of Medical-Surgical Nursing, Faculty of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran. *(Corresponding Author) e-mail:[email protected]. Lecturer, Dept. of Basic Sciences, Faculty of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran.3. Lecturer, Azad University of Abhar, Abhar, Iran.4. Associate Professor, Dept. of Biostatistics, Faculty of Paramedical, Shahid Beheshti University of Medical Sciences, Tehran, Iran.5.Lecturer, Dept. of operating room & Anesthesia, Faculty of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Abstract Background and aimMultiple sclerosis (MS) is the most common disabling condition in young adults, which is caused by an inflammatory demyelination process in central nervous system. Fatigue and depression are the primary symptoms leading to dysfunction as well as disability in activities of daily living and decreased quality of life. Because of many drug-associated complications, applying other methods to lessen the symptoms seems reasonable. The aim of this study was to determine the effects of humor on fatigue and depression of clients referring to Iranian MS Society. Materials and methodsIn this one-group before-after clinical trial, 30 MS clients were selected by convenience sampling method. A 4-part questionnaire including demographics, items related to the condition, Fatigue Severity Scale (FSS) and Beck's Depression Inventory was used for data collection, validated and made reliable by content and test-retest methods respectively. The clients took part in humor therapy sessions 3 times a week, each lasting 30 minutes for 12 weeks. The sessions were hold during the day with entertaining and funny programs recorded on compact discs (CDs). The clients completed the questionnaire before and after the intervention. Data were then analyzed by different statistical methods. Findings A significant decrease was found in mean severities of fatigue and depression after the intervention (
