The Experts below are selected from a list of 13341 Experts worldwide ranked by ideXlab platform
Daniela De Venuto - One of the best experts on this subject based on the ideXlab platform.
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Fall Risk Assessment by combined movement related potentials and co contraction index monitoring
Biomedical Circuits and Systems Conference, 2015Co-Authors: V Annese, Daniela De VenutoAbstract:In this paper we propose a novel approach for online Fall-Risk Assessment based on concurrent EEG and EMG monitoring. The Fall-Risk evaluation is based on: i) clinical condition of the individual, ii) environment, iii) EMG agonist-antagonist co-contraction analysis and iv) Movement Related Potentials and event related desynchronizations occurrence/absence. The Fall-Risk Assessment evaluation algorithm has been implemented on a FPGA (Altera Cyclone V SE 5CSEMA5F31C6N) in order to realize an autonomous and stand-alone Fall prevention tool. The experimental results (based on a dataset of 10 individuals) are described and demonstrate the validity of the algorithm and its FPGA implementation, which responds in 41ms, well within the 300ms time limit according to a study on 45 Fallers and 80 non-Fallers (with 74 years average age).
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fpga based architecture for Fall Risk Assessment during gait monitoring by synchronous eeg emg
IEEE International Workshop on Advances in Sensors and Interfaces, 2015Co-Authors: V Annese, Daniela De VenutoAbstract:One out of three subjects older than 65 years Falls. Despite extensive research, existing Assessment tools for Fall Risk have been insufficient for predicting Falls since the phenomenology is complex and there is no equipment on the market that allows everyday life monitoring. In this paper we present a novel approach for Fall-Risk on-line Assessment based on: i) clinical condition of the subject, ii) environmental conditions, iii) electromyographic (EMG) co-contraction analysis and iv) electroencephalographic (EEG) analysis based on Movement Related Potentials (MRPs) and μ-rhythm event related desynchronizations (μ-ERDs) occurrence. This Fall-Risk Assessment approach is implemented by a complete cyber-physical system made up by EEG and EMG wearable recording systems interfaced to an FPGA on-line performing the needed real-time processing for indexes extraction. The results present a Fall-Risk Assessment case study on healthy subjects walking showing detectable Fall-Risk increasing (+1.5%) when obstacles are overcome.
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BioCAS - Fall-Risk Assessment by combined movement related potentials and co-contraction index monitoring
2015 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2015Co-Authors: V Annese, Daniela De VenutoAbstract:In this paper we propose a novel approach for online Fall-Risk Assessment based on concurrent EEG and EMG monitoring. The Fall-Risk evaluation is based on: i) clinical condition of the individual, ii) environment, iii) EMG agonist-antagonist co-contraction analysis and iv) Movement Related Potentials and event related desynchronizations occurrence/absence. The Fall-Risk Assessment evaluation algorithm has been implemented on a FPGA (Altera Cyclone V SE 5CSEMA5F31C6N) in order to realize an autonomous and stand-alone Fall prevention tool. The experimental results (based on a dataset of 10 individuals) are described and demonstrate the validity of the algorithm and its FPGA implementation, which responds in 41ms, well within the 300ms time limit according to a study on 45 Fallers and 80 non-Fallers (with 74 years average age).
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IWASI - FPGA based architecture for Fall-Risk Assessment during gait monitoring by synchronous EEG/EMG
2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI), 2015Co-Authors: V Annese, Daniela De VenutoAbstract:One out of three subjects older than 65 years Falls. Despite extensive research, existing Assessment tools for Fall Risk have been insufficient for predicting Falls since the phenomenology is complex and there is no equipment on the market that allows everyday life monitoring. In this paper we present a novel approach for Fall-Risk on-line Assessment based on: i) clinical condition of the subject, ii) environmental conditions, iii) electromyographic (EMG) co-contraction analysis and iv) electroencephalographic (EEG) analysis based on Movement Related Potentials (MRPs) and μ-rhythm event related desynchronizations (μ-ERDs) occurrence. This Fall-Risk Assessment approach is implemented by a complete cyber-physical system made up by EEG and EMG wearable recording systems interfaced to an FPGA on-line performing the needed real-time processing for indexes extraction. The results present a Fall-Risk Assessment case study on healthy subjects walking showing detectable Fall-Risk increasing (+1.5%) when obstacles are overcome.
Matjaž Gams - One of the best experts on this subject based on the ideXlab platform.
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Detection of Gait Abnormalities for Fall Risk Assessment Using Wrist-Worn Inertial Sensors and Deep Learning.
Sensors, 2020Co-Authors: Ivana Kiprijanovska, Hristijan Gjoreski, Matjaž GamsAbstract:Falls are a significant threat to the health and independence of elderly people and represent an enormous burden on the healthcare system. Successfully predicting Falls could be of great help, yet this requires a timely and accurate Fall Risk Assessment. Gait abnormalities are one of the best predictive signs of underlying locomotion conditions and precursors of Falls. The advent of wearable sensors and wrist-worn devices provides new opportunities for continuous and unobtrusive monitoring of gait during daily activities, including the identification of unexpected changes in gait. To this end, we present in this paper a novel method for determining gait abnormalities based on a wrist-worn device and a deep neural network. It integrates convolutional and bidirectional long short-term memory layers for successful learning of spatiotemporal features from multiple sensor signals. The proposed method was evaluated using data from 18 subjects, who recorded their normal gait and simulated abnormal gait while wearing impairment glasses. The data consist of inertial measurement unit (IMU) sensor signals obtained from smartwatches that the subjects wore on both wrists. Numerous experiments showed that the proposed method provides better results than the compared methods, achieving 88.9% accuracy, 90.6% sensitivity, and 86.2% specificity in the detection of abnormal walking patterns using data from an accelerometer, gyroscope, and rotation vector sensor. These results indicate that reliable Fall Risk Assessment is possible based on the detection of walking abnormalities with the use of wearable sensors on a wrist.
V Annese - One of the best experts on this subject based on the ideXlab platform.
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Fall Risk Assessment by combined movement related potentials and co contraction index monitoring
Biomedical Circuits and Systems Conference, 2015Co-Authors: V Annese, Daniela De VenutoAbstract:In this paper we propose a novel approach for online Fall-Risk Assessment based on concurrent EEG and EMG monitoring. The Fall-Risk evaluation is based on: i) clinical condition of the individual, ii) environment, iii) EMG agonist-antagonist co-contraction analysis and iv) Movement Related Potentials and event related desynchronizations occurrence/absence. The Fall-Risk Assessment evaluation algorithm has been implemented on a FPGA (Altera Cyclone V SE 5CSEMA5F31C6N) in order to realize an autonomous and stand-alone Fall prevention tool. The experimental results (based on a dataset of 10 individuals) are described and demonstrate the validity of the algorithm and its FPGA implementation, which responds in 41ms, well within the 300ms time limit according to a study on 45 Fallers and 80 non-Fallers (with 74 years average age).
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fpga based architecture for Fall Risk Assessment during gait monitoring by synchronous eeg emg
IEEE International Workshop on Advances in Sensors and Interfaces, 2015Co-Authors: V Annese, Daniela De VenutoAbstract:One out of three subjects older than 65 years Falls. Despite extensive research, existing Assessment tools for Fall Risk have been insufficient for predicting Falls since the phenomenology is complex and there is no equipment on the market that allows everyday life monitoring. In this paper we present a novel approach for Fall-Risk on-line Assessment based on: i) clinical condition of the subject, ii) environmental conditions, iii) electromyographic (EMG) co-contraction analysis and iv) electroencephalographic (EEG) analysis based on Movement Related Potentials (MRPs) and μ-rhythm event related desynchronizations (μ-ERDs) occurrence. This Fall-Risk Assessment approach is implemented by a complete cyber-physical system made up by EEG and EMG wearable recording systems interfaced to an FPGA on-line performing the needed real-time processing for indexes extraction. The results present a Fall-Risk Assessment case study on healthy subjects walking showing detectable Fall-Risk increasing (+1.5%) when obstacles are overcome.
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BioCAS - Fall-Risk Assessment by combined movement related potentials and co-contraction index monitoring
2015 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2015Co-Authors: V Annese, Daniela De VenutoAbstract:In this paper we propose a novel approach for online Fall-Risk Assessment based on concurrent EEG and EMG monitoring. The Fall-Risk evaluation is based on: i) clinical condition of the individual, ii) environment, iii) EMG agonist-antagonist co-contraction analysis and iv) Movement Related Potentials and event related desynchronizations occurrence/absence. The Fall-Risk Assessment evaluation algorithm has been implemented on a FPGA (Altera Cyclone V SE 5CSEMA5F31C6N) in order to realize an autonomous and stand-alone Fall prevention tool. The experimental results (based on a dataset of 10 individuals) are described and demonstrate the validity of the algorithm and its FPGA implementation, which responds in 41ms, well within the 300ms time limit according to a study on 45 Fallers and 80 non-Fallers (with 74 years average age).
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IWASI - FPGA based architecture for Fall-Risk Assessment during gait monitoring by synchronous EEG/EMG
2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI), 2015Co-Authors: V Annese, Daniela De VenutoAbstract:One out of three subjects older than 65 years Falls. Despite extensive research, existing Assessment tools for Fall Risk have been insufficient for predicting Falls since the phenomenology is complex and there is no equipment on the market that allows everyday life monitoring. In this paper we present a novel approach for Fall-Risk on-line Assessment based on: i) clinical condition of the subject, ii) environmental conditions, iii) electromyographic (EMG) co-contraction analysis and iv) electroencephalographic (EEG) analysis based on Movement Related Potentials (MRPs) and μ-rhythm event related desynchronizations (μ-ERDs) occurrence. This Fall-Risk Assessment approach is implemented by a complete cyber-physical system made up by EEG and EMG wearable recording systems interfaced to an FPGA on-line performing the needed real-time processing for indexes extraction. The results present a Fall-Risk Assessment case study on healthy subjects walking showing detectable Fall-Risk increasing (+1.5%) when obstacles are overcome.
Laurence Z. Rubenstein - One of the best experts on this subject based on the ideXlab platform.
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A multivariate Fall Risk Assessment model for VHA nursing homes using the minimum data set.
Journal of the American Medical Directors Association, 2006Co-Authors: Dustin D. French, Audrey Nelson, Laurence Z. Rubenstein, Dennis C. Werner, Robert R. Campbell, Gail Powell-cope, Tatjana Bulat, Andrea M. SpeharAbstract:Objectives The purpose of this study was to develop a multivariate Fall Risk Assessment model beyond the current Fall Resident Assessment Protocol (RAP) triggers for nursing home residents using the Minimum Data Set (MDS). Design Retrospective, clustered secondary data analysis. Setting: National Veterans Health Administration (VHA) long-term care nursing homes (N = 136). Participants The study population consisted of 6577 national VHA nursing home residents who had an annual Assessment during FY 2005, identified from the MDS, as well as an earlier annual or admission Assessment within a 1-year look-back period. Measurement A dichotomous multivariate model of nursing home residents coded with a Fall on selected Fall Risk characteristics from the MDS, estimated with general estimation equations (GEE). Results There were 17 170 Assessments corresponding to 6577 long-term care nursing home residents. The increased odds ratio (OR) of being classified as a Faller relative to the omitted “dependent” category of activities of daily living (ADL) ranged from OR = 1.35 for “limited” ADL category up to OR = 1.57 for “extensive-2” ADL (P Conclusions This national study in one of the largest managed healthcare systems in the United States has empirically confirmed the relative importance of certain Risk factors for Falls in long-term care settings. The model incorporated an ADL index and adjusted for case mix by including only long-term care nursing home residents. The study offers clinicians practical estimates by combining multiple univariate MDS elements in an empirically based, multivariate Fall Risk Assessment model.
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Fall Risk Assessment Measures An Analytic Review
The journals of gerontology. Series A Biological sciences and medical sciences, 2001Co-Authors: Karen L. Perell, Audrey Nelson, Ronald L. Goldman, Stephen L. Luther, Nicole Prieto-lewis, Laurence Z. RubensteinAbstract:Clinicians are often unaware of the many existing scales for identifying Fall Risk and are uncertain about how to select an appropriate one. Our purpose was to summarize existing Fall Risk Assessment scales to enable more informed choices regarding their use. After a systematic literature search, 21 articles published from 1984 through 2000 describing 20 Fall Risk Assessments were reviewed independently for content and validation by a panel of five reviewers using a standardized review form. Fourteen were institution-focused nursing Assessment scales, and six were functional Assessment scales. The majority of the scales were developed for elderly populations, mainly in hospital or nursing home settings. The patient characteristics assessed were quite similar across the nursing Assessment forms. The time to complete the form varied from less than 1 minute to 80 minutes. For those scales with reported diagnostic accuracy, sensitivity varied from 43% to 100% (median = 80%), and specificity varied from 38% to 96% (median = 75%). Several scales with superior diagnostic characteristics were identified. A substantial number of Fall Risk Assessment tools are readily available and assess similar patient characteristics. Although their diagnostic accuracy and overall usefulness showed wide variability, there are several scales that can be used with confidence as part of an effective Falls prevention program. Consequently, there should be little need for facilities to develop their own scales. To continue to develop Fall Risk Assessments unique to individual facilities may be counterproductive because scores will not be comparable across facilities.
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Fall Risk Assessment measures an analytic review
Journals of Gerontology Series A-biological Sciences and Medical Sciences, 2001Co-Authors: Karen L. Perell, Audrey Nelson, Ronald L. Goldman, Stephen L. Luther, Nicole Prietolewis, Laurence Z. RubensteinAbstract:BACKGROUND Clinicians are often unaware of the many existing scales for identifying Fall Risk and are uncertain about how to select an appropriate one. Our purpose was to summarize existing Fall Risk Assessment scales to enable more informed choices regarding their use. METHODS After a systematic literature search, 21 articles published from 1984 through 2000 describing 20 Fall Risk Assessments were reviewed independently for content and validation by a panel of five reviewers using a standardized review form. Fourteen were institution-focused nursing Assessment scales, and six were functional Assessment scales. RESULTS The majority of the scales were developed for elderly populations, mainly in hospital or nursing home settings. The patient characteristics assessed were quite similar across the nursing Assessment forms. The time to complete the form varied from less than 1 minute to 80 minutes. For those scales with reported diagnostic accuracy, sensitivity varied from 43% to 100% (median = 80%), and specificity varied from 38% to 96% (median = 75%). Several scales with superior diagnostic characteristics were identified. CONCLUSIONS A substantial number of Fall Risk Assessment tools are readily available and assess similar patient characteristics. Although their diagnostic accuracy and overall usefulness showed wide variability, there are several scales that can be used with confidence as part of an effective Falls prevention program. Consequently, there should be little need for facilities to develop their own scales. To continue to develop Fall Risk Assessments unique to individual facilities may be counterproductive because scores will not be comparable across facilities.
Seon Heui Lee - One of the best experts on this subject based on the ideXlab platform.
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A Systematic Review of the Guidelines and Delphi Study for the Multifactorial Fall Risk Assessment of Community-Dwelling Elderly.
International journal of environmental research and public health, 2020Co-Authors: Jieun Kim, Worl-sook Lee, Seon Heui LeeAbstract:As Falls are among the most common causes of injury for the elderly, the prevention and early intervention are necessary. Fall Assessment tools that include a variety of factors are recommended for preventing Falls, but there is a lack of such tools. This study developed a multifactorial Fall Risk Assessment tool based on current guidelines and validated it from the perspective of professionals. We followed the Meta-Analysis of Observational Studies in Epidemiology's guidelines in this systematic review. We used eight international and five Korean databases to search for appropriate guidelines. Based on the review results, we conducted the Delphi survey in three rounds; one open round and two scoring rounds. About nine experts in five professional areas participated in the Delphi study. We included nine guidelines. After conducting the Delphi study, the final version of the "Multifactorial Fall Risk Assessment tool for Community-Dwelling Older People" (MFA-C) has 36 items in six factors; general characteristics, behavior factors, disease history, medication history, physical function, and environmental factors. The validity of the MFA-C tool was largely supported by various academic fields. It is expected to be beneficial to the elderly in the community when it comes to tailored interventions to prevent Falls.