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https://www.readbyqxmd.com/read/28443059/algorithm-for-turning-detection-and-analysis-validated-under-home-like-conditions-in-patients-with-parkinson-s-disease-and-older-adults-using-a-6-degree-of-freedom-inertial-measurement-unit-at-the-lower-back
#1
Minh H Pham, Morad Elshehabi, Linda Haertner, Tanja Heger, Markus A Hobert, Gert S Faber, Dina Salkovic, Joaquim J Ferreira, Daniela Berg, Álvaro Sanchez-Ferro, Jaap H van Dieën, Walter Maetzler
INTRODUCTION: Aging and age-associated disorders such as Parkinson's disease (PD) are often associated with turning difficulties, which can lead to falls and fractures. Valid assessment of turning and turning deficits specifically in non-standardized environments may foster specific treatment and prevention of consequences. METHODS: Relative orientation, obtained from 3D-accelerometer and 3D-gyroscope data of a sensor worn at the lower back, was used to develop an algorithm for turning detection and qualitative analysis in PD patients and controls in non-standardized environments...
2017: Frontiers in Neurology
https://www.readbyqxmd.com/read/28432935/accelerometry-based-assessment-and-detection-of-early-signs-of-balance-deficits
#2
Heidi Similä, Milla Immonen, Miikka Ermes
Falls are the cause for more than half of the injury-related hospitalizations among older people. Accurate assessment of individuals' fall risk could enable targeted interventions to reduce the risk. This paper presents a novel method for using wearable accelerometers to detect early signs of deficits in balance from gait. Gait acceleration data were analyzed from 35 healthy female participants (73.86±5.40 years). The data were collected with waist-mounted accelerometer and the participants performed three supervised balance tests: Berg Balance Scale (BBS), Timed-Up-and-Go (TUG) and 4m walk...
April 13, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28415071/lower-physical-activity-in-persons-with-multiple-sclerosis-at-increased-fall-risk-a-cross-sectional-study
#3
Emerson Sebastião, Yvonne C Learmonth, Robert W Motl
Persons with multiple sclerosis (MS) often report being afraid of falling, and this may have effects on physical activity (PA) engagement. This study investigated PA levels in persons with MS as a function of fall risk categories. Forty-seven persons with MS participated in the study and were categorized into either increased fall risk (IFR; n = 21; 55.5 ± 9.0 years) or normal fall risk (NFR; n = 26; 51.2 ± 12.9 years) groups based on scores from the Activities-Balance Confidence scale. PA was measured by accelerometer and expressed as average steps per day, and time spent in sedentary behavior, light PA, and moderate to vigorous physical activity over the course of 7 consecutive days...
May 2017: American Journal of Physical Medicine & Rehabilitation
https://www.readbyqxmd.com/read/28358689/prospective-fall-risk-prediction-models-for-older-adults-based-on-wearable-sensors
#4
Jennifer Howcroft, Jonathan Kofman, Edward Lemaire
Wearable sensors can provide quantitative, gait-based assessments that can translate to point-of-care environments. This investigation generated elderly fall-risk predictive models based on wearable-sensor-derived gait data and prospective fall occurrence; and identified the optimal sensor type, location, and combination for single and dual-task walking. 75 individuals who reported six month prospective fall occurrence (75.2 ± 6.6 years; 47 non-fallers, 28 fallers) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks...
March 24, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28291838/level-and-correlates-of-physical-activity-and-sedentary-behavior-in-patients-with-type-2-diabetes-a-cross-sectional-analysis-of-the-italian-diabetes-and-exercise-study_2
#5
Stefano Balducci, Valeria D'Errico, Jonida Haxhi, Massimo Sacchetti, Giorgio Orlando, Patrizia Cardelli, Nicolina Di Biase, Lucilla Bollanti, Francesco Conti, Silvano Zanuso, Antonio Nicolucci, Giuseppe Pugliese
OBJECTIVE: Patients with type 2 diabetes usually show reduced physical activity (PA) and increased sedentary (SED)-time, though to a varying extent, especially for low-intensity PA (LPA), a major determinant of daily energy expenditure that is not accurately captured by questionnaires. This study assessed the level and correlates of PA and SED-time in patients from the Italian Diabetes and Exercise Study_2 (IDES_2). METHODS: Three-hundred physically inactive and sedentary patients with type 2 diabetes were enrolled in the IDES_2 to be randomized to an intervention group, receiving theoretical and practical exercise counseling, and a control group, receiving standard care...
2017: PloS One
https://www.readbyqxmd.com/read/28269666/principal-component-analysis-can-decrease-neural-networks-performance-for-incipient-falls-detection-a-preliminary-study-with-hands-and-feet-accelerations
#6
Fiorenzo Artoni, Dario Martelli, Vito Monaco, Silvestre Micera
Fall-related accidents constitute a major problem for elderly people and a burden to the health-care national system. It is therefore important to design devices (e.g., accelerometers) and machine learning algorithms able to recognize incipient falls as quickly and reliably as possible. Blind source separation (BSS) methods are often used as a preprocessing step before classification, however the effects of BSS on classification performance are not well understood. The aim of this work is to preliminarily characterize the effect that two methods, namely Principal and Independent Component Analysis (PCA and ICA) and their combined use have on the performance of a neural network in detecting incipient falls...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269392/identifying-the-number-and-location-of-body-worn-sensors-to-accurately-classify-walking-transferring-and-sedentary-activities
#7
Omar Aziz, Stephen N Robinovitch, Edward J Park
In order to perform fall risk assessments using wearable inertial sensors in older adults in their natural settings where falls are likely to occur, a first step is to automatically segment and classify sensor signals of human movements into the known `activities of interest'. Sensor data from such activities can later be used through quantitative and qualitative analysis for differentiating fallers from non-fallers. In this study, ten young adults participated in experimental trials involving several variations of walking, transferring and sedentary activities...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269347/uhf-wearable-battery-free-sensor-module-for-activity-and-falling-detection
#8
Nam Trung Dang, Thang Viet Tran, Wan-Young Chung
Falling is one of the most serious medical and social problems in aging population. Therefore taking care of the elderly by detecting activity and falling for preventing and mitigating the injuries caused by falls needs to be concerned. This study proposes a wearable, wireless, battery free ultra-high frequency (UHF) smart sensor tag module for falling and activity detection. The proposed tag is powered by UHF RF wave from reader and read by a standard UHF Electronic Product Code (EPC) Class-1 Generation-2 reader...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269098/fall-detection-algorithms-for-real-world-falls-harvested-from-lumbar-sensors-in-the-elderly-population-a-machine-learning-approach
#9
Alan K Bourke, Jochen Klenk, Lars Schwickert, Kamiar Aminian, Espen A F Ihlen, Sabato Mellone, Jorunn L Helbostad, Lorenzo Chiari, Clemens Becker
Automatic fall detection will promote independent living and reduce the consequences of falls in the elderly by ensuring people can confidently live safely at home for linger. In laboratory studies inertial sensor technology has been shown capable of distinguishing falls from normal activities. However less than 7% of fall-detection algorithm studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events and to develop fall detection algorithms to combat the problems associated with falls...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268967/two-threshold-energy-based-fall-detection-using-a-triaxial-accelerometer
#10
Angela Sucerquia, Jose D Lopez, Francisco Vargas
Elderly fall detection based on accelerometers is an active research area. Nowadays authors are addressing specific problems such as failure rates and energy consumption, but in most cases their strategies do not conciliate these objectives. In this paper we propose a double threshold based methodology with two novel detection features, a product between the sum vector magnitude and the signal magnitude area, and a normalization of the signal magnitude area over five 1 s windows. The methodology was validated using the public Mobifall dataset, and one developed for this work...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268412/towards-holistic-free-living-assessment-in-parkinson-s-disease-unification-of-gait-and-fall-algorithms-with-a-single-accelerometer
#11
Alan Godfrey, Alan Bourke, Silvia Del Din, Rosie Morris, Aodhan Hickey, Jorunn L Helbostad, Lynn Rochester
Technological developments have seen the miniaturization of sensors, small enough to be embedded in wearable devices facilitating unobtrusive and longitudinal monitoring in free-living environments. Concurrently, the advances in algorithms have been ad-hoc and fragmented. To advance the mainstream use of wearable technology and improved functionality of algorithms all methodologies must be unified and robustly tested within controlled and free-living conditions. Here we present and unify a (i) gait segmentation and analysis algorithm and (ii) a fall detection algorithm...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28263256/factors-associated-with-ambulatory-activity-in-de-novo-parkinson-disease
#12
Cory Christiansen, Charity Moore, Margaret Schenkman, Benzi Kluger, Wendy Kohrt, Anthony Delitto, Brian Berman, Deborah Hall, Deborah Josbeno, Cynthia Poon, Julie Robichaud, Toby Wellington, Samay Jain, Cynthia Comella, Daniel Corcos, Ed Melanson
BACKGROUND AND PURPOSE: Objective ambulatory activity during daily living has not been characterized for people with Parkinson disease prior to initiation of dopaminergic medication. Our goal was to characterize ambulatory activity based on average daily step count and examine determinants of step count in nonexercising people with de novo Parkinson disease. METHODS: We analyzed baseline data from a randomized controlled trial, which excluded people performing regular endurance exercise...
April 2017: Journal of Neurologic Physical Therapy: JNPT
https://www.readbyqxmd.com/read/28251444/combining-novelty-detectors-to-improve-accelerometer-based-fall-detection
#13
Carlos Medrano, Raúl Igual, Iván García-Magariño, Inmaculada Plaza, Guillermo Azuara
Research on body-worn sensors has shown how they can be used for the detection of falls in the elderly, which is a relevant health problem. However, most systems are trained with simulated falls, which differ from those of the target population. In this paper, we tackle the problem of fall detection using a combination of novelty detectors. A novelty detector can be trained only with activities of daily life (ADL), which are true movements recorded in real life. In addition, they allow adapting the system to new users, by recording new movements and retraining the system...
March 1, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28251357/freezing-of-gait-and-fall-detection-in-parkinson-s-disease-using-wearable-sensors-a-systematic-review
#14
REVIEW
Ana Lígia Silva de Lima, Luc J W Evers, Tim Hahn, Lauren Bataille, Jamie L Hamilton, Max A Little, Yasuyuki Okuma, Bastiaan R Bloem, Marjan J Faber
Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable systems to assess FOG and falls in Parkinson's disease (PD) and validation performance is presented. A systematic search in the PubMed and Web of Science databases was performed using a group of concept key words...
March 1, 2017: Journal of Neurology
https://www.readbyqxmd.com/read/28248856/comparison-of-gait-symmetry-between-poststroke-fallers-and-nonfallers-during-level-walking-using-triaxial-accelerometry-a-strobe-compliant-cross-sectional-study
#15
COMPARATIVE STUDY
Wei-Chih Lien, Yung-Heng Cheng, Ta-Shen Kuan, Yu-Lun Zheng, Chao-Hsien Hsieh, Wen-Fong Wang
To compare the degree of gait symmetry of chronic poststroke fallers with that of nonfallers during level walking using triaxial accelerometry.In this cross-sectional study, a total of 14 patients with chronic stroke were recruited from a community hospital from February 2015 to July 2016. Patient characteristics, including the number of falls in the previous 12 months, were obtained from medical records. The Berg Balance Scale (BBS) and timed up and go (TUG) test were used at the onset of the study. Triaxial accelerometers were attached to the back and bilateral lower extremities of each subject with sampling rates of 120 Hz...
March 2017: Medicine (Baltimore)
https://www.readbyqxmd.com/read/28227925/principal-component-analysis-can-decrease-neural-networks-performance-for-incipient-falls-detection-a-preliminary-study-with-hands-and-feet-accelerations
#16
Fiorenzo Artoni, Dario Martelli, Vito Monaco, Silvestre Micera, Fiorenzo Artoni, Dario Martelli, Vito Monaco, Silvestre Micera
Fall-related accidents constitute a major problem for elderly people and a burden to the health-care national system. It is therefore important to design devices (e.g., accelerometers) and machine learning algorithms able to recognize incipient falls as quickly and reliably as possible. Blind source separation (BSS) methods are often used as a preprocessing step before classification, however the effects of BSS on classification performance are not well understood. The aim of this work is to preliminarily characterize the effect that two methods, namely Principal and Independent Component Analysis (PCA and ICA) and their combined use have on the performance of a neural network in detecting incipient falls...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227635/identifying-the-number-and-location-of-body-worn-sensors-to-accurately-classify-walking-transferring-and-sedentary-activities
#17
Omar Aziz, Stephen N Robinovitch, Edward J Park, Omar Aziz, Stephen N Robinovitch, Edward J Park, Omar Aziz, Edward J Park, Stephen N Robinovitch
In order to perform fall risk assessments using wearable inertial sensors in older adults in their natural settings where falls are likely to occur, a first step is to automatically segment and classify sensor signals of human movements into the known `activities of interest'. Sensor data from such activities can later be used through quantitative and qualitative analysis for differentiating fallers from non-fallers. In this study, ten young adults participated in experimental trials involving several variations of walking, transferring and sedentary activities...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227589/uhf-wearable-battery-free-sensor-module-for-activity-and-falling-detection
#18
Nam Trung Dang, Thang Viet Tran, Wan-Young Chung, Nam Trung Dang, Thang Viet Tran, Wan-Young Chung, Thang Viet Tran, Wan-Young Chung, Nam Trung Dang
Falling is one of the most serious medical and social problems in aging population. Therefore taking care of the elderly by detecting activity and falling for preventing and mitigating the injuries caused by falls needs to be concerned. This study proposes a wearable, wireless, battery free ultra-high frequency (UHF) smart sensor tag module for falling and activity detection. The proposed tag is powered by UHF RF wave from reader and read by a standard UHF Electronic Product Code (EPC) Class-1 Generation-2 reader...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227321/fall-detection-algorithms-for-real-world-falls-harvested-from-lumbar-sensors-in-the-elderly-population-a-machine-learning-approach
#19
Alan K Bourke, Jochen Klenk, Lars Schwickert, Kamiar Aminian, Espen A F Ihlen, Sabato Mellone, Jorunn L Helbostad, Lorenzo Chiari, Clemens Becker, Alan K Bourke, Jochen Klenk, Lars Schwickert, Kamiar Aminian, Espen A F Ihlen, Sabato Mellone, Jorunn L Helbostad, Lorenzo Chiari, Clemens Becker, Lorenzo Chiari, Lars Schwickert, Kamiar Aminian, Clemens Becker, Jorunn L Helbostad, Jochen Klenk, Alan K Bourke, Sabato Mellone, Espen A F Ihlen
Automatic fall detection will promote independent living and reduce the consequences of falls in the elderly by ensuring people can confidently live safely at home for linger. In laboratory studies inertial sensor technology has been shown capable of distinguishing falls from normal activities. However less than 7% of fall-detection algorithm studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events and to develop fall detection algorithms to combat the problems associated with falls...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227174/two-threshold-energy-based-fall-detection-using-a-triaxial-accelerometer
#20
Angela Sucerquia, Jose D Lopez, Francisco Vargas, Angela Sucerquia, Jose D Lopez, Francisco Vargas, Angela Sucerquia, Francisco Vargas, Jose D Lopez
Elderly fall detection based on accelerometers is an active research area. Nowadays authors are addressing specific problems such as failure rates and energy consumption, but in most cases their strategies do not conciliate these objectives. In this paper we propose a double threshold based methodology with two novel detection features, a product between the sum vector magnitude and the signal magnitude area, and a normalization of the signal magnitude area over five 1 s windows. The methodology was validated using the public Mobifall dataset, and one developed for this work...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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