Gait-stabilizing devices (GSDs) are effective at preventing falls, but people are often reluctant to use them until after experiencing a fall. Inexpensive, convenient, and effective methods for predicting which patients need GSDs could help improve adoption. The purpose of this study was to determine if a Wii Balance Board (WBB) can be used to determine whether or not patients use a GSD. We prospectively recruited participants ages 70-100, some who used GSDs and some who did not. Participants first answered questions from the Modified Vulnerable Elders Survey, and then completed a grip-strength test using a handgrip dynamometer. Finally, they were asked to complete a series of four 30-second balance tests on a WBB in random order: (1) eyes open, feet apart; (2) eyes open, feet together; (3) eyes closed, feet apart; and (4) eyes closed, feet together. The four-test series was repeated a second time in the same random order. The resulting data, represented as 25 features extracted from the questionnaires and the grip test, and data from the eight balance tests, were used to predict a subject's GSD use using generalized functional linear models based on the Bernoulli distribution. 268 participants were consented; 62 were missing data elements and were removed from analysis; 109 were not GSD users and 97 were GSD users. The use of velocity and acceleration information from the WBB improved upon predictions based solely on grip strength, demographic, and survey variables. The WBB is a convenient, inexpensive, and easy-to-use device that can be used to recommend whether or not patients should be using a GSD.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10553233 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0292548 | PLOS |
Am J Crit Care
January 2025
Mona N. Bahouth is medical director, Brain Rescue Unit and an associate professor of neurology, Johns Hopkins University School of Medicine.
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Sensors (Basel)
December 2024
Department of Engineering, University of Messina, Contrada di Dio, 98166 Messina, Italy.
The knee is one of the joints most vulnerable to disease and injury, particularly in athletes and older adults. Surface temperature monitoring provides insights into the health of the analysed area, supporting early diagnosis and monitoring of conditions such as osteoarthritis and tendon injuries. This study presents an innovative approach that combines infrared thermography techniques with a Resnet 152 (DeepLabCut based) to detect and monitor temperature variations across specific knee regions during repeated sit-to-stand exercises.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Mechanical Engineering, The University of Tokyo, Tokyo 113-8654, Japan.
Fatigue plays a critical role in sports science, significantly affecting recovery, training effectiveness, and overall athletic performance. Understanding and predicting fatigue is essential to optimize training, prevent overtraining, and minimize the risk of injuries. The aim of this study is to leverage Human Activity Recognition (HAR) through deep learning methods for dimensionality reduction.
View Article and Find Full Text PDFJ Bodyw Mov Ther
March 2025
Department of Ergonomics, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran. Electronic address:
Purpose: Poor performance in practicing balance tasks is partly caused by the changes in the knee function and balance control activities following total knee arthroplasty (TKA). The purpose of this study was to determine the test-retest reliability of the static and dynamic balance measures using Wii Balance Board (WBB) in patients after TKA.
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J Stroke Cerebrovasc Dis
January 2025
Higher School of Health, Polytechnic Institute of Viana do Castelo, Portugal.
Background: It is essential to explore alternative methods to motivate stroke survivors throughout their lengthy rehabilitation journey. Exergames have emerged as promising tools for rehabilitating this demographic.
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