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Applied Sciences
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Falls: Risk, Prevention and Rehabilitation (2nd Edition)
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Falls: Risk, Prevention and Rehabilitation (2nd Edition)

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (20 January 2025) | Viewed by 4062

Special Issue Editors

Dr. Hanatsu Nagano

E-Mail Website
Guest Editor
Institute for Health and Sport (IHES), Victoria University, Melbourne, VIC 3011, Australia
Interests: minimisation of falls risks among older adults; understanding biomechanical factors for knee osteoarthritis; effects of 3D visual perception on flooring to control walking patterns to reduce slipping risks; biomechanical modelling and simulation of the major factors of falls when older adults are walking (i.e., tripping, slipping and balance loss); footwear ergonomics to control gait patterns
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rezaul Begg

E-Mail Website
Guest Editor
Institute for Health and Sport (IHES), Victoria University, Melbourne, VIC 3011, Australia
Interests: falls prevention; machine learning; wearabe sensor; assistive technologies for gait and posture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In many countries, advances in medical science and stronger social security systems are improving longevity. But the pursuit of longer, healthier, and more physically active lives, rather than simply prolonging life, is also critically important for both individuals and national security. Opportunities for people to lead healthier lives are, however, seriously compromised by fall-related injuries. In addition to high direct mortality, falls can lead to secondary health problems, a reduced quality of life, and considerable medical costs. Falls are, therefore, critically life-threatening for people. There is a worldwide research effort to combat this problem and our contribution will be a Special Issue for which we are inviting manuscript submissions in the following subdisciplines: (1) falls risk identification, (2) falls prevention strategies, and (3) rehabilitation interventions for falls-related injuries. Empirical research articles and comprehensive reviews will be considered. Sound study designs and high scholarly standards are essential, but we will also consider contributions reporting findings from smaller samples when there are constraints on participant recruitment and testing. Studies reporting the findings from practical physical health interventions in real-world settings are also prioritised topics.

Dr. Hanatsu Nagano
Prof. Dr. Rezaul Begg
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • falls prevention
  • tripping
  • slipping
  • rehabilitation
  • walking
  • gait
  • falls risk
  • sensor

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Related Special Issue

Published Papers (2 papers)

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Research

14 pages, 619 KiB  
Article
Predicting the Occurrence of Falls Among Portuguese Community-Dwelling Adults Aged 50 or Older Using the Timed up and Go Test
by Anabela Correia Martins, Juliana Moreira, Catarina Silva, Cláudia Tonelo and Clara Rocha
Appl. Sci. 2025, 15(8), 4370; https://doi.org/10.3390/app15084370 - 15 Apr 2025
Viewed by 181
Abstract
Falls are a major cause of morbidity and mortality among older adults. While the Timed Up and Go (TUG) test has recently been identified as the best predictor of falls, it should not be used in isolation to identify individuals at risk. This [...] Read more.
Falls are a major cause of morbidity and mortality among older adults. While the Timed Up and Go (TUG) test has recently been identified as the best predictor of falls, it should not be used in isolation to identify individuals at risk. This study aims to develop a predictive model by combining the TUG test with fall risk factors that involve intrinsic and extrinsic elements to predict future falls in Portuguese community-dwelling adults aged 50–60, 60–70, and 70 years or older. A total of 403 participants aged 50 or older completed a questionnaire on demographic information and fall risk factors, underwent the TUG test, and were monitored for 12 months to record falls. ROC curve analysis demonstrated that the TUG test alone effectively distinguished fallers from non-fallers exclusively among adults aged 50–60, with a cut-off time of 6.9 s. Multivariate logistic regression defined three predictive models based on age groups, with ROC curve results as follows: 50–60 (AUC = 0.825, cut-off = 18.1), 60–70 (AUC = 0.754, cut-off = 17.8), and 70 or older (AUC = 0.708, cut-off = 24.8). These findings are clinically significant, demonstrating that the TUG test combined with a few self-reported questions can efficiently identify individuals at risk of falling in just a few minutes, without requiring specialized equipment. Full article
(This article belongs to the Special Issue Falls: Risk, Prevention and Rehabilitation (2nd Edition))
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22 pages, 11975 KiB  
Article
Fall Risk Classification Using Trunk Movement Patterns from Inertial Measurement Units and Mini-BESTest in Community-Dwelling Older Adults: A Deep Learning Approach
by Diego Robles Cruz, Sebastián Puebla Quiñones, Andrea Lira Belmar, Denisse Quintana Figueroa, María Reyes Hidalgo and Carla Taramasco Toro
Appl. Sci. 2024, 14(20), 9170; https://doi.org/10.3390/app14209170 - 10 Oct 2024
Viewed by 3456
Abstract
Falls among older adults represent a critical global public health problem, as they are one of the main causes of disability in this age group. We have developed an automated approach to identifying fall risk using low-cost, accessible technology. Trunk movement patterns were [...] Read more.
Falls among older adults represent a critical global public health problem, as they are one of the main causes of disability in this age group. We have developed an automated approach to identifying fall risk using low-cost, accessible technology. Trunk movement patterns were collected from 181 older people, with and without a history of falls, during the execution of the Mini-BESTest. Data were captured using smartphone sensors (an accelerometer, a gyroscope, and a magnetometer) and classified based on fall history using deep learning algorithms (LSTM). The classification model achieved an overall accuracy of 88.55% a precision of 90.14%, a recall of 87.93%, and an F1 score of 89.02% by combining all signals from the Mini-BESTest tasks. The performance outperformed the metrics we obtained from individual tasks, demonstrating that aggregating all cues provides a more complete and robust assessment of fall risk in older adults. The results suggest that combining signals from multiple tasks allowed the model to better capture the complexities of postural control and dynamic gait, leading to better prediction of falls. This highlights the potential of integrating multiple assessment modalities for more effective fall risk monitoring. Full article
(This article belongs to the Special Issue Falls: Risk, Prevention and Rehabilitation (2nd Edition))
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