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Sensors and Technologies for Fall Risk Awareness

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 August 2022) | Viewed by 25674

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Special Issue Editor

Dr. Cristina P. Santos

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Guest Editor

Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4710-057 Braga, Portugal
Interests: human motion; human locomotion; human–robot interactions and collaboration; medical devices; neuro-rehabilitation of patients suffering from motor problems by means of bio-inspired robotics and neuroscience technologies
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Special Issue Information

Dear Colleagues,

Each year, 37 million seniors experience at least one fall with resultant fall-related motor injuries that decrease quality of life (QoL). Fallers develop fear of falling with consequent depression and restricted autonomy and social and physical activity, which increases fall risk in the long-term. Resulting social and health costs are a public burden with a high socioeconomic impact that may reach 4% of European healthcare expenditures. The aging of the world’s population has place the focus on age-related health issues. There is evidence that assistive fall prevention technologies can reduce potential falls, fall rate, and fall-related injuries among seniors, promoting healthy aging.

Over the last few years, several healthcare technologies have been proposed to detect falls, estimate the risk of falls, and predict and/or prevent falls, including wearable devices, body-sensor networks, environmental sensors, their combination, artificial intelligence algorithms for fall risk assessment and prediction, and robotic devices and associated algorithms for fall prevention.

This Special Issue addresses cutting-edge technologies designed to support healthcare interventions to detect, estimate the risk of falls, predict, and/or prevent falls, or to reduce their consequences. The Special Issue welcomes submissions describing the application, technologies, and/or validation of innovative approaches, in areas including:

Sensor-based feedback on balance/sway to patients and/or care providers;
Body-sensor networks
Sensor-based detection of falls and fall risk assessment;
Technological methods for risk/fall prediction;
Reliability and validity of risk/fall predictions;
Cost effectiveness of technologies for preventing falls
Fall prevention

Dr. Cristina P. Santos
Guest Editor

Manuscript Submission Information

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Keywords

fall risk assessment
fall detection
fall prediction
fall prevention
technologies developed for implementation
wearable sensors
biosensors
body-sensor networks
environmental sensors

Published Papers (4 papers)

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Research

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20 pages, 1064 KiB

Open AccessArticle

Inertial Data-Based AI Approaches for ADL and Fall Recognition

by Luís M. Martins, Nuno Ferrete Ribeiro, Filipa Soares and Cristina P. Santos

Sensors 2022, 22(11), 4028; https://doi.org/10.3390/s22114028 - 26 May 2022

Cited by 6 | Viewed by 2106

Abstract

The recognition of Activities of Daily Living (ADL) has been a widely debated topic, with applications in a vast range of fields. ADL recognition can be accomplished by processing data from wearable sensors, specially located at the lower trunk, which appears to be a suitable option in uncontrolled environments. Several authors have addressed ADL recognition using Artificial Intelligence (AI)-based algorithms, obtaining encouraging results. However, the number of ADL recognized by these algorithms is still limited, rarely focusing on transitional activities, and without addressing falls. Furthermore, the small amount of data used and the lack of information regarding validation processes are other drawbacks found in the literature. To overcome these drawbacks, a total of nine public and private datasets were merged in order to gather a large amount of data to improve the robustness of several ADL recognition algorithms. Furthermore, an AI-based framework was developed in this manuscript to perform a comparative analysis of several ADL Machine Learning (ML)-based classifiers. Feature selection algorithms were used to extract only the relevant features from the dataset’s lower trunk inertial data. For the recognition of 20 different ADL and falls, results have shown that the best performance was obtained with the K-NN classifier with the first 85 features ranked by Relief-F (98.22% accuracy). However, Ensemble Learning classifier with the first 65 features ranked by Principal Component Analysis (PCA) presented 96.53% overall accuracy while maintaining a lower classification time per window (0.039 ms), showing a higher potential for its usage in real-time scenarios in the future. Deep Learning algorithms were also tested. Despite its outcomes not being as good as in the prior procedure, their potential was also demonstrated (overall accuracy of 92.55% for Bidirectional Long Short-Term Memory (LSTM) Neural Network), indicating that they could be a valid option in the future. Full article

(This article belongs to the Special Issue Sensors and Technologies for Fall Risk Awareness)

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Review

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31 pages, 758 KiB

Open AccessReview

Provoking Artificial Slips and Trips towards Perturbation-Based Balance Training: A Narrative Review

by Rafael N. Ferreira, Nuno Ferrete Ribeiro, Joana Figueiredo and Cristina P. Santos

Sensors 2022, 22(23), 9254; https://doi.org/10.3390/s22239254 - 28 Nov 2022

Cited by 5 | Viewed by 2460

Abstract

Humans’ balance recovery responses to gait perturbations are negatively impacted with ageing. Slip and trip events, the main causes preceding falls during walking, are likely to produce severe injuries in older adults. While traditional exercise-based interventions produce inconsistent results in reducing patients’ fall rates, perturbation-based balance training (PBT) emerges as a promising task-specific solution towards fall prevention. PBT improves patients’ reactive stability and fall-resisting skills through the delivery of unexpected balance perturbations. The adopted perturbation conditions play an important role towards PBT’s effectiveness and the acquisition of meaningful sensor data for studying human biomechanical reactions to loss of balance (LOB) events. Hence, this narrative review aims to survey the different methods employed in the scientific literature to provoke artificial slips and trips in healthy adults during treadmill and overground walking. For each type of perturbation, a comprehensive analysis was conducted to identify trends regarding the most adopted perturbation methods, gait phase perturbed, gait speed, perturbed leg, and sensor systems used for data collection. The reliable application of artificial perturbations to mimic real-life LOB events may reduce the gap between laboratory and real-life falls and potentially lead to fall-rate reduction among the elderly community. Full article

(This article belongs to the Special Issue Sensors and Technologies for Fall Risk Awareness)

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22 pages, 577 KiB

Open AccessReview

Fall Risk Assessment Using Wearable Sensors: A Narrative Review

by Rafael N. Ferreira, Nuno Ferrete Ribeiro and Cristina P. Santos

Sensors 2022, 22(3), 984; https://doi.org/10.3390/s22030984 - 27 Jan 2022

Cited by 20 | Viewed by 6572

Abstract

Recently, fall risk assessment has been a main focus in fall-related research. Wearable sensors have been used to increase the objectivity of this assessment, building on the traditional use of oversimplified questionnaires. However, it is necessary to define standard procedures that will us enable to acknowledge the multifactorial causes behind fall events while tackling the heterogeneity of the currently developed systems. Thus, it is necessary to identify the different specifications and demands of each fall risk assessment method. Hence, this manuscript provides a narrative review on the fall risk assessment methods performed in the scientific literature using wearable sensors. For each identified method, a comprehensive analysis has been carried out in order to find trends regarding the most used sensors and its characteristics, activities performed in the experimental protocol, and algorithms used to classify the fall risk. We also verified how studies performed the validation process of the developed fall risk assessment systems. The identification of trends for each fall risk assessment method would help researchers in the design of standard innovative solutions and enhance the reliability of this assessment towards a homogeneous benchmark solution. Full article

(This article belongs to the Special Issue Sensors and Technologies for Fall Risk Awareness)

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23 pages, 903 KiB

Open AccessReview

Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

by Sara Usmani, Abdul Saboor, Muhammad Haris, Muneeb A. Khan and Heemin Park

Sensors 2021, 21(15), 5134; https://doi.org/10.3390/s21155134 - 29 Jul 2021

Cited by 77 | Viewed by 13567

Abstract

Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues. Full article

(This article belongs to the Special Issue Sensors and Technologies for Fall Risk Awareness)

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