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Sensor Technology for Fall Prevention
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Sensor Technology for Fall Prevention

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

Deadline for manuscript submissions: closed (20 October 2021) | Viewed by 19706

Special Issue Editors

Prof. Dr. Maria Linden

E-Mail Website
Guest Editor
Division of Intelligent Future Technologies, Mälardalen University, 721 23 Västerås, Sweden
Interests: health technology; biomedical engineering; signal processing; wearable body sensors; e-health and m-health; biomedical sensor systems; non-invasive sensor systems; motion analysis; fall detection; fall prevention; blood flow measurements; end-user compliance; user acceptance
Special Issues, Collections and Topics in MDPI journals
Dr. Annica Kristoffersson

E-Mail
Guest Editor
Malardalen University, School of Innovation, Design and Engineering, Embedded Sensor Systems for Health, Högskoleplan 1, 721 23 Västerås, Sweden
Interests: health technology; biomedical engineering; wearable body sensors; e-health and m-health; smart homes; biomedical sensor systems; non-invasive sensor systems; motion analysis; fall detection; fall prevention; health trend monitoring; ensuring safe and secure independent living; end-user compliance; user acceptance; quality of interaction; human–robot interaction; human–computer interaction; human–machine interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Marina Arkkukangas

E-Mail Website
Guest Editor
1. Department of Medicine, Sport and Fitness Science, School of Education, Health and Social Studies, Dalarna University, 791 88 Falun, Sweden
2. Department of Physiotherapy, School of Health, Care and Social Welfare, Mälardalen University, 721 23 Vasteras, Sweden
3. Research and Development in Sörmland, Region Sörmland, 632 17 Eskilstuna, Sweden
Interests: behavioural medicine; behaviour change techniques; fall prevention exercise and falling techniques for middle- and older-age adults; clinical trials; medical technology and m-health.active and healthy aging; physical activity and exercise; welfare technology

Special Issue Information

Dear Colleagues,

Falls and injuries related to falls are common health problems among older people. There are also almost 40 million non-fatal falls that result in the need for medical attention each year. The financial costs of falls in an acute phase are substantial without accounting for the time needed to recover after a fall injury. Hence, preventing falls is important, not only from an economic perspective but also from the perspective of individual fallers. Falls are commonly associated with decreased participation in social and physical activities and quality of life. Therefore, preventing falls is much more complex than detecting falls. In order to prevent falls, developing means for predicting the occurrence of falls is of the utmost importance. Wearable sensors allow for the monitoring of postures, physical activity levels, and vital signs and the detection of falls.

This Special Issue seeks to explore opportunities and challenges regarding the use of sensors or other technologies for predicting and/or preventing falls. Prospective authors are cordially invited to submit their original contributions related to various aspects of the use of sensor technology for fall prevention. We especially welcome clinical trials, studies that adopt a participatory research design, and systematic reviews. Topics of interest include, but are not limited to:

  * technological methods for risk/fall prediction;
  * wireless sensors and networks;
  * wearable sensors;
  * sensor-based feedback on balance/sway to patients and/or care providers;
  * sensor-based detection of near-falls;
  * reliability and validity of risk/fall predictions;
  * patients’ and care providers’ perspectives on sensor technology;
  * cost-effectiveness of technologies for preventing falls; and
  * the social impact of technologies for preventing falls.

Prof. Dr. Maria Linden
Dr. Annica Kristoffersson
Dr. Marina Arkkukangas
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.

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Published Papers (6 papers)

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Research

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9 pages, 2597 KiB  
Article
A Pilot Study Quantifying Center of Mass Trajectory during Dynamic Balance Tasks Using an HTC Vive Tracker Fixed to the Pelvis
by Susanne M. van der Veen and James S. Thomas
Sensors 2021, 21(23), 8034; https://doi.org/10.3390/s21238034 - 1 Dec 2021
Cited by 6 | Viewed by 2390
Abstract
Fall rates are increasing among the aging population and even higher falls rates have been reported in populations with neurological impairments. The Berg Balance Scale is often used to assess balance in older adults and has been validated for use in people with [...] Read more.
Fall rates are increasing among the aging population and even higher falls rates have been reported in populations with neurological impairments. The Berg Balance Scale is often used to assess balance in older adults and has been validated for use in people with stroke, traumatic brain injury, and Parkinson’s disease. While the Berg Balance Scale (BBS) has been found to be predictive of the length of rehabilitation stay following stroke, a recent review concluded the BBS lacked predictive validity for fall risk. Conversely, sophisticated measures assessing center of mass (COM) displacement have shown to be predictive of falls risk. However, calculating COM displacement is difficult to measure outside a laboratory. Accordingly, we sought to validate COM displacement measurements derived from an HTC Vive tracker secured to the pelvis by comparing it to COM derived from ‘gold’ standard laboratory-based full-body motion capture. Results showed that RMS between the COM calculated from HTC Vive tracker and full body motion capture agree with an average error rate of 2.1 ± 2.6 cm. Therefore, we conclude measurement of COM displacement using an HTC Vive tracker placed on the pelvis is reasonably representative of laboratory-based measurement of COM displacement. Full article
(This article belongs to the Special Issue Sensor Technology for Fall Prevention)
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7 pages, 234 KiB  
Article
Kinematics or Kinetics: Optimum Measurement of the Vertical Variations of the Center of Mass during Gait Initiation
by Antoine Langeard, Charlotte Mathon, Mourad Ould-Slimane, Leslie Decker, Nicolas Bessot, Antoine Gauthier and Nathalie Chastan
Sensors 2021, 21(23), 7954; https://doi.org/10.3390/s21237954 - 29 Nov 2021
Cited by 2 | Viewed by 2162
Abstract
Background: During gait, the braking index represents postural control, and consequently, the risk of falls. Previous studies based their determination of the braking index during the first step on kinetic methods using force platforms, which are highly variable. This study aimed to investigate [...] Read more.
Background: During gait, the braking index represents postural control, and consequently, the risk of falls. Previous studies based their determination of the braking index during the first step on kinetic methods using force platforms, which are highly variable. This study aimed to investigate whether determining the braking index with a kinematic method, through 3D motion capture, provides more precise results. Methods: Fifty participants (20 to 40 years) performed ten trials in natural and fast gait conditions. Their braking index was estimated from their first step simultaneously using a force platform and VICON motion capture system. The reliability of each braking index acquisition method was assessed by intraclass correlation coefficients, standard error measurements, and the minimal detectable change. Results: Both kinetic and kinematic methods allowed good to excellent reliability and similar minimum detectable changes (10%). Conclusion: Estimating the braking index through a kinetic or a kinematic method was highly reliable. Full article
(This article belongs to the Special Issue Sensor Technology for Fall Prevention)
7 pages, 657 KiB  
Communication
An Assessment of Balance through Posturography in Healthy about Women: An Observational Study
by Elena Escamilla-Martínez, Ana Gómez-Maldonado, Beatriz Gómez-Martín, Aurora Castro-Méndez, Juan Antonio Díaz-Mancha and Lourdes María Fernández-Seguín
Sensors 2021, 21(22), 7684; https://doi.org/10.3390/s21227684 - 19 Nov 2021
Cited by 2 | Viewed by 1950
Abstract
The incidence of falls in adults constitutes a public health problem, and the alteration in balance is the most important factor. It is necessary to evaluate this through objective tools in order to quantify alterations and prevent falls. This study aims to determine [...] Read more.
The incidence of falls in adults constitutes a public health problem, and the alteration in balance is the most important factor. It is necessary to evaluate this through objective tools in order to quantify alterations and prevent falls. This study aims to determine the existence of alteration of balance and the influence of age in a population of healthy women. Static posturography was performed on 49 healthy adult women with no history of falls in four different situations using the Romberg test with the NedSVE/IBV® platform. The variables studied were the body sway area and the anteroposterior and mediolateral displacements. The situation of maximum instability occurred in RGC (p = 0.001), with a significant increase in anteroposterior oscillations regarding the ML (p < 0.001), with no correlation to age. Age alone does not influence the balance in the sample studied, other factors must come together to alter it. The joint cancellation of visual and somatosensory afferents could facilitate the appearance of falls, given that it is a situation of maximum instability. Proprioceptive training is interesting as a preventive strategy for falls. Full article
(This article belongs to the Special Issue Sensor Technology for Fall Prevention)
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10 pages, 4721 KiB  
Communication
Assessment of Selected Spatio-Temporal Gait Parameters on Subjects with Pronated Foot Posture on the Basis of Measurements Using OptoGait. A Case-Control Study
by Inmaculada Requelo-Rodríguez, Aurora Castro-Méndez, Ana María Jiménez-Cebrián, María Luisa González-Elena, Inmaculada C. Palomo-Toucedo and Manuel Pabón-Carrasco
Sensors 2021, 21(8), 2805; https://doi.org/10.3390/s21082805 - 16 Apr 2021
Cited by 4 | Viewed by 3302
Abstract
Walking is part of daily life and in asymptomatic subjects it is relatively easy. The physiology of walking is complex and when this complex control system fails, the risk of falls increases. As a result, gait disorders have a major impact on the [...] Read more.
Walking is part of daily life and in asymptomatic subjects it is relatively easy. The physiology of walking is complex and when this complex control system fails, the risk of falls increases. As a result, gait disorders have a major impact on the older adult population and have increased in frequency as a result of population aging. Therefore, the OptoGait sensor is intended to identify gait imbalances in pronating feet to try to prevent falling and injury by compensating for it with treatments that normalize such alteration. This study is intended to assess whether spatiotemporal alterations occur in the gait cycle in a young pronating population (cases) compared to a control group (non-pronating patients) analyzed with OptoGait. Method: a total of n = 142 participants consisting of n = 70 cases (pronators) and n = 72 healthy controls were studied by means of a 30 s treadmill program with a system of 96 OptoGait LED sensors. Results: Significant differences were found between the two groups and both feet in stride length and stride time, gait cycle duration and gait cadence (in all cases p < 0.05). Conclusions: pronating foot posture alters normal gait patterns measured by OptoGait; this finding presents imbalance in gait as an underlying factor. Prevention of this alteration could be considered in relation to its relationship to the risk of falling in future investigations. Full article
(This article belongs to the Special Issue Sensor Technology for Fall Prevention)
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26 pages, 2947 KiB  
Article
NT-FDS—A Noise Tolerant Fall Detection System Using Deep Learning on Wearable Devices
by Marvi Waheed, Hammad Afzal and Khawir Mehmood
Sensors 2021, 21(6), 2006; https://doi.org/10.3390/s21062006 - 12 Mar 2021
Cited by 41 | Viewed by 4650
Abstract
Given the high prevalence and detrimental effects of unintentional falls in the elderly, fall detection has become a pertinent public concern. A Fall Detection System (FDS) gathers information from sensors to distinguish falls from routine activities in order to provide immediate medical assistance. [...] Read more.
Given the high prevalence and detrimental effects of unintentional falls in the elderly, fall detection has become a pertinent public concern. A Fall Detection System (FDS) gathers information from sensors to distinguish falls from routine activities in order to provide immediate medical assistance. Hence, the integrity of collected data becomes imperative. Presence of missing values in data, caused by unreliable data delivery, lossy sensors, local interference and synchronization disturbances and so forth, greatly hamper the credibility and usefulness of data making it unfit for reliable fall detection. This paper presents a noise tolerant FDS performing in presence of missing values in data. The work focuses on Deep Learning (DL) particularly Recurrent Neural Networks (RNNs) with an underlying Bidirectional Long Short-Term Memory (BiLSTM) stack to implement FDS based on wearable sensors. The proposed technique is evaluated on two publicly available datasets—SisFall and UP-Fall Detection. Our system produces an accuracy of 97.21% and 97.41%, sensitivity of 96.97% and 99.77% and specificity of 93.18% and 91.45% on SisFall and UP-Fall Detection respectively, thus outperforming the existing state of the art on these benchmark datasets. The resultant outcomes suggest that the ability of BiLSTM to retain long term dependencies from past and future make it an appropriate model choice to handle missing values for wearable fall detection systems. Full article
(This article belongs to the Special Issue Sensor Technology for Fall Prevention)
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Review

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46 pages, 3784 KiB  
Review
Performance and Characteristics of Wearable Sensor Systems Discriminating and Classifying Older Adults According to Fall Risk: A Systematic Review
by Annica Kristoffersson, Jiaying Du and Maria Ehn
Sensors 2021, 21(17), 5863; https://doi.org/10.3390/s21175863 - 31 Aug 2021
Cited by 12 | Viewed by 3555
Abstract
Sensor-based fall risk assessment (SFRA) utilizes wearable sensors for monitoring individuals’ motions in fall risk assessment tasks. Previous SFRA reviews recommend methodological improvements to better support the use of SFRA in clinical practice. This systematic review aimed to investigate the existing evidence of [...] Read more.
Sensor-based fall risk assessment (SFRA) utilizes wearable sensors for monitoring individuals’ motions in fall risk assessment tasks. Previous SFRA reviews recommend methodological improvements to better support the use of SFRA in clinical practice. This systematic review aimed to investigate the existing evidence of SFRA (discriminative capability, classification performance) and methodological factors (study design, samples, sensor features, and model validation) contributing to the risk of bias. The review was conducted according to recommended guidelines and 33 of 389 screened records were eligible for inclusion. Evidence of SFRA was identified: several sensor features and three classification models differed significantly between groups with different fall risk (mostly fallers/non-fallers). Moreover, classification performance corresponding the AUCs of at least 0.74 and/or accuracies of at least 84% were obtained from sensor features in six studies and from classification models in seven studies. Specificity was at least as high as sensitivity among studies reporting both values. Insufficient use of prospective design, small sample size, low in-sample inclusion of participants with elevated fall risk, high amounts and low degree of consensus in used features, and limited use of recommended model validation methods were identified in the included studies. Hence, future SFRA research should further reduce risk of bias by continuously improving methodology. Full article
(This article belongs to the Special Issue Sensor Technology for Fall Prevention)
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