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Fall Detection Based on Wearable Sensors
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Fall Detection Based on Wearable Sensors

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

Deadline for manuscript submissions: 25 December 2025 | Viewed by 5814

Special Issue Editor

Dr. Plinio Moreno

E-Mail Website
Guest Editor
1. Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
2. Institute for Systems and Robotics, LARSyS, Torre Norte Piso 7, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
Interests: wearables; fall detection; data augmentation; robot; 3D Vision; tactile sensing

Special Issue Information

Dear Colleagues,

Fall detection in the senior population has the potential of reducing healthcare costs, as well as improving the impact on the lives of every senior person. However, data on the senior population during falls are scarce or not possible to gather, so current models for fall detection are not accurate for their application. This Special Issue aims to gather ideas and methods that generate accurate values for wearable sensors, as well as other aspects of fall detection. These accurate samples should be as close as possible to wearable device data. Thus, this Special Issue calls for works that (i) generate plausible data of senior people falling from simulations, (ii) generate samples that follow the trajectory patterns of the senior population, and (iii) create mappings that generate data of senior falls from actual data of healthy participants (either young or senior). This topic fits into the scope of Sensors due to the sensor modeling and data analysis. The topics of interest are as follows:

  • Data generation from simulation;
  • Transfer learning;
  • Data augmentation from fall videos;
  • Motion capture;
  • Fall detection.

Dr. Plinio Moreno
Guest Editor

Manuscript Submission Information

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Keywords

  • data generation from simulation
  • transfer learning
  • data augmentation from fall videos
  • motion capture
  • fall detection

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Published Papers (6 papers)

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Research

Jump to: Review

10 pages, 223 KiB  
Article
Gait Metrics in Elderly Fallers and Non-Fallers with Varying Levels of Glaucoma: A Longitudinal Prospective Cohort Study
by Louay Almidani, José G. Vargas, Zhuochen Yuan, Seema Banerjee, Xindi Chen, Mariah Diaz, Rhonda Miller, Aleksandra Mihailovic and Pradeep Y. Ramulu
Sensors 2025, 25(12), 3712; https://doi.org/10.3390/s25123712 - 13 Jun 2025
Viewed by 330
Abstract
To understand the impact of falls on gait in those with poor sight, we examined how gait changed after falls in older adults with varying degrees of visual impairment from glaucoma. Participants were classified as fallers or non-fallers based on prospective falls data [...] Read more.
To understand the impact of falls on gait in those with poor sight, we examined how gait changed after falls in older adults with varying degrees of visual impairment from glaucoma. Participants were classified as fallers or non-fallers based on prospective falls data from the first study year. Injurious fallers were those who suffered injuries from falls. The GAITRite Electronic Walkway characterized gait at baseline and three annual follow-ups. Parameters examined included stride length, variability in stride length (CV), stride velocity, stride velocity CV, base of support, base of support CV, and cadence. Longitudinal gait changes were assessed using generalized estimating equation models. Stride length significantly decreased in both fallers (β = −0.09 z-score unit/year) and non-fallers (β = −0.08 z-score unit/year), stride velocity slowed only among fallers (β = −0.08 z-score unit/year), and, in contrast, stride velocity CV decreased only among non-fallers (β = −0.07 z-score unit/year). No longitudinal differences were noted between groups. Additionally, no significant differences in gait metrics were observed between non-fallers, one-time fallers, and multiple fallers, nor between those with and without an injurious fall. Amongst older adults, and enriched for those with visual impairment, fallers and non-fallers adopted a more cautious gait over time, with similar gait changes across groups. Our results suggest that, in visual impairment, many falls may not lead to significant changes in gait. Full article
(This article belongs to the Special Issue Fall Detection Based on Wearable Sensors)
34 pages, 5724 KiB  
Article
Wearable Fall Detection System with Real-Time Localization and Notification Capabilities
by Chin-Kun Tseng, Shi-Jia Huang and Lih-Jen Kau
Sensors 2025, 25(12), 3632; https://doi.org/10.3390/s25123632 - 10 Jun 2025
Viewed by 701
Abstract
Despite significant progress in fall detection systems, many of the proposed algorithms remain difficult to implement in real-world applications. A common limitation is the lack of location awareness, especially in outdoor scenarios where accurately determining the fall location is crucial for a timely [...] Read more.
Despite significant progress in fall detection systems, many of the proposed algorithms remain difficult to implement in real-world applications. A common limitation is the lack of location awareness, especially in outdoor scenarios where accurately determining the fall location is crucial for a timely emergency response. Moreover, the complexity of many existing algorithms poses a challenge for deployment on edge devices, such as wearable systems, which are constrained by limited computational resources and battery life. As a result, these solutions are often impractical for long-term, continuous use in practical settings. To address the aforementioned issues, we developed a portable, wearable device that integrates a microcontroller (MCU), an inertial sensor, and a chip module featuring Global Positioning System (GPS) and Narrowband Internet of Things (NB-IoT) technologies. A low-complexity algorithm based on a finite-state machine was employed to detect fall events, enabling the module to meet the requirements for long-term outdoor use. The proposed algorithm is capable of filtering out eight types of daily activities—running, walking, sitting, ascending stairs, descending stairs, stepping, jumping, and rapid sitting—while detecting four types of falls: forward, backward, left, and right. In case a fall event is detected, the device immediately transmits a fall alert and GPS coordinates to a designated server via NB-IoT. The server then forwards the alert to a specified communication application. Experimental tests demonstrated the system’s effectiveness in outdoor environments. A total of 6750 samples were collected from fifteen test participants, including 6000 daily activity samples and 750 fall events. The system achieved an average sensitivity of 97.9%, an average specificity of 99.9%, and an overall accuracy of 99.7%. The implementation of this system provides enhanced safety assurance for elderly individuals during outdoor activities. Full article
(This article belongs to the Special Issue Fall Detection Based on Wearable Sensors)
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10 pages, 373 KiB  
Article
Detection of Falls and Frailty in Older Adults with Oldfry: Associated Risk Factors
by Eva Martí-Marco, Enrique J. Vera-Remartínez, Aurora Esteve-Clavero, Irene Carmona-Fortuño, Martín Flores-Saldaña, Jorge Vila-Pascual, Malena Barba-Muñoz and María Pilar Molés-Julio
Sensors 2025, 25(10), 2964; https://doi.org/10.3390/s25102964 - 8 May 2025
Viewed by 527
Abstract
Objective: To describe the characteristics and outcomes of using the Oldfry technology application in older adults, analyzing changes in frailty and fall risk after its implementation. Design and Methods: Observational, analytical, prospective, cross-sectional, and multicenter study conducted in residential centers in Plana Baja [...] Read more.
Objective: To describe the characteristics and outcomes of using the Oldfry technology application in older adults, analyzing changes in frailty and fall risk after its implementation. Design and Methods: Observational, analytical, prospective, cross-sectional, and multicenter study conducted in residential centers in Plana Baja (Castellón, Spain). A total of 156 older adults over 65 years old participated, selected based on specific criteria and voluntary consent. Sociodemographic, anthropometric, and clinical variables were collected, including fall history, sensory problems, medication use, and standardized cognitive, nutritional, and functional assessment scales. The study was approved by the Ethics Committee of Universitat Jaume I. Results: The sample included 156 individuals (median age: 84 years). Women showed greater functional dependence (Barthel scale) and cognitive impairment (Pfeiffer scale). The Oldfry device detected frailty with statistically significant differences. A direct relationship was found between greater functional dependence and higher fall risk, as well as between higher comorbidity and increased fall risk. An adequate nutritional status was associated with a lower fall risk. Conclusion: The use of Oldfry is crucial for assessing frailty and fall risk in older adults. Factors such as functionality, comorbidities, and nutritional status directly influence fall prevention, highlighting the importance of technological tools in monitoring these risks. Full article
(This article belongs to the Special Issue Fall Detection Based on Wearable Sensors)
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14 pages, 962 KiB  
Article
Efficacy of a Waist-Mounted Sensor in Predicting Prospective Falls Among Older People Residing in Community Dwellings: A Prospective Cohort Study
by Ka-Ming Lai and Kenneth N. K. Fong
Sensors 2025, 25(8), 2516; https://doi.org/10.3390/s25082516 - 16 Apr 2025
Viewed by 646
Abstract
Falls pose a significant health risk for older people, necessitating accurate predictive tools for fall prevention. This study evaluated the sensitivity of a wearable waist-belt sensor, the Booguu Aspire system, in predicting prospective fall incidents among 37 community-dwelling older people in Hong Kong. [...] Read more.
Falls pose a significant health risk for older people, necessitating accurate predictive tools for fall prevention. This study evaluated the sensitivity of a wearable waist-belt sensor, the Booguu Aspire system, in predicting prospective fall incidents among 37 community-dwelling older people in Hong Kong. A prospective cohort design was employed, involving two analytical groups: the overall cohort and a subset with cognitive performance data available, measured using the Montreal Cognitive Assessment (MoCA). Participants were categorized into moderate- or high-risk groups for falls using the sensor and further assessed with physical function tests, including the Single Leg Stand Test (SLST), 6 Meter Walk Test (6MWT), and Five Times Sit to Stand Test (5STS). Fall incidents were monitored for 12 months through quarterly follow-up phone calls. Statistical analyses showed no significant differences in physical performance between high- and moderate-risk groups and no significant correlations between sensor-based fall risk ratings and physical function test outcomes. The SLST, 6MWT, 5STS, and MoCA tests classified sensor-determined fall risk ratings with accuracies of 51.4%, 64.9%, 59.5%, and 50%. The sensor showed low sensitivity, with 13.51% true positives for fallers and a 20% sensitivity for high-risk individuals. ROC analysis yielded an Area Under the Curve of 0.688. Our findings indicate that the wearable waist-belt Sensor System may not be a sensitive tool in predicting prospective fall incidents. The algorithm for fall risk classification in the wearable sensor merits further exploration. Full article
(This article belongs to the Special Issue Fall Detection Based on Wearable Sensors)
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18 pages, 4830 KiB  
Article
Performance Analysis of Data Augmentation Approaches for Improving Wrist-Based Fall Detection System
by Yu-Chen Tu, Che-Yu Lin, Chien-Pin Liu and Chia-Tai Chan
Sensors 2025, 25(7), 2168; https://doi.org/10.3390/s25072168 - 29 Mar 2025
Viewed by 655
Abstract
The aging of society is a global concern nowadays. Falls and fall-related injuries can influence the elderly’s daily living, including physical damage, psychological effects, and financial problems. A reliable fall detection system can trigger an alert immediately when a fall event happens to [...] Read more.
The aging of society is a global concern nowadays. Falls and fall-related injuries can influence the elderly’s daily living, including physical damage, psychological effects, and financial problems. A reliable fall detection system can trigger an alert immediately when a fall event happens to reduce the adverse effects of falls. Notably, the wrist-based fall detection system provides the most acceptable placement for the elderly; however, the performance is the worst due to the complicated hand movement modeling. Many works recently implemented deep learning technology on wrist-based fall detection systems to address the worst, but class imbalance and data scarcity issues occur. In this study, we analyze different data augmentation methodologies to enhance the performance of wrist-based fall detection systems using deep learning technology. Based on the results, the conditional diffusion model is an ideal data augmentation approach, which improves the F1 score by 6.58% when trained with only 25% of the actual data, and the synthetic data maintains a high quality. Full article
(This article belongs to the Special Issue Fall Detection Based on Wearable Sensors)
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Review

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19 pages, 11014 KiB  
Review
A Decade of Progress in Wearable Sensors for Fall Detection (2015–2024): A Network-Based Visualization Review
by Yifei Li, Pei Liu, Yan Fang, Xiangyuan Wu, Yewei Xie, Zhongzhi Xu, Hao Ren and Fengshi Jing
Sensors 2025, 25(7), 2205; https://doi.org/10.3390/s25072205 - 31 Mar 2025
Cited by 1 | Viewed by 2413
Abstract
Over the past decade, wearable sensors for fall detection have gained significant attention due to their potential in improving the safety of elderly users and reducing fall-related injuries. This review employs a network-based visualization approach to analyze research trends, key technologies, and collaborative [...] Read more.
Over the past decade, wearable sensors for fall detection have gained significant attention due to their potential in improving the safety of elderly users and reducing fall-related injuries. This review employs a network-based visualization approach to analyze research trends, key technologies, and collaborative networks. Using studies from SCI- and SSCI-indexed journals from 2015 to 2024, we analyzed 582 articles and 65 reviews with CiteSpace, revealing a significant rise in research on wearable sensors for fall detection. Additionally, we reviewed various datasets and machine learning techniques, from traditional methods to advanced deep learning frameworks, which demonstrate high accuracies, F1 scores, sensitivities, and specificities in controlled settings. This review provides a comprehensive overview of the progress and emerging trends, offering a foundation for future advancements in wearable fall detection systems. Full article
(This article belongs to the Special Issue Fall Detection Based on Wearable Sensors)
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