Abstract
Background:
Falls pose a significant public health challenge for the elderly, impacting morbidity, mortality, and independence. Traditional assessment methods often lack precision and practicality, necessitating the development of innovative solutions. Wearable sensors, utilizing accelerometers, gyroscopes, and machine learning algorithms, have emerged as transformative tools for real-time fall-risk monitoring.
Objectives:
This study aimed to explore the research landscape of wearable sensors in fall-risk assessment through bibliometric analysis, identifying key trends, technological breakthroughs, and contributors that have shaped advancements in the field over the past 2 decades.
Methods:
A systematic search of the Scopus database was conducted, analyzing scholarly outputs from 2000 to 2024. Using targeted keywords, 221 peer-reviewed studies were identified and aggregated into a dataset. Analytical tools like VOSviewer and Publish or Perish were utilized to visualize research networks, intellectual contributions, and citation metrics, offering insights into the field’s evolution.
Results:
Research activity has surged since 2013, highlighting the growing importance of wearable technologies. The United States leads this domain, with significant contributions from Europe and Asia. Key thematic areas include medicine, computer science, and engineering, with keywords such as “balance,” “gait,” and “fall risk” predominating. Advances in machine learning and sensor technology have enhanced predictive accuracy and usability.
Conclusion:
Wearable sensors are revolutionizing fall-risk assessment, offering precision, portability, and practicality. Addressing usability, affordability, and standardization will be critical for equitable access, and its promise lies not only in preventing falls but in empowering the elderly with confidence and improving their quality of life.
Keywords: balance, elderly, falls, quality of life, wearable sensors
1. Introduction
Falls are a significant global health concern, especially among older adults, contributing to serious physical, mental, and social consequences. As the second leading cause of accidental deaths, falls often result from tripping or collisions, leading to severe injuries such as head or torso trauma.[1] Beyond physical harm, falls instill fear, limiting outdoor activities and reducing quality of life.[2] Studies show that around 40% of older adults experience difficulties in daily activities, linking declining physical and mental health to increased fall risks, fractures, cognitive decline, and mortality.[3,4] These incidents often signal underlying health issues requiring intervention.[5]
To mitigate fall risks, fall detection systems have gained prominence, enabling timely intervention and injury prevention.[6] Wearable sensors, such as accelerometers and gyroscopes, provide real-time monitoring of movement patterns, body posture, gait, and balance, helping identify abnormalities linked to fall risks.[7] Postural sway, a key balance impairment indicator, plays a crucial role in assessing instability.[8] Furthermore, gait analysis is essential in identifying fall risks, particularly in conditions like Parkinson disease.[9]
Fall detection technologies fall into 3 categories: optical sensors (Kinect, infrared cameras) provide whole-body motion feedback; perception sensors (Wii balance boards, force platforms) offer force biofeedback; and wearable sensors (accelerometers, gyroscopes) track partial body movements.[10–12] Advances in wearable sensor technology, combined with deep learning, enhance real-time monitoring and fall prediction using large datasets.[13] Machine learning further refines fall-risk assessment by analyzing gait, balance, and posture, improving predictive accuracy.[14–16]
Inertial measurement unit (IMU) based gait analysis provides precise fall prediction, fostering trust in clinical application.[17] Additionally, accelerometer-based technologies in geriatrics show a 4.73% annual growth in fall detection research since 2008, with gait, balance, and physical activity monitoring being key study areas.[18] Wearable sensors offer a portable, cost-effective alternative to non-wearable systems, which, despite their precision, face limitations like high costs and immobility.[19] However, both methods encounter challenges in data interpretation and scalability, necessitating advanced models to bridge these gaps.[20]
Wearable device-based technology has proven crucial for detecting subtle changes in gait and balance biomarkers associated with fall risk and fall detection systems. Although wearable sensor-based systematic reviews and systematic reviews with meta-analyses done primarily focus on falls and fall-risk detection in elderly populations, no bibliometric and network analysis studies using wearable sensors have been conducted on this topic.[6,12] Conducting a sensor-based bibliometric and network analysis on falls in older adults is vital for providing both clinical and academic insights into this significant issue within the geriatric population, especially with current advancements in wearable sensor technology. Therefore, this study aimed to analyze research in this field from a bibliometric perspective.
1.1. Research questions
R1: Which Institutes, Countries, Journals, and Key Authors are leading the research on wearable sensors for fall detection in the elderly, based on data from Scopus-indexed publications? Additionally, what are the common Author-chosen Keywords, and which papers have the highest citation counts in this field?
R2: What are the recent growth trends and advancements in wearable technology for fall-risk assessment and detection among the elderly population over the past 2 decades?
R3: What are the clinical implications of current trends in the use of wearable sensors for fall-risk assessment in the elderly population, and how are these technologies being integrated into clinical practices?
2. Methodology
2.1. Search strategy
Scopus is a multidisciplinary database that indexes scholarly literature, including journals, conference proceedings, and books, across various fields such as science, medicine, and the social sciences. It is one of the largest databases for abstracts and citations, offering tools to track research trends, analyze citations, and identify key contributors. With its extensive coverage of peer-reviewed sources and user-friendly features, Scopus is a valuable resource for reliable research and bibliometric studies. Figure 1 shows specific keywords were used in Scopus to find articles on falls and sensors in the elderly. As this study is a bibliometric analysis based on published literature, it did not require ethical approval.
Figure 1.
Keywords selection.
2.2. Study selection and criteria
The search targeted the title, keywords, and abstract fields to ensure a comprehensive and precise selection of publications. The study included publications up to October 12, 2024, with no restrictions on the publication year, focusing only on articles written in English to ensure global accessibility. Journal articles and conference papers were prioritized due to their rigorous peer-review process, ensuring reliable findings. An initial search identified 626 publications, which were screened in detail, as shown in Figure 2. Articles not specifically addressing wearable devices, fall-risk assessment, or the older population were excluded, resulting in the removal of 405 papers. This refinement produced a final dataset of 221 publications for bibliometric analysis, aligning with the study’s core objectives.
Figure 2.
Flow diagram of the search strategy.
3. Data analysis
For this bibliometric analysis, Scopus database’s built-in analytical tools provided insights into document analysis which included type, source and language of document, subject area, and yearly contributors.[21] VOSviewer (v1.6.19; Centre for Science and Technology Studies, Leiden, Netherlands) is widely recognized for its capabilities in bibliometric mapping, served as the primary tool to construct visual networks of co-occurrence and collaboration across a comprehensive body of literature. In this study, it was employed to analyze indexed keywords, co-authorship patterns, geographical research distribution, and to identify emerging research trends.[22,23] To complement these, the Publish or Perish reference manager application facilitated detailed exploration and citation analysis of the Scopus data, enabling precise filtering and an in-depth examination of bibliometric attributes and citation metrics.[24]
4. Results
4.1. Document analysis
The dissemination of research in wearable technology for fall prevention reflects a well-established and rapidly evolving field. Among 221 publications, type of journal articles (130) dominated, ensuring scientific rigor, while conference papers (70) drove innovation and collaboration. Together with reviews (21) provided critical insights into emerging trends. The source type analysis revealed that journals are the primary platforms for disseminating research findings, with 155 papers published in journals and 66 studies originating from conference proceedings. Meanwhile, English was identified as the dominant language in this bibliometric analysis, comprising 98.2% of the selected studies, equivalent to 217 papers. As shown in Figure 3, this research thrives at the intersection of Medicine (145, 34.6%), which pioneer’s clinical applications, Computer Science (87, 20.8%), which refines machine learning models for sensor data, and Engineering (84, 20.04%), which enhances device design and efficiency.
Figure 3.
Subject area.
4.2. Evolution of publications and key academic sources
Remarkable growth on wearable technology research presented in Figure 4, has witnessed over the past 2 decades, accelerating from 2013 onward. Publication peaks in 2022 (27 studies) and 2023 (25 studies) reflect groundbreaking advancements in wearable sensors like IMUs, accelerometers, and gyroscopes, coupled with sophisticated machine learning techniques. This surge underscores a global commitment to mitigating fall risks in aging populations through cutting-edge innovation. Leading the field, as illustrated in Table 1, the Journal of Neurology (Cite Score 10 in 2023) and IEEE Transactions on Biomedical Engineering (9.4 in 2023) continue to drive high-impact research, while steadily rising journals like BMC Geriatrics (5.7 in 2023) gain traction. Although lower-ranked sources progress at a slower pace, elite journals remain at the forefront, shaping the future of wearable technology in fall prevention.
Figure 4.
Year of publication.
Table 1.
Most active source title.
| Source | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings | 0 | 0.3 | 0.6 | 1.1 | 1.2 | 1.1 | 1 | 1.6 | 1.5 | 1.5 | 1.8 | 2.1 | 2.2 |
| BMC Geriatrics | 2.2 | 2.4 | 3.4 | 3.5 | 3.3 | 4.1 | 4.1 | 4 | 4.4 | 4.5 | 4.8 | 5.1 | 5.7 |
| Journal of Neuro Engineering and Rehabilitation | 4.9 | 5.8 | 5 | 4.7 | 5.7 | 6.8 | 8.2 | 7 | 6.5 | 6.6 | 7.4 | 8.8 | 9.6 |
| IEEE Transactions on Neural Systems and Rehabilitation Engineering | 6.5 | 6.7 | 7.5 | 6.4 | 6.8 | 7 | 6.7 | 6 | 6.9 | 7.7 | 8.1 | 8.8 | 8.6 |
| IEEE Transactions on Biomedical Engineering | 4.7 | 4.9 | 5.7 | 6.1 | 6.9 | 7.8 | 8.4 | 8.8 | 9.1 | 9.4 | 9.4 | 9.5 | 9.4 |
| International Journal of Environmental Research and Public Health | 2 | 3.2 | 3.4 | 2.9 | 2.7 | 3.1 | 3.2 | 3.1 | 3 | 3.4 | 4.5 | 5.4 | 7.3 |
| Journal of Geriatric Physical Therapy | 2.4 | 2.9 | 2.7 | 2.8 | 2.6 | 3.3 | 3.3 | 3.6 | 3.3 | 5 | 6.1 | 6.4 | 3.7 |
| Journal of Neurology | 5.7 | 5.6 | 5.5 | 5.9 | 6 | 6 | 6 | 6 | 6 | 6.4 | 7.6 | 8.8 | 10 |
| Scientific Reports | 0.2 | 1.1 | 2.4 | 4.2 | 4.2 | 4.2 | 4.8 | 6.4 | 7.2 | 7.1 | 6.9 | 7.5 | 7.5 |
| Gerontechnology | 0.1 | 0.8 | 1.1 | 1.8 | 0.5 | 0.7 | 0.6 | 0.9 | 1 | 0.8 | 0.9 | 1 | 1 |
Maximum up to 10 sources compared.
4.3. Top contributors, citation impact, and research collaboration dynamics
The analysis of leading authors highlights key pioneers shaped the field illustrated in Table 2. B.R. Greene emerges as the most prolific contributor (10 publications), specializing in gait and balance assessment, while B. Najafi and B. Caulfield (8 each) drive advancements in IMU applications and machine learning for fall-risk prediction. Notable researchers like K. Delbaere, M. Mancini, and A. Ejupi have enriched psychological, clinical, and ICT-based approaches to fall prevention. Seminal works as shown in Table 3, author Najafi et al (2002), revolutionized movement analysis using gyroscopes with highest citation, while Howcroft et al (2013) provided a definitive review of IMU-based fall-risk assessments.[25,26] Sun and Sosnoff (2018) explored 4 major sensing technologies, and Montesinos et al (2018) identified key gait parameters for fall prediction.[27,28] Besides, collaboration patterns revealed in Table 4, that most studies involve 4 to 6 authors, reflecting the multidisciplinary synergy required in biomechanics, healthcare, and sensor technology, while smaller teams focus on specialized research and large-scale collaborations drive groundbreaking, multi-institutional advancements.
Table 2.
Most productive authors.
| No | Authors | Total publications | No of citations | Average of total citations |
|---|---|---|---|---|
| 1 | B.R. Greene | 10 | 353 | 35 |
| 2 | B. Caulfield | 8 | 125 | 16 |
| 3 | B. Najafi | 8 | 632 | 79 |
| 4 | K. Delbaere | 6 | 297 | 50 |
| 5 | A. Ejupi | 6 | 297 | 50 |
| 6 | S.R. Lord | 5 | 316 | 63 |
| 7 | M. Mancini | 5 | 388 | 78 |
| 8 | D. McGrath | 5 | 68 | 14 |
| 9 | K. Aminian | 4 | 490 | 122 |
| 10 | J. Annegarn. | 4 | 223 | 56 |
Table 3.
Top 10 highly cited articles.
| No | Cites | Authors | Title | Year | Cites/Year |
|---|---|---|---|---|---|
| 1 | 349 | B. Najafi, K. Aminian, F. Loew, Y. Blanc, P.A. Robert | Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall-risk evaluation in the elderly | 2002 | 15.86 |
| 2 | 278 | J. Howcroft, J. Kofman, E.D. Lemaire | Review of fall-risk assessment in geriatric populations using inertial sensors | 2013 | 25.27 |
| 3 | 199 | B.R. Greene, A. Odonovan, R. Romero-Ortuno, L. Cogan, C.N. Scanaill, R.A. Kenny | Quantitative falls risk assessment using the timed up and go test | 2010 | 14.21 |
| 4 | 184 | F.B. Horak, M. Mancini | Objective biomarkers of balance and gait for Parkinson disease using body-worn sensors | 2013 | 16.73 |
| 5 | 127 | R. Sun, J.J. Sosnoff | Novel sensing technology in fall risk assessment in older adults: a systematic review | 2018 | 21.17 |
| 6 | 119 | W. Zijlstra, K. Aminian | Mobility assessment in older people: new possibilities and challenges | 2007 | 7 |
| 7 | 112 | M. Mancini, H. Schlueter, M. El-Gohary, N. Mattek, C. Duncan, J. Kaye, F.B. Horak | Continuous monitoring of turning mobility and its association to falls and cognitive function: a pilot study | 2016 | 14 |
| 8 | 106 | J.H.J. Allum, M.G. Carpenter | A speedy solution for balance and gait analysis: Angular velocity measured at the center of body mass | 2005 | 5.58 |
| 9 | 102 | R. Schniepp, M. Wuehr, C. Schlick, S. Huth, C. Pradhan, M. Dieterich, T. Brandt, K. Jahn | Increased gait variability is associated with the history of falls in patients with cerebellar ataxia | 2014 | 10.2 |
| 10 | 91 | K. Sato, K. Kuroki, S. Saiki, R. Nagatomi | Improving walking, muscle strength, and balance in the elderly with an exergame using kinect: a randomized controlled trial | 2015 | 10.11 |
Table 4.
Number of author(s) per document.
| Author Count | Total Publications | Percentage |
|---|---|---|
| 2 | 18 | 8.14 |
| 3 | 31 | 14.03 |
| 4 | 33 | 14.93 |
| 5 | 43 | 19.46 |
| 6 | 33 | 14.93 |
| 7 | 20 | 9.05 |
| 8 | 14 | 6.34 |
| 9 | 8 | 3.62 |
| 10 | 6 | 2.72 |
| 11 | 6 | 2.72 |
| 12 | 4 | 1.81 |
| 13 | 1 | 0.45 |
| 14 | 1 | 0.45 |
| 15 | 1 | 0.45 |
| 19 | 1 | 0.45 |
| 27 | 1 | 0.45 |
| Total | 221 | 100 |
4.4. Authors and indexed keywords: trends in fall and preventable technology integration
Figure 5 unveils the dynamic evolution of balance and fall-prevention research, with “Balance” and “Falls” (27 occurrences each) standing as pivotal themes. Key terms like “Fall Risk” (22), “Fall Prevention” (16), “Older Adults” (19), “Gait” (20), “Postural Balance” (11), “Wearable Sensors” (12), “Inertial Sensors” (11), and “Machine Learning” (14) underscore the seamless fusion of technology and clinical assessment in predicting fall risks. With “Balance” (68) and “Falls” (53) exhibiting strong link strengths, the field has transitioned from conventional approaches like “postural balance” and “rehabilitation” to cutting-edge methodologies leveraging “inertial sensors,” “fall detection,” and machine learning-driven predictive modeling. Co-occurrence clustering, as shown in Figure 6, paints a rich landscape of interconnected research domains: Cluster 1 champions wearable technology for mobility and fall-risk mitigation, Cluster 2 refines clinical assessments with the berg balance scale and timed up and go (TUG) test, Cluster 3 harnesses real-time feedback for personalized, tech-driven therapy, Cluster 4 delves into aging-specific studies with predictive assessments, and Cluster 5 pioneers real-world applications enhancing independent living. The trajectory of research now favors sophisticated, low-cost, data-driven solutions, seamlessly integrating gait analysis and postural sway monitoring to revolutionize elderly care with precision, accessibility, and innovation.
Figure 5.
Indexed keyword.
Figure 6.
Network visualization map of the co-occurrence and author keyword.
4.5. Geographical and institutional impact in fall-prevention research
Figure 7 showcases the global synergy in wearable sensor research for fall prevention, with the United States spearheading innovation (61 studies), followed by Germany (19), Italy (15), Ireland (11), Japan (15), and China (13), highlighting a worldwide commitment to technological advancements in healthcare. While the U.S. leads in research output, European and Asian nations play a crucial role in refining sensor technology and clinical applications. Key institutional contributors to fall-prevention research include University College Dublin (8 publications) at the forefront, alongside Philips Research and Neuroscience Research Australia (7 each). Prominent U.S. institutions, including Oregon Health & Science University and the University of Arizona, also drive groundbreaking advancements. Through innovation, clinical trials, and sensor validation, these research hubs are contributing to what is increasingly becoming a unified global pursuit toward improving fall-prevention strategies.
Figure 7.
Network visualization map of the co-authorship.
4.6. Citation metrics: impact and influence
Spanning 22 years, the field has produced 221 papers with 4933 citations, averaging 224.23 citations annually and 22.32 citations per paper, highlighting its strong academic impact in Table 5. The h-index of 37 and g-index of 63 reflect both productivity and the depth of influential research. A collaborative, multidisciplinary approach is evident, with an average of 5.47 authors per paper. Researchers contribute significantly, averaging 49.28 papers and 1188.58 citations per author, showcasing the field’s profound academic engagement and lasting influence.
Table 5.
Citations metrics.
| Metrics | Data |
|---|---|
| Papers | 221 |
| Citations | 4933 |
| Years | 22 |
| Cites_Year | 224.23 |
| Cites_Paper | 22.32 |
| Cites_Author | 1188.58 |
| Papers_Author | 49.28 |
| Authors_Paper | 5.47 |
| h_index | 37 |
| g_index | 63 |
5. Discussion
Wearable devices equipped with sensors have emerged as powerful tools for assessing fall risk by monitoring movement patterns and physiological signals. These devices integrate sensors, electronics, and software to process motion data and communicate with other systems via the internet, providing valuable insights into fall prevention.[29] The use of accelerometers, gyroscopes, magnetometers, and ultrasonic sensors has significantly enhanced motion analysis, allowing for precise multi-axis tracking and real-time monitoring of mobility and stability.[30,31] By leveraging clinical-scale methodologies integrated with sensor data, wearable technology enables continuous monitoring, predictive modeling, and early detection of fall risks, ensuring proactive and individualized interventions.[32]
One of the major breakthroughs in fall-risk assessment is the integration of machine learning algorithms with wearable sensors. Turan and Barshan (2021) demonstrated that single waist-worn sensors combined with Bayesian Decision Making achieved perfect classification accuracy, while k-nearest neighbor (k-NN) and artificial neural networks exceeded 96.2% accuracy, even when analyzing unknown class data.[29] Similarly, Abdollahi et al (2024) highlighted the effectiveness of random forest models, which attained 91% accuracy using 92 features, emphasizing that additional sensors beyond the thorax and pelvis offer diminishing returns.[33] Further advancements include the use of convolutional neural networks and recurrent neural networks to detect temporal patterns and movement anomalies, surpassing traditional classifiers such as Naive Bayes.[30] These advancements underscore the transformative role of machine learning in improving the accuracy, efficiency, and scalability of fall-risk prediction.
Connectivity improvements, including Bluetooth and Wi-Fi integration, facilitate seamless data sharing with smartphones, cloud platforms, and healthcare systems, enabling real-time monitoring and personalized feedback through user-friendly interfaces.[34,35] The analysis of highly cited research in wearable sensor technology highlights the pivotal contribution of Najafi et al (2002), whose study, with 349 citations, introduced miniature gyroscopes for real-time postural transition monitoring.[25] This research demonstrated the feasibility of wearable sensors in assessing sit-to-stand and stand-to-sit movements, offering a more objective and accurate alternative to traditional methods. Their findings also emphasized the potential of long-term monitoring in real-life environments, providing valuable insights into daily activity patterns and fall-risk assessment in elderly populations.
Advancements in sensor placement and system efficiency have further optimized wearable technology for fall-risk assessment. Abdollahi et al (2024) validated that a single thorax-worn sensor could rival the accuracy of multi-sensor systems, enabling cost-effective solutions using everyday technologies such as smartphones.[33] Turan and Barshan (2021) compared traditional marker-based systems like VICON, which uses 39 reflective markers, to the IB-gait® system, concluding that the latter provides reliable results without requiring physical markers, making it a practical alternative in clinical settings.[29] Wang et al (2024) demonstrated the potential of combining IMUs with depth cameras for capturing detailed movement patterns and 3D motion tracking during the TUG test, enhancing the accuracy of traditional clinical assessments.[36]
Energy-efficient components, adaptive sampling rates, and rechargeable batteries have improved the operational longevity of wearable devices, making them practical for continuous monitoring, especially in community and home-based settings.[31] The miniaturization and ergonomic design of sensors have addressed user comfort concerns, particularly for older adults who may be resistant to bulky or intrusive devices.[34] Wearable sensors are now widely used beyond fall-risk assessment, supporting chronic disease management, fitness tracking, mental health monitoring, and telehealth applications. Their utility extends to pathological populations, including individuals with stroke and Parkinson disease, demonstrating their versatility in healthcare.[33,37]
While wearable sensor research in fall-risk assessment has shown considerable progress, this analysis reveals emerging blind spots in the global research landscape. Notably, there is limited representation from developing nations and low-to-middle income countries, where aging demographics are increasing but research infrastructure and technology deployment remain constrained. These underrepresented regions may offer different patterns of fall risk influenced by lifestyle, environment, and healthcare access yet their perspectives remain largely absent from current literature.
Furthermore, wearable devices have proven essential in rehabilitation programs and remote monitoring. They facilitate continuous assessment of patients progress while reducing the need for frequent clinical visits, ultimately lowering healthcare costs and improving patient safety.[30,38] The affordability of wearable technology has also improved, with Chen et al (2024) showing that devices estimating the center of pressure are now cost-effective alternatives to expensive force platforms.[39] The IB-gait® system offers a viable, lower-cost option compared to the VICON Valkyrie motion capture cameras system for gait analysis, bridging the gap between clinical precision and accessibility.[29] These developments highlight the transformative potential of wearable technology in clinical settings and sports science, making them indispensable tools for modern mobility assessment and fall-prevention strategies.[31,34]
6. Clinical implications and integration into practices
Wearable sensors provide objective, real-time data on balance, gait, and postural control, replacing subjective assessments with precise insights.[40] Their integration into clinical tests like the TUG and Multi-Directional Reach Test enhances the reliability of fall-risk evaluations. Continuous monitoring through IMUs enables early detection of mobility impairments, allowing timely interventions.[14] These devices facilitate personalized fall-prevention strategies, reducing reliance on frequent clinical visits and expanding access to care through telehealth. Despite challenges such as data security and standardization, advancements in user-friendly systems and machine learning analytics support proactive interventions, ultimately promoting independence and improving the quality of life for older adults.
7. Conclusion
Drawing from 2 decades of Scopus-indexed literature, this bibliometric inquiry captured a pivotal shift in how science approaches fall risk among the elderly, not with passive observation, but with intelligent, body-worn technologies that sense, learn, and adapt. The rise of wearable sensors signals more than just technological progress, it marks a reimagining of eldercare where prevention is personalized, mobility is monitored in real-time, and autonomy is no longer sacrificed to age. As the global population continues to age, the future of fall prevention depends not only on technological sophistication but on its translation into practical, inclusive, and clinically meaningful solutions. The next generation of wearable systems must go beyond innovation, anchored in accessibility, validated through real-world application, and designed to restore confidence, preserve autonomy, and uphold the dignity of aging with purpose.
Author contributions
Conceptualization: Puspamalar Batumalai, Deepak Thazhakkattu Vasu, Kiruthika Selvakumar, Goh Choon Hian.
Formal analysis: Puspamalar Batumalai.
Methodology: Deepak Thazhakkattu Vasu, Kiruthika Selvakumar, Goh Choon Hian.
Supervision: Deepak Thazhakkattu Vasu, Kiruthika Selvakumar.
Writing – original draft: Puspamalar Batumalai.
Writing – review & editing: Deepak Thazhakkattu Vasu, Kiruthika Selvakumar.
Abbreviations:
- IMU
- inertial measurement unit
- TUG
- timed up and go
The authors have no funding and conflicts of interest to disclose.
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
How to cite this article: Batumalai P, Thazhakkattu Vasu D, Selvakumar K, Choon Hian G. A bibliometric analysis of wearable sensors for fall-risk assessment in the elderly population. Medicine 2025;104:35(e44118).
Contributor Information
Puspamalar Batumalai, Email: puspamalarb@gmail.com.
Kiruthika Selvakumar, Email: kiruthika@utar.edu.my.
Goh Choon Hian, Email: gohch@utar.edu.my.
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