Elderly woman confidently walking with wearable sensors for fall prevention.

Smart Steps: Can Wearable Sensors Predict Falls in Older Adults?

Jordan Keane in Health & Wellness December 2025 • 4 min read.

"New Research Unlocks Potential for Proactive Fall Prevention with Wearable Tech"

Falls are a significant threat to the health and independence of older adults. Each year, a substantial portion of the population aged 65 and over experiences a fall, leading to injuries, reduced quality of life, and increased healthcare costs. Identifying individuals at high risk of falling is crucial for proactive intervention and prevention.

Traditionally, fall risk assessments have relied on questionnaires, clinical scales, and sophisticated laboratory equipment. While these methods have their merits, they often suffer from limitations such as subjectivity, high costs, and the inability to assess individuals in real-world settings. This is where wearable sensor technology steps in, offering a more objective, affordable, and accessible approach to fall risk assessment.

Recent research published in Scientific Reports explores the potential of wearable inertial sensors and a new test battery for distinguishing between older adults with and without a history of falls. This study suggests that wearable sensor-based systems hold promise for identifying individuals at risk, paving the way for proactive fall prevention strategies.

Decoding the Science: How Wearable Sensors Can Predict Falls

Elderly woman confidently walking with wearable sensors for fall prevention.

The study, led by Hai Qiu, Rana Zia Ur Rehman, Xiaoqun Yu, and Shuping Xiong, sought to determine whether data collected from wearable inertial sensors could accurately differentiate between older adults who had experienced falls (fallers) and those who had not (non-fallers). The researchers designed a comprehensive test battery that utilized five wearable inertial sensors to assess various factors contributing to fall risk.

The test battery consisted of seven subtests, each designed to evaluate specific aspects of balance, mobility, and cognitive function. These subtests included:

Sensory Integration Test (SIT): Assesses the individual’s ability to maintain balance under different sensory conditions, such as eyes open or closed, and on firm or foam surfaces.
Limits of Stability (LOS): Measures how far an individual can reach in different directions without losing balance.
Sit-to-Stand Five Times (STS5): Evaluates the time it takes for an individual to repeatedly stand up and sit down.
Timed Up and Go (TUG): Assesses overall mobility and balance during a series of movements, including standing up, walking, turning, and sitting down.
Motor Function (MF): Examines the range of motion and strength in the lower extremities.
Choice Reaction Test (CRT): Measures cognitive processing speed and reaction time.
Computerized Falls Efficacy Scale (FES): Assesses an individual’s fear of falling.

The study involved 196 community-dwelling Korean older women, categorized as fallers (n=82) and non-fallers (n=114) based on their history of falls. Participants wore inertial sensors on their low back, upper legs, and lower legs while performing the test battery. The sensors collected data on acceleration, angular velocity, and magnetism, which were then analyzed using machine learning models to classify participants as fallers or non-fallers.

A Promising Future for Proactive Fall Prevention

This research highlights the potential of wearable inertial sensors for accurate and objective fall risk assessment in older adults. By combining sensor data with machine learning models, healthcare professionals can identify individuals at high risk of falling and implement targeted interventions to prevent falls and improve the quality of life for older adults. Further research is needed to refine these technologies and translate them into practical tools for clinical practice.

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This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1038/s41598-018-34671-6, Alternate LINK

Title: Application Of Wearable Inertial Sensors And A New Test Battery For Distinguishing Retrospective Fallers From Non-Fallers Among Community-Dwelling Older People

Subject: Multidisciplinary

Journal: Scientific Reports

Publisher: Springer Science and Business Media LLC

Authors: Hai Qiu, Rana Zia Ur Rehman, Xiaoqun Yu, Shuping Xiong

Published: 2018-11-05

Everything You Need To Know

What is wearable sensor technology in the context of fall prevention?

Wearable sensor technology is a promising innovation for predicting falls in older adults. It utilizes wearable inertial sensors to collect data on acceleration, angular velocity, and magnetism during specific tests. This data, combined with machine learning models, helps to differentiate between individuals who have experienced falls (fallers) and those who have not (non-fallers). This approach offers a more objective and accessible way to assess fall risk compared to traditional methods like questionnaires and clinical scales, which can be subjective and limited in real-world applicability.

Why is wearable sensor technology important for fall risk assessment?

The research highlights the significance of using wearable inertial sensors to assess fall risk because falls are a major health concern for older adults, leading to injuries, reduced quality of life, and increased healthcare costs. By identifying individuals at high risk of falling, healthcare professionals can implement proactive interventions to prevent falls, thereby improving the overall well-being and independence of older adults. This technology provides a more accurate and efficient method compared to traditional assessments, enabling timely and personalized care.

What are the different components of the test battery used in the research?

The test battery consists of seven subtests: the Sensory Integration Test (SIT), Limits of Stability (LOS), Sit-to-Stand Five Times (STS5), Timed Up and Go (TUG), Motor Function (MF), Choice Reaction Test (CRT), and Computerized Falls Efficacy Scale (FES). Each subtest assesses a specific aspect of balance, mobility, or cognitive function. For example, the SIT evaluates balance under different sensory conditions, while the TUG assesses overall mobility. The CRT examines cognitive processing speed, and the FES assesses an individual’s fear of falling. These tests, performed with the wearable inertial sensors, provide comprehensive data for fall risk assessment.

How do wearable inertial sensors work in this research?

Wearable inertial sensors collect data on acceleration, angular velocity, and magnetism. This data is gathered while participants perform a series of tests, known as the test battery. The sensors are typically placed on the low back, upper legs, and lower legs. The collected data is then analyzed using machine learning models. This analysis helps to identify patterns and characteristics that distinguish between older adults who have fallen (fallers) and those who have not (non-fallers). The accuracy of these sensors and the analysis offers a significant advantage in the early detection of fall risks.

What are the implications of this research for the future of fall prevention?

The study's findings suggest that wearable sensor-based systems have the potential to revolutionize fall prevention. By accurately assessing fall risk, these technologies can facilitate the development of targeted interventions. This includes personalized exercise programs to improve balance and mobility or modifications to the home environment to reduce hazards. The goal is to proactively prevent falls and improve the quality of life for older adults. Although further research is needed to refine these technologies, the early results point toward a future where falls can be predicted and prevented more effectively.