Research Topic

Preserving Health: Health Technology for Fall Prevention

About this Research Topic

In recent years, many technologies for mobility, balance, and fall detection in various populations have emerged. Wearable sensors, passive in-house monitors, and many combinations thereof all promise to alert caregivers or emergency personnel once a fall is detected. But for many individuals, detecting a fall once it has occurred is already too late – the damage has been done, outcomes are typically poor, and cost is always high. What is needed for health preservation is fall prevention.

While falls are considered to be a major problem that is growing as the population is rapidly aging, the majority of current technology and care solutions are based on a one-dimensional approach. However, the causes leading to falls are varied and are better understood by assessing information at different scales and populations.

To our knowledge, there has been no previous effort to address fall prevention from multiple levels of monitoring and predictive perspectives. In this Research Topic, we call upon researchers who are at the helm of measuring functions among older adults as well as those with pathology. Especially those involved in fall risk assessments and fall risk prediction areas, including balance and mobility.

Although biomechanical and physiological parameters associated with mobility issues and fall risk have been established by testing/collecting cohort relevant data at the population levels, currently, it is unknown, how these features can be used for personalized assessments in international environments. The fundamental contribution of this Research Topic is to provide cogent features/models relevant for health assessments utilizing multimodal, time varying physiological and biomechanical fall risk characteristics utilizing a variety of subjective and objective techniques.

Relevant sub-topics include:

• Remote health monitoring
• Frailty
• Mobility impairment
• Smart sensors / IoT
• Deep and/or machine learning
• Smart materials
• Wearable therapies
• Wearable cueing
• Wearable based training

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Dr. Bijan Najafi has received research support from the following organizations: Biosensics LLC, Eden Medical, EO2 Concept, AVEX, OHI, LifeNet and AVAZZIA. All other Topic Editors declare no conflicts of interest.


Keywords: Fall Prevention, Fall Detection, Fall Risk Assessment, Personalized Assessment, Wearables


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

In recent years, many technologies for mobility, balance, and fall detection in various populations have emerged. Wearable sensors, passive in-house monitors, and many combinations thereof all promise to alert caregivers or emergency personnel once a fall is detected. But for many individuals, detecting a fall once it has occurred is already too late – the damage has been done, outcomes are typically poor, and cost is always high. What is needed for health preservation is fall prevention.

While falls are considered to be a major problem that is growing as the population is rapidly aging, the majority of current technology and care solutions are based on a one-dimensional approach. However, the causes leading to falls are varied and are better understood by assessing information at different scales and populations.

To our knowledge, there has been no previous effort to address fall prevention from multiple levels of monitoring and predictive perspectives. In this Research Topic, we call upon researchers who are at the helm of measuring functions among older adults as well as those with pathology. Especially those involved in fall risk assessments and fall risk prediction areas, including balance and mobility.

Although biomechanical and physiological parameters associated with mobility issues and fall risk have been established by testing/collecting cohort relevant data at the population levels, currently, it is unknown, how these features can be used for personalized assessments in international environments. The fundamental contribution of this Research Topic is to provide cogent features/models relevant for health assessments utilizing multimodal, time varying physiological and biomechanical fall risk characteristics utilizing a variety of subjective and objective techniques.

Relevant sub-topics include:

• Remote health monitoring
• Frailty
• Mobility impairment
• Smart sensors / IoT
• Deep and/or machine learning
• Smart materials
• Wearable therapies
• Wearable cueing
• Wearable based training

---
Dr. Bijan Najafi has received research support from the following organizations: Biosensics LLC, Eden Medical, EO2 Concept, AVEX, OHI, LifeNet and AVAZZIA. All other Topic Editors declare no conflicts of interest.


Keywords: Fall Prevention, Fall Detection, Fall Risk Assessment, Personalized Assessment, Wearables


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

18 June 2021 Abstract
05 November 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

18 June 2021 Abstract
05 November 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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