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 ageing, 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 concurrently measuring multiple 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. Researchers focused on all aspects related to wearable design, data science, and data interpretation are encouraged to submit.
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 Patient Monitoring
- Frailty
- Mobility impairment
- Mobile Health Technology
- 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.
While falls are considered to be a major problem that is growing as the population is rapidly ageing, 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 concurrently measuring multiple 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. Researchers focused on all aspects related to wearable design, data science, and data interpretation are encouraged to submit.
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 Patient Monitoring
- Frailty
- Mobility impairment
- Mobile Health Technology
- 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: remote health monitoring, mhealth, wearable, mobility impairment, frailty
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