Research Topic

Wearable and Ambient Sensing: Technological Advances in Human Motion Analysis

About this Research Topic

There is a pressing need to develop holistic technologies that build upon the Internet of Medical Things (IoMT) to allow information from wearable and ambient sensing to be safely exploited in healthcare applications.
These platforms have the potential to provide objective measures of well-being and disease progression along with rehabilitation strategies based on human motion analysis. For example, neurological diseases and mood disorders have profound impact in gait characteristics such as increased gait speed, step length and arm swing. Furthermore, reduced ability of elderly people to adapt their gait results in falls and further hospitalizations, which increases the risk of depression and mortality.

Wearable and ambient sensing along with recent advances in Artificial Intelligence (AI) can empower patients with chronic diseases/disorders as well as elderly people to live an independent life at home while they manage their condition safely. For example, it is well recognized that eHealth can provide more personalized and accurate treatments with the potential to improve health outcome in deprived areas, thus reducing inequalities. Human motion analysis is also of paramount importance in assistive and rehabilitative robotics and technologies. Wearable robots should sense user’s intention and adjust to help for example a stroke patient to reach and grasp an object or a patient with an artificial leg to adapt his gait efficiently.

This Research Topic focuses on the challenges involved in translating recent advances in wearable technology and computer vision in home healthcare and rehabilitation. Topics of interest include but are not limited to:

• Wearable sensors and devices for gait analysis
• New sensing technologies – ambient radar and 5G
• Wearable and ambient sensing for detection and evaluation of neurological diseases
• Assistive and rehabilitative robotics and technologies.
• Intention detection and closed loop interfaces
• Biometrics based on human motion analysis
• Advances in machine learning that tackle privacy and ethical concerns
• Intuitive interactive designs that enable patient-centred AI systems


Keywords: human motion analysis, gait analysis, wearable sensing technology, ambient sensing technology, assistive and rehabilitation robotics technology, human motion in biometrics, machine learning advances


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.

There is a pressing need to develop holistic technologies that build upon the Internet of Medical Things (IoMT) to allow information from wearable and ambient sensing to be safely exploited in healthcare applications.
These platforms have the potential to provide objective measures of well-being and disease progression along with rehabilitation strategies based on human motion analysis. For example, neurological diseases and mood disorders have profound impact in gait characteristics such as increased gait speed, step length and arm swing. Furthermore, reduced ability of elderly people to adapt their gait results in falls and further hospitalizations, which increases the risk of depression and mortality.

Wearable and ambient sensing along with recent advances in Artificial Intelligence (AI) can empower patients with chronic diseases/disorders as well as elderly people to live an independent life at home while they manage their condition safely. For example, it is well recognized that eHealth can provide more personalized and accurate treatments with the potential to improve health outcome in deprived areas, thus reducing inequalities. Human motion analysis is also of paramount importance in assistive and rehabilitative robotics and technologies. Wearable robots should sense user’s intention and adjust to help for example a stroke patient to reach and grasp an object or a patient with an artificial leg to adapt his gait efficiently.

This Research Topic focuses on the challenges involved in translating recent advances in wearable technology and computer vision in home healthcare and rehabilitation. Topics of interest include but are not limited to:

• Wearable sensors and devices for gait analysis
• New sensing technologies – ambient radar and 5G
• Wearable and ambient sensing for detection and evaluation of neurological diseases
• Assistive and rehabilitative robotics and technologies.
• Intention detection and closed loop interfaces
• Biometrics based on human motion analysis
• Advances in machine learning that tackle privacy and ethical concerns
• Intuitive interactive designs that enable patient-centred AI systems


Keywords: human motion analysis, gait analysis, wearable sensing technology, ambient sensing technology, assistive and rehabilitation robotics technology, human motion in biometrics, machine learning advances


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

14 May 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

14 May 2021 Manuscript

Participating Journals

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

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