Neurological disorders such as stroke, multiple sclerosis, and Parkinson’s disease affect mobility of the subject. In stroke, it is hypothesized that the recovery of movement quality may take place through a combination of spontaneous and learning-dependent processes. Eventually, motor patterns either return to more normal pre-stroke patterns (restitution) or manifest as new patterns different from those pre-stroke (compensation). Objective measurement of movement quality can thus help us understand recovery post stroke, and tailor patient specific therapies during rehabilitation. Similar strategies are also observed post onset of Multiple Sclerosis, or Parkinson’s’.
Wearables for sensing human movement are omnipresent. The number of published articles related to 'Wearable', 'Sensing', and 'movement' have gone up every year for the last five years when considering the Scopus database alone. There is an ongoing race to make them smaller, and smarter. This growth has been accelerated by miniaturization of sensors, and better understanding of the physics of sensing and movement. Adding to this, concepts of machine learning have allowed us to extract high-level parameters such as activity and pose of the human body. These are proving useful in a number of applications for sensing different types of movement, as miniature wearables bring the laboratory to the body.
Wearables have been used extensively to measure movement biomechanics after neurological disorders. Examples of these wearable sensors include gyroscopes, accelerometers, pressure sensors, electromyography, etc. Wearable sensors have been more commonly used for activity recognition, and quantifying movement intensity. However, objective measurement of biomechanics using wearables could be useful for measuring movement quality, which have two potential advantages. Miniature sensors can be used by the clinicians to setup more measurements post stroke, and can also help clinicians track motor recovery at the patient’s living space.
This Research Topic welcomes studies that develop or validate wearable sensors for measuring quality of movement, post neurological disorders. It will attract and allow cross-pollination of state-of-the-art submissions in new wearable sensing techniques among different neurological disorders. This can accelerate development of such solutions towards viable products that benefit the survivors of the disorders.
Topic Editor Dr. Jaap H. Buurke is affiliated to Roessingh Research and Development; an SME focussing on rehabilitation research. Topic Editor Dr. Peter Shull is a co-founder of SageMotion, LLC. All other Topic Editors declare no competing interests with regards to the Research Topic.
Neurological disorders such as stroke, multiple sclerosis, and Parkinson’s disease affect mobility of the subject. In stroke, it is hypothesized that the recovery of movement quality may take place through a combination of spontaneous and learning-dependent processes. Eventually, motor patterns either return to more normal pre-stroke patterns (restitution) or manifest as new patterns different from those pre-stroke (compensation). Objective measurement of movement quality can thus help us understand recovery post stroke, and tailor patient specific therapies during rehabilitation. Similar strategies are also observed post onset of Multiple Sclerosis, or Parkinson’s’.
Wearables for sensing human movement are omnipresent. The number of published articles related to 'Wearable', 'Sensing', and 'movement' have gone up every year for the last five years when considering the Scopus database alone. There is an ongoing race to make them smaller, and smarter. This growth has been accelerated by miniaturization of sensors, and better understanding of the physics of sensing and movement. Adding to this, concepts of machine learning have allowed us to extract high-level parameters such as activity and pose of the human body. These are proving useful in a number of applications for sensing different types of movement, as miniature wearables bring the laboratory to the body.
Wearables have been used extensively to measure movement biomechanics after neurological disorders. Examples of these wearable sensors include gyroscopes, accelerometers, pressure sensors, electromyography, etc. Wearable sensors have been more commonly used for activity recognition, and quantifying movement intensity. However, objective measurement of biomechanics using wearables could be useful for measuring movement quality, which have two potential advantages. Miniature sensors can be used by the clinicians to setup more measurements post stroke, and can also help clinicians track motor recovery at the patient’s living space.
This Research Topic welcomes studies that develop or validate wearable sensors for measuring quality of movement, post neurological disorders. It will attract and allow cross-pollination of state-of-the-art submissions in new wearable sensing techniques among different neurological disorders. This can accelerate development of such solutions towards viable products that benefit the survivors of the disorders.
Topic Editor Dr. Jaap H. Buurke is affiliated to Roessingh Research and Development; an SME focussing on rehabilitation research. Topic Editor Dr. Peter Shull is a co-founder of SageMotion, LLC. All other Topic Editors declare no competing interests with regards to the Research Topic.