Wearables for Movement Analysis in Healthcare

Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates...

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Bibliographic Details
Other Authors: Capodaglio, Paolo (Editor), Cimolin, Veronica (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
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DOAB: description of the publication
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245 1 0 |a Wearables for Movement Analysis in Healthcare 
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520 |a Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients' capabilities, unobtrusively and continuously, for clinical purposes. 
540 |a Creative Commons  |f https://creativecommons.org/licenses/by/4.0/  |2 cc  |4 https://creativecommons.org/licenses/by/4.0/ 
546 |a English 
650 7 |a Research & information: general  |2 bicssc 
650 7 |a Biology, life sciences  |2 bicssc 
650 7 |a Biochemistry  |2 bicssc 
653 |a gait 
653 |a smoothness 
653 |a older adults 
653 |a accelerometer 
653 |a inertial measurement unit (IMU) 
653 |a upper extremity 
653 |a stroke 
653 |a biomechanical phenomena 
653 |a kinematics 
653 |a inertial measurement systems 
653 |a motion analysis 
653 |a wearable devices 
653 |a e-textile 
653 |a gait analysis 
653 |a m-health 
653 |a plantar pressure 
653 |a validation 
653 |a Internet of Things 
653 |a body sensor network 
653 |a inertial sensors 
653 |a ground reaction force 
653 |a spatio-temporal parameters 
653 |a wearable sensors 
653 |a decision trees 
653 |a foot drop stimulation 
653 |a symmetry 
653 |a inertial measurement sensor 
653 |a wearable inertial sensors 
653 |a marker-based optoelectronic system 
653 |a ACL 
653 |a rehabilitation 
653 |a motion capture validation 
653 |a upper limb 
653 |a Parkinson's disease 
653 |a Box and Block test 
653 |a inertial sensors network 
653 |a biomechanics analysis 
653 |a kinematic data 
653 |a hand trajectories 
653 |a kinematic 
653 |a inertial measurement units 
653 |a angle-angle diagrams 
653 |a cyclograms 
653 |a obesity 
653 |a bradykinesia 
653 |a real-life 
653 |a naturalistic monitoring 
653 |a motor fluctuation 
653 |a wearable movement sensor 
653 |a IMU 
653 |a motion capture 
653 |a reliability 
653 |a clinical 
653 |a orthopedic 
653 |a sensory-motor gait disorders 
653 |a limb prosthesis 
653 |a spatial-temporal analysis 
653 |a symmetry index 
653 |a walking 
653 |a 6-min walking test 
653 |a wearable system 
653 |a inertial sensor 
653 |a RGB-D sensors 
653 |a optoelectronic system 
653 |a movement analysis 
653 |a hemiparesis 
653 |a n/a 
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