Integration of smart insoles for gait assessment in exoskeleton assisted rehabilitation

A robotic exoskeleton enables individuals with limited or no mobility to engage in moderate exercises, thereby promoting physical fitness and overall well-being. However, exoskeletons alone do not provide comprehensive insights into gait pattern monitoring and analysis over time. This study proposes...

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Published in:	Scientific reports Vol. 15; no. 1; pp. 28350 - 16
Main Authors:	D’Arco, Luigi, Wang, Haiying, Zheng, Huiru
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 04.08.2025 Nature Publishing Group Nature Portfolio
Subjects:	639/705/117 692/700 Adult Algorithms Biomechanical Phenomena Brain Injuries, Traumatic - physiopathology Brain Injuries, Traumatic - rehabilitation Exercise Therapy - methods Exoskeleton Exoskeleton Device Female Fitness equipment Foot Orthoses Gait Gait - physiology Gait analysis Gait kinematic parameters Gait kinetic parameters Humanities and Social Sciences Humans Kinematics Male Middle Aged multidisciplinary Multiple sclerosis Multiple Sclerosis - physiopathology Multiple Sclerosis - rehabilitation Musculoskeletal diseases Physical fitness Rehabilitation Robotics Science Science (multidisciplinary) Sensors Shoes Smart insoles Spinal cord injuries Spinal Cord Injuries - physiopathology Spinal Cord Injuries - rehabilitation Stroke Rehabilitation Traumatic brain injury Walking Gait kinetic parameters Gait kinematic parameters Exoskeleton Rehabilitation Smart insoles Gait analysis
ISSN:	2045-2322, 2045-2322
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Abstract	A robotic exoskeleton enables individuals with limited or no mobility to engage in moderate exercises, thereby promoting physical fitness and overall well-being. However, exoskeletons alone do not provide comprehensive insights into gait pattern monitoring and analysis over time. This study proposes the integration of smart insoles as a cost-effective and non-invasive tool for gait assessment in exoskeleton-assisted rehabilitation. Ten participants, including three unimpaired subjects used only as a reference, one stroke, one spinal cord injury, one traumatic brain injury, and four multiple sclerosis subjects were involved in a 12-week program where weekly rehabilitation exercises were conducted and gait patterns were monitored in three assessment sessions. Gait phases were identified using a Finite State Machine, with transitions guided by predictions from a fuzzy c-means clustering algorithm. Kinematic and kinetic analyses revealed significant disparities in stride time, stance time, and the trajectories of the centre of pressure. The findings demonstrated that while the exoskeleton enabled participants with limited or no mobility to walk similarly to unimpaired individuals, the use of smart insoles identified notable differences in their gait patterns. These differences could be traced back to choices in the rehabilitation plan, underscoring the importance of such devices for understanding rehabilitation progress. An acceptability analysis showed that participants found the smart insoles comfortable and expressed a willingness to use them for future rehabilitation. In conclusion, this study demonstrates the potential of smart insoles for the assessment of individuals’ rehabilitation progress while using an exoskeleton, laying the groundwork for a system that can support clinicians in developing tailored rehabilitation plans.
AbstractList	A robotic exoskeleton enables individuals with limited or no mobility to engage in moderate exercises, thereby promoting physical fitness and overall well-being. However, exoskeletons alone do not provide comprehensive insights into gait pattern monitoring and analysis over time. This study proposes the integration of smart insoles as a cost-effective and non-invasive tool for gait assessment in exoskeleton-assisted rehabilitation. Ten participants, including three unimpaired subjects used only as a reference, one stroke, one spinal cord injury, one traumatic brain injury, and four multiple sclerosis subjects were involved in a 12-week program where weekly rehabilitation exercises were conducted and gait patterns were monitored in three assessment sessions. Gait phases were identified using a Finite State Machine, with transitions guided by predictions from a fuzzy c-means clustering algorithm. Kinematic and kinetic analyses revealed significant disparities in stride time, stance time, and the trajectories of the centre of pressure. The findings demonstrated that while the exoskeleton enabled participants with limited or no mobility to walk similarly to unimpaired individuals, the use of smart insoles identified notable differences in their gait patterns. These differences could be traced back to choices in the rehabilitation plan, underscoring the importance of such devices for understanding rehabilitation progress. An acceptability analysis showed that participants found the smart insoles comfortable and expressed a willingness to use them for future rehabilitation. In conclusion, this study demonstrates the potential of smart insoles for the assessment of individuals' rehabilitation progress while using an exoskeleton, laying the groundwork for a system that can support clinicians in developing tailored rehabilitation plans.A robotic exoskeleton enables individuals with limited or no mobility to engage in moderate exercises, thereby promoting physical fitness and overall well-being. However, exoskeletons alone do not provide comprehensive insights into gait pattern monitoring and analysis over time. This study proposes the integration of smart insoles as a cost-effective and non-invasive tool for gait assessment in exoskeleton-assisted rehabilitation. Ten participants, including three unimpaired subjects used only as a reference, one stroke, one spinal cord injury, one traumatic brain injury, and four multiple sclerosis subjects were involved in a 12-week program where weekly rehabilitation exercises were conducted and gait patterns were monitored in three assessment sessions. Gait phases were identified using a Finite State Machine, with transitions guided by predictions from a fuzzy c-means clustering algorithm. Kinematic and kinetic analyses revealed significant disparities in stride time, stance time, and the trajectories of the centre of pressure. The findings demonstrated that while the exoskeleton enabled participants with limited or no mobility to walk similarly to unimpaired individuals, the use of smart insoles identified notable differences in their gait patterns. These differences could be traced back to choices in the rehabilitation plan, underscoring the importance of such devices for understanding rehabilitation progress. An acceptability analysis showed that participants found the smart insoles comfortable and expressed a willingness to use them for future rehabilitation. In conclusion, this study demonstrates the potential of smart insoles for the assessment of individuals' rehabilitation progress while using an exoskeleton, laying the groundwork for a system that can support clinicians in developing tailored rehabilitation plans. A robotic exoskeleton enables individuals with limited or no mobility to engage in moderate exercises, thereby promoting physical fitness and overall well-being. However, exoskeletons alone do not provide comprehensive insights into gait pattern monitoring and analysis over time. This study proposes the integration of smart insoles as a cost-effective and non-invasive tool for gait assessment in exoskeleton-assisted rehabilitation. Ten participants, including three unimpaired subjects used only as a reference, one stroke, one spinal cord injury, one traumatic brain injury, and four multiple sclerosis subjects were involved in a 12-week program where weekly rehabilitation exercises were conducted and gait patterns were monitored in three assessment sessions. Gait phases were identified using a Finite State Machine, with transitions guided by predictions from a fuzzy c-means clustering algorithm. Kinematic and kinetic analyses revealed significant disparities in stride time, stance time, and the trajectories of the centre of pressure. The findings demonstrated that while the exoskeleton enabled participants with limited or no mobility to walk similarly to unimpaired individuals, the use of smart insoles identified notable differences in their gait patterns. These differences could be traced back to choices in the rehabilitation plan, underscoring the importance of such devices for understanding rehabilitation progress. An acceptability analysis showed that participants found the smart insoles comfortable and expressed a willingness to use them for future rehabilitation. In conclusion, this study demonstrates the potential of smart insoles for the assessment of individuals’ rehabilitation progress while using an exoskeleton, laying the groundwork for a system that can support clinicians in developing tailored rehabilitation plans. Abstract A robotic exoskeleton enables individuals with limited or no mobility to engage in moderate exercises, thereby promoting physical fitness and overall well-being. However, exoskeletons alone do not provide comprehensive insights into gait pattern monitoring and analysis over time. This study proposes the integration of smart insoles as a cost-effective and non-invasive tool for gait assessment in exoskeleton-assisted rehabilitation. Ten participants, including three unimpaired subjects used only as a reference, one stroke, one spinal cord injury, one traumatic brain injury, and four multiple sclerosis subjects were involved in a 12-week program where weekly rehabilitation exercises were conducted and gait patterns were monitored in three assessment sessions. Gait phases were identified using a Finite State Machine, with transitions guided by predictions from a fuzzy c-means clustering algorithm. Kinematic and kinetic analyses revealed significant disparities in stride time, stance time, and the trajectories of the centre of pressure. The findings demonstrated that while the exoskeleton enabled participants with limited or no mobility to walk similarly to unimpaired individuals, the use of smart insoles identified notable differences in their gait patterns. These differences could be traced back to choices in the rehabilitation plan, underscoring the importance of such devices for understanding rehabilitation progress. An acceptability analysis showed that participants found the smart insoles comfortable and expressed a willingness to use them for future rehabilitation. In conclusion, this study demonstrates the potential of smart insoles for the assessment of individuals’ rehabilitation progress while using an exoskeleton, laying the groundwork for a system that can support clinicians in developing tailored rehabilitation plans.
ArticleNumber	28350
Author	D’Arco, Luigi Wang, Haiying Zheng, Huiru
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Keywords	Gait kinetic parameters Gait kinematic parameters Exoskeleton Rehabilitation Smart insoles Gait analysis
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Snippet	A robotic exoskeleton enables individuals with limited or no mobility to engage in moderate exercises, thereby promoting physical fitness and overall... Abstract A robotic exoskeleton enables individuals with limited or no mobility to engage in moderate exercises, thereby promoting physical fitness and overall...
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SubjectTerms	639/705/117 692/700 Adult Algorithms Biomechanical Phenomena Brain Injuries, Traumatic - physiopathology Brain Injuries, Traumatic - rehabilitation Exercise Therapy - methods Exoskeleton Exoskeleton Device Female Fitness equipment Foot Orthoses Gait Gait - physiology Gait analysis Gait kinematic parameters Gait kinetic parameters Humanities and Social Sciences Humans Kinematics Male Middle Aged multidisciplinary Multiple sclerosis Multiple Sclerosis - physiopathology Multiple Sclerosis - rehabilitation Musculoskeletal diseases Physical fitness Rehabilitation Robotics Science Science (multidisciplinary) Sensors Shoes Smart insoles Spinal cord injuries Spinal Cord Injuries - physiopathology Spinal Cord Injuries - rehabilitation Stroke Rehabilitation Traumatic brain injury Walking
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Title	Integration of smart insoles for gait assessment in exoskeleton assisted rehabilitation
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