Quantifying States and Transitions of Emerging Postural Control for Children Not Yet Able to Sit Independently
Objective, quantitative postural data is limited for individuals who are non-ambulatory, especially for those who have not yet developed trunk control for sitting. There are no gold standard measurements to monitor the emergence of upright trunk control. Quantification of intermediate levels of post...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/6/3309 |
| _version_ | 1797608980687618048 |
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| author | Patricia Mellodge Sandra Saavedra Linda Tran Poit Kristamarie A. Pratt Adam D. Goodworth |
| author_facet | Patricia Mellodge Sandra Saavedra Linda Tran Poit Kristamarie A. Pratt Adam D. Goodworth |
| author_sort | Patricia Mellodge |
| collection | DOAJ |
| description | Objective, quantitative postural data is limited for individuals who are non-ambulatory, especially for those who have not yet developed trunk control for sitting. There are no gold standard measurements to monitor the emergence of upright trunk control. Quantification of intermediate levels of postural control is critically needed to improve research and intervention for these individuals. Accelerometers and video were used to record postural alignment and stability for eight children with severe cerebral palsy aged 2 to 13 years, under two conditions, seated on a bench with only pelvic support and with additional thoracic support. This study developed an algorithm to classify vertical alignment and states of upright control; Stable, Wobble, Collapse, Rise and Fall from accelerometer data. Next, a Markov chain model was created to calculate a normative score for postural state and transition for each participant with each level of support. This tool allowed quantification of behaviors previously not captured in adult-based postural sway measures. Histogram and video recordings were used to confirm the output of the algorithm. Together, this tool revealed that providing external support allowed all participants: (1) to increase their time spent in the Stable state, and (2) to reduce the frequency of transitions between states. Furthermore, all participants except one showed improved state and transition scores when given external support. |
| first_indexed | 2024-03-11T05:55:14Z |
| format | Article |
| id | doaj.art-54571f7ca19e42d093e3cc9ae6c623a3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:55:14Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-54571f7ca19e42d093e3cc9ae6c623a32023-11-17T13:48:54ZengMDPI AGSensors1424-82202023-03-01236330910.3390/s23063309Quantifying States and Transitions of Emerging Postural Control for Children Not Yet Able to Sit IndependentlyPatricia Mellodge0Sandra Saavedra1Linda Tran Poit2Kristamarie A. Pratt3Adam D. Goodworth4Department of Electrical and Computer Engineering, College of Engineering, Technology, and Architecture, University of Hartford, West Hartford, CT 06117, USAPhysical Therapy Program, College of Health Sciences, Western University of Health Sciences-Oregon, Lebanon, OR 97355, USAHartford Hospital, Hartford, CT 06106, USADepartment of Rehabilitation Sciences, College of Education, Nursing and Health Professions, University of Hartford, West Hartford, CT 06117, USADepartment of Kinesiology, Westmont College, Santa Barbara, CA 93108, USAObjective, quantitative postural data is limited for individuals who are non-ambulatory, especially for those who have not yet developed trunk control for sitting. There are no gold standard measurements to monitor the emergence of upright trunk control. Quantification of intermediate levels of postural control is critically needed to improve research and intervention for these individuals. Accelerometers and video were used to record postural alignment and stability for eight children with severe cerebral palsy aged 2 to 13 years, under two conditions, seated on a bench with only pelvic support and with additional thoracic support. This study developed an algorithm to classify vertical alignment and states of upright control; Stable, Wobble, Collapse, Rise and Fall from accelerometer data. Next, a Markov chain model was created to calculate a normative score for postural state and transition for each participant with each level of support. This tool allowed quantification of behaviors previously not captured in adult-based postural sway measures. Histogram and video recordings were used to confirm the output of the algorithm. Together, this tool revealed that providing external support allowed all participants: (1) to increase their time spent in the Stable state, and (2) to reduce the frequency of transitions between states. Furthermore, all participants except one showed improved state and transition scores when given external support.https://www.mdpi.com/1424-8220/23/6/3309motor controlaccelerometercerebral palsyassessmenttrunkbiomechanical algorithm |
| spellingShingle | Patricia Mellodge Sandra Saavedra Linda Tran Poit Kristamarie A. Pratt Adam D. Goodworth Quantifying States and Transitions of Emerging Postural Control for Children Not Yet Able to Sit Independently Sensors motor control accelerometer cerebral palsy assessment trunk biomechanical algorithm |
| title | Quantifying States and Transitions of Emerging Postural Control for Children Not Yet Able to Sit Independently |
| title_full | Quantifying States and Transitions of Emerging Postural Control for Children Not Yet Able to Sit Independently |
| title_fullStr | Quantifying States and Transitions of Emerging Postural Control for Children Not Yet Able to Sit Independently |
| title_full_unstemmed | Quantifying States and Transitions of Emerging Postural Control for Children Not Yet Able to Sit Independently |
| title_short | Quantifying States and Transitions of Emerging Postural Control for Children Not Yet Able to Sit Independently |
| title_sort | quantifying states and transitions of emerging postural control for children not yet able to sit independently |
| topic | motor control accelerometer cerebral palsy assessment trunk biomechanical algorithm |
| url | https://www.mdpi.com/1424-8220/23/6/3309 |
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