Department of Electrical and Computer Engineering, College of Engineering, Technology, and Architecture, University of Hartford, West Hartford, CT 06117, USA.
Physical Therapy Program, College of Health Sciences, Western University of Health Sciences-Oregon, Lebanon, OR 97355, USA.
Sensors (Basel). 2023 Mar 21;23(6):3309. doi: 10.3390/s23063309.
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.
对于那些无法行走的人，特别是那些尚未发展出坐姿躯干控制能力的人，客观、定量的姿势数据是有限的。目前还没有金标准的测量方法来监测直立躯干控制的出现。为了改善这些人的研究和干预，迫切需要对姿势控制的中间水平进行量化。本研究使用加速度计和视频记录了 8 名 2 至 13 岁患有严重脑瘫的儿童在两种情况下的姿势对齐和稳定性，分别是仅骨盆支撑的坐姿和增加胸部支撑的坐姿。该研究开发了一种算法，根据加速度计数据将垂直对齐和直立控制状态分类为稳定、晃动、倒塌、上升和下降。然后，创建了一个马尔可夫链模型来计算每个参与者在每个支撑水平下的姿势状态和过渡的标准分数。该工具允许对以前在基于成人的姿势摆动测量中未捕获的行为进行量化。直方图和视频记录用于确认算法的输出。该工具共同揭示，提供外部支持使所有参与者：(1)增加稳定状态的时间，(2)减少状态之间的转换频率。此外，当给予外部支持时，除了一名参与者外，所有参与者的状态和过渡评分都有所提高。
