ecent studies have shown that balance performance assessment based on artificial intelligence (AI) is feasible. However, balance control is very complex and requires different subsystems to participate, which have not been evaluated individually yet. Furthermore, these studies only classified individual's balance performance across limited grades. Therefore, in this study we attempted to implement AI to precisely evaluate different types of balance control subsystems (BCSes). First, a total of 224 commonly used and newly developed features were extracted from the center of pressure (CoP) data for each participant, respectively. Then, regressors were employed in order to map these features to the evaluation scores given by physical therapists, which include the total score in Mini-Balance-Evaluation-Systems-Tests (Mini-BESTest) and its sub-scores on BCSes, namely anticipatory postural adjustments (APA), reactive postural control (RPC), sensory orientation (SO), and dynamic gait (DG). Their scoring ranges should be 0-28, 0-6, 0-6, 0-6, and 0-10, respectively. The results show that their minimum mean absolute errors from AI estimation reach up to 2.658, 0.827, 0.970, 0.642, and 0.98, respectively. In sum, our study is a preliminary study for assessing BCSes based on AI, which shows its possibility to be used in the clinics in the future.

中文翻译：

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人工智能对平衡控制子系统的评估。

最新研究表明，基于人工智能（AI）的平衡性能评估是可行的。但是，余额控制非常复杂，需要不同的子系统参与，尚未单独进行评估。此外，这些研究仅对个人在有限等级中的平衡表现进行了分类。因此，在本研究中，我们尝试实现AI以精确评估不同类型的平衡控制子系统（BCSes）。首先，分别从每个参与者的压力中心（CoP）数据中提取出总共224种常用和新开发的特征。然后，使用回归分析器将这些特征映射到物理治疗师给出的评估分数，其中包括迷你平衡评估系统测试（迷你最佳）的总分及其在BCS上的子得分，即预期姿势调整（APA），反应性姿势控制（RPC），感觉定向（SO）和动态步态（DG）。他们的得分范围应分别为0-28、0-6、0-6、0-6和0-10。结果表明，根据AI估计，它们的最小平均绝对误差分别达到2.658、0.827、0.970、0.642和0.98。总而言之，我们的研究是一项基于AI评估BCS的初步研究，这表明它有可能在未来的临床中使用。结果表明，根据AI估计，它们的最小平均绝对误差分别达到2.658、0.827、0.970、0.642和0.98。总而言之，我们的研究是一项基于AI评估BCS的初步研究，这表明它有可能在未来的临床中使用。结果表明，根据AI估计，它们的最小平均绝对误差分别达到2.658、0.827、0.970、0.642和0.98。总而言之，我们的研究是一项基于AI评估BCS的初步研究，这表明它有可能在未来的临床中使用。