PDF(Pubmed)
Abstract：
(1) Background: The objective of this study was to recognize tai chi movements using inertial measurement units (IMUs) and temporal convolutional neural networks (TCNs) and to provide precise interventions for elderly people. (2) Methods: This study consisted of two parts: firstly, 70 skilled tai chi practitioners were used for movement recognition; secondly, 60 elderly males were used for an intervention study. IMU data were collected from skilled tai chi practitioners performing Bafa Wubu, and TCN models were constructed and trained to classify these movements. Elderly participants were divided into a precision intervention group and a standard intervention group, with the former receiving weekly real-time IMU feedback. Outcomes measured included balance, grip strength, quality of life, and depression. (3) Results: The TCN model demonstrated high accuracy in identifying tai chi movements, with percentages ranging from 82.6% to 94.4%. After eight weeks of intervention, both groups showed significant improvements in grip strength, quality of life, and depression. However, only the precision intervention group showed a significant increase in balance and higher post-intervention scores compared to the standard intervention group. (4) Conclusions: This study successfully employed IMU and TCN to identify Tai Chi movements and provide targeted feedback to older participants. Real-time IMU feedback can enhance health outcome indicators in elderly males.
摘要：
（1）背景：本研究的目的是使用惯性测量单元（IMU）和时间卷积神经网络（TCN）识别太极拳运动，并为老年人提供精确的干预措施。(2)研究方法:本研究包括两个部分:首先,70名熟练的太极拳练习者被用于动作识别；其次，60名老年男性被用于一项干预研究。IMU数据是从熟练的太极拳从业者那里收集的，构建和训练TCN模型以对这些运动进行分类。将老年参与者分为精准干预组和标准干预组,前者每周接收实时IMU反馈。测量的结果包括余额，握力,生活质量,和抑郁症。（3）结果：TCN模型在识别太极拳运动方面表现出很高的准确性，百分比从82.6%到94.4%不等。经过八周的干预，两组的握力均有显著改善,生活质量,和抑郁症。然而,与标准干预组相比,只有精准干预组的平衡性显著提高,且干预后评分较高.（4）结论：本研究成功使用IMU和TCN来识别太极拳运动，并为老年参与者提供有针对性的反馈。实时IMU反馈可以增强老年男性的健康结果指标。
