Medical Informatics and Computer Science Laboratory, Integrated Research Centre, University Campus Bio-Medico of Rome, Via Alvaro del Portillo 21, Rome, Italy.
Artif Intell Med. 2010 Sep;50(1):55-61. doi: 10.1016/j.artmed.2010.04.008. Epub 2010 May 26.
Recent research has successfully introduced the application of robotics and mechatronics to functional assessment and motor therapy. Measurements of movement initiation in isometric conditions are widely used in clinical rehabilitation and their importance in functional assessment has been demonstrated for specific parts of the human body. The determination of the voluntary movement initiation time, also referred to as onset time, represents a challenging issue since the time window characterizing the movement onset is of particular relevance for the understanding of recovery mechanisms after a neurological damage. Establishing it manually as well as a troublesome task may also introduce oversight errors and loss of information.
The most commonly used methods for automatic onset time detection compare the raw signal, or some extracted measures such as its derivatives (i.e., velocity and acceleration) with a chosen threshold. However, they suffer from high variability and systematic errors because of the weakness of the signal, the abnormality of response profiles as well as the variability of movement initiation times among patients. In this paper, we introduce a technique to optimise onset detection according to each input signal. It is based on a classification system that enables us to establish which deterministic method provides the most accurate onset time on the basis of information directly derived from the raw signal.
The approach was tested on annotated force and torque datasets. Each dataset is constituted by 768 signals acquired from eight anatomical districts in 96 patients who carried out six tasks related to common daily activities. The results show that the proposed technique improves not only on the performance achieved by each of the deterministic methods, but also on that attained by a group of clinical experts.
The paper describes a classification system detecting the voluntary movement initiation time and adaptable to different signals. By using a set of features directly derived from raw data, we obtained promising results. Furthermore, although the technique has been developed within the scope of isometric force and torque signal analysis, it can be applied to other detection problems where several simple detectors are available.
最近的研究成功地将机器人技术和机电一体化应用于功能评估和运动治疗。等长条件下运动起始的测量在临床康复中得到广泛应用，其对人体特定部位功能评估的重要性已得到证明。自愿运动起始时间的确定，也称为起始时间，是一个具有挑战性的问题，因为特征运动起始的时间窗口对于理解神经损伤后的恢复机制特别重要。手动确定以及这是一项麻烦的任务，也可能引入疏忽错误和信息丢失。
最常用的自动起始时间检测方法将原始信号或一些提取的措施（例如速度和加速度）与选定的阈值进行比较。然而，由于信号的弱点、响应曲线的异常以及患者之间运动起始时间的可变性，它们存在高度的可变性和系统误差。在本文中，我们介绍了一种根据每个输入信号优化起始检测的技术。它基于一个分类系统，使我们能够根据直接从原始信号中得出的信息，确定哪种确定性方法能提供最准确的起始时间。
该方法在注释的力和扭矩数据集上进行了测试。每个数据集由从 96 名患者的八个解剖区采集的 768 个信号组成，这些患者执行了与常见日常活动相关的六项任务。结果表明，所提出的技术不仅提高了每种确定性方法的性能，而且提高了一组临床专家的性能。
本文描述了一种检测自愿运动起始时间的分类系统，该系统具有适应性，可以应用于不同的信号。通过使用一组直接从原始数据中提取的特征，我们获得了有希望的结果。此外，尽管该技术是在等长力和扭矩信号分析的范围内开发的，但它可以应用于其他存在多个简单检测器的检测问题。
