School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China.
Comput Intell Neurosci. 2018 Dec 30;2018:9861697. doi: 10.1155/2018/9861697. eCollection 2018.
As an advanced interaction mode, the gesture has been widely used for the human-computer interaction (HCI). The paper proposes a comfort evaluation model based on the mechanical energy expenditure (MEE) and the mechanical efficiency (ME) to predict the comfort of gestures. The proposed comfort evaluation model takes nineteen muscles and seven degrees of freedom into consideration based on the data of muscles and joints and is capable of simulating the MEE and the ME of both static and dynamic gestures. The comfort scores (CSs) can be therefore calculated by normalizing and assigning different decision weights to the MEE and the ME. Compared with the traditional comfort prediction methods based on measurement, on the one hand, the proposed comfort evaluation model makes it possible for providing a quantitative value for the comfort of gestures without using electromyography (EMG) or other measuring devices; on the other hand, from the ergonomic perspective, the results provide an intuitive indicator to predict which act has the higher risk of fatigue or injury for joints and muscles. Experiments are conducted to validate the effectiveness of the proposed model. According to the comparison result among the proposed comfort evaluation model, the model based on the range of motion (ROM) and the model based on the method for movement and gesture assessment (MMGA), a slight difference can be found due to the ignorance of dynamic gestures and the relative kinematic characteristics during the movements of dynamic gestures. Therefore, considering the feedback of perceived effects and gesture recognition rate in HCI, designers can achieve a better optimization for the gesture design by making use of the proposed comfort evaluation model.
作为一种高级交互模式,手势已广泛应用于人机交互(HCI)。本文提出了一种基于机械能消耗(MEE)和机械效率(ME)的舒适度评估模型,用于预测手势的舒适度。所提出的舒适度评估模型基于肌肉和关节数据考虑了十九块肌肉和七个自由度,能够模拟静态和动态手势的 MEE 和 ME。因此,可以通过归一化和为 MEE 和 ME 分配不同的决策权重来计算舒适度得分(CS)。与基于测量的传统舒适度预测方法相比,一方面,所提出的舒适度评估模型可以在不使用肌电图(EMG)或其他测量设备的情况下为手势舒适度提供定量值;另一方面,从人体工程学的角度来看,结果提供了一个直观的指标,可以预测哪种动作对关节和肌肉的疲劳或受伤风险更高。进行了实验以验证所提出模型的有效性。根据所提出的舒适度评估模型与基于运动范围(ROM)的模型和基于运动和手势评估方法(MMGA)的模型之间的比较结果,由于忽略了动态手势以及动态手势运动过程中的相对运动学特性,可能会发现细微的差异。因此,考虑到 HCI 中的感知效果和手势识别率的反馈,设计师可以通过利用所提出的舒适度评估模型来对手势设计进行更好的优化。