Chiang Shu-Yin, Kan Yao-Chiang, Chen Yun-Shan, Tu Ying-Ching, Lin Hsueh-Chun
Department of Information and Telecommunications Engineering, Ming Chuan University, Gui-Shan, Taoyuan 333, Taiwan.
Department of Communications Engineering, Yuan Ze University, Chung-Li, Taoyuan 320, Taiwan.
Sensors (Basel). 2016 Dec 3;16(12):2053. doi: 10.3390/s16122053.
Ubiquitous health care (UHC) is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN). The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS) can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC.
泛在医疗保健（UHC）有利于患者通过在家自我管理来确保完成治疗性锻炼。我们设计了一种模糊计算模型，该模型能够在保护隐私的情况下识别UHC中的指定动作。这些动作由自行开发的身体运动传感器测量，该传感器结合了加速度计和陀螺仪芯片，使惯性传感节点符合无线传感器网络（WSN）的要求。研究了模糊逻辑过程，以计算包含静态姿势和动态动作必要特征的传感器信号。研究了这些特征的组合，并通过兼容的模糊规则选择了合适的特征集。然后，可以生成一个模糊推理系统（FIS），根据这些规则识别指定的动作。因此，我们在模型中实现了模糊推理系统和自适应神经模糊推理系统，以区分静态和动态动作。所提出的模型能够有效地达到指定活动的识别范围。此外，将物理治疗中的两种上肢屈曲练习应用于该模型，其中识别率可代表指定动作的通过率。最后，开发了一个基于网络的界面，以帮助远程测量UHC物理治疗中的动作。
