###### Contents  
* 1 Introduction
* The evolution of the Internet of Things (IoT) network has made intelligent devices more available, which offers more possibilities to facilitate people's lives [1; 2]. In aging societies, one focus of smart infrastructure field is the assistance of the elderly and the disabled by using advanced IoT devices. Therefore, there is a strong demand for robots to tackle problems that resulted from the aging population, such as the lack of caregivers for nursing and accompanying of the elderly, and are promoting the development of nursing-care assistant robots [3]. Providing more natural and intelligent interaction modes [4; 5] with the nursing-care assistant robots [6] is one of the frontiers of smart infrastructure development [7]. Hand gesture recognition paves an appropriate way [8] to obtain people's intention for the control of smart devices, and some progress has been made in previous studies. Elderly people can use smart IoT devices to express intention by making corresponding gestures, so as to control various smart devices remotely [9] at home [10]. Therefore, natural human-robot interaction based on hand gestures [11] will become a popular research topic in the near future. Hand gestures are the most common means for nonverbal communication [12]. Generally speaking, gestures are divided into two types: static gestures and dynamic gestures. The former mainly focuses on the finger's flex angles and poses [13; 14], while the latter pays more attention to the hand motion trajectory (HMT) [15]. In previous studies, sensors for the above two types of gesture recognition mainly referred to two categories: image-based sensors [16] and non-image based sensors [17]. Most previous studies of static hand gestures recognition have used non-image based sensors (integrated in wearable gloves and bands [14]), while the studies of HMT gesture recognition are based on fixed image-based sensors (such as using in-depth information provided by Kinect [18]). Inertial sensors are commonly applied in the field of non-image based HMT gesture recognition. [PERSON] et al. and [PERSON] et al. used the accelerometer inertial sensor for the HMT gesture recognition with a mean recognition accuracy of 95.6% and 98.9% [19; 20]. However, their methods were based on the feature of acceleration, which was susceptible to the sensor's posture. Besides, the acceleration is not as intuitive as the velocity or displacement when representing a trajectory gesture, which might further limit the system's performance on more diverse and complex gestures. In recent years, significant efforts have been devoted to developing the image-based sensors for HMT gesture recognition. [PERSON] et al. used the Kinect sensor to achieve the recognition of static and dynamic hand gesture recognition in real time, and achieved an average accuracy of 92.4% [21]. [PERSON] et al. proposed an approach for continuous hand trajectory recognition based on the depth data collected by the Kinect2 sensor [22]. In addition, [PERSON] et al. proposed a novel system for dynamic continuous hand gesture recognition based on a frequency-modulated continuous wave radar sensor [23], which achieved a high recognition rate of 96%. However, the above methods for the dynamic gesture recognition have to rely on the position-fixed sensors, which limits the spatial flexibility of gesture actions and is not a suitable human-robot interaction [24] mode for the elderly. What's more, there is a study on gesture recognition without spatial position restriction. [PERSON] et al. recovered the full three-dimensional (3D) pose of the user's hand using a wrist-worn sensor [25]. It should be pointed out that the work of [PERSON] et al. is only effective for static finger gesture recognition, and cannot achieve the HMT gesture recognition.