41:4·C.Wang et al. RF-Kinect.It is the first training-free and low-cost human body movement tracking system,including both the limb orientation and joint displacement,by leveraging multiple wearable RFID tags,and it overcomes many drawbacks on existing light-dependent works.2)We demonstrate that RF-Kinect could accurately track the 3D body movement other than simply tracking one joint on the body,with the minimum hardware requirements involving only a dual-antenna RFID reader and several low-cost wearable RFID tags.3)Instead of locating the absolute position of each joint for tracking,we regard the human body as the combination of several rigid bodies(i.e.,skeletons)and use a kinematic method to connect each skeleton as the human body model.Then,we exploit the features PDT and PDA to estimate the orientations of each skeleton and use the relative distances to measure the relationship between different skeletons for tracking.4)The fast adoption and low-cost deployment of RF-Kinect are also validated through our prototype implementation.Given the groundtruth from the Kinect 2.0 testbed,our systematic evaluation shows that RF-Kinect could achieve the average angle and position error as low as 8.7 and 4.4cm for the limb orientation and joints'position estimation,respectively. 2 RELATED WORK Existing studies on the gesture/posture recognition can be classified into three main categories: Computer Vision-based.The images and videos captured by the camera could truthfully record the human body movement in different levels of granularity,so there have been active studies on tracking and analyzing the human motion based on the computer vision.For example,Microsoft Kinect [8]provides the fine-grained body movement tracking by fusing the RGB and depth image.Other works try to communicate or sense the human location and activities based on the visible light [15,16,18].LiSense [20]reconstructs the human skeleton in real-time by analyzing the shadows produced by the human body blockage on the encoded visible light sources.It is obvious that the computer vision-based methods are highly light-dependent,so they could fail in tracking the body movement if the line-of-sight(LOS)light channel is unavailable.Besides,the videos may incur the privacy problem of the users in some sensitive scenarios.Unlike the computer vision-based approaches,RF-Kinect relies on the RF device,which can work well in most Non-line-of-sight(NLOS)channel environments.Moreover,given the unique ID for each tag,it can also be easily extended to the body movement tracking scenario involving multiple users. Motion Sensor-based.Previous research has shown that the built-in motion sensors on wearable devices can also be utilized for the body movement recognition [19,44].Wearable devices such as the smartwatch and wristband can detect a variety of body movements,including walking,running,jumping,arm movement etc., based on the accelerometer and gyroscope readings [22,23,33,40,45].For example,ArmTrack [30]proposes to track the posture of the entire arm solely relying on the smartwatch.However,the motion sensors in wearable devices are only able to track the movement of a particular part of the human body,and more importantly, their availability is highly limited by the battery life.Some academic studies [32]and commercial products(e.g, Vicon [6])have the whole human body attached with the special sensors,and then rely on the high-speed cameras to capture the motion of different sensors for the accurate gesture recognition.Nevertheless,the high-speed cameras are usually so expensive that are not affordable by everyone,and the tracking process with camera is also highly light-dependent.Different from the above motion sensor-based systems,RF-Kinect aims to track the body movement with RFID tags,which are battery-free and more low-cost.Moreover,since each RFID tag only costs from 5 to 15 U.S.cents today,such price is affordable for almost everyone,even if the tags are embedded into clothes Wireless Signal-based.More recently,several studies propose to utilize wireless signals to sense human gestures [10,17,25,35,37,38,42,43].Pu et al.[25]leverage the Doppler influence from Wi-Fi signals caused by body gestures to recognize several pre-defined gestures;Kellogg et al.[17]recognize a set of gestures by analyzing the amplitude changes of RF signals without wearing any device;Adib et al.[9]propose to reconstruct a human Proceedings of the ACM on Interactive,Mobile,Wearable and Ubiquitous Technologies,Vol.2,No.1,Article 41.Publication date:March 2018.41:4 • C. Wang et al. RF-Kinect. It is the first training-free and low-cost human body movement tracking system, including both the limb orientation and joint displacement, by leveraging multiple wearable RFID tags, and it overcomes many drawbacks on existing light-dependent works. 2) We demonstrate that RF-Kinect could accurately track the 3D body movement other than simply tracking one joint on the body, with the minimum hardware requirements involving only a dual-antenna RFID reader and several low-cost wearable RFID tags. 3) Instead of locating the absolute position of each joint for tracking, we regard the human body as the combination of several rigid bodies (i.e., skeletons) and use a kinematic method to connect each skeleton as the human body model. Then, we exploit the features PDT and PDA to estimate the orientations of each skeleton and use the relative distances to measure the relationship between different skeletons for tracking. 4) The fast adoption and low-cost deployment of RF-Kinect are also validated through our prototype implementation. Given the groundtruth from the Kinect 2.0 testbed, our systematic evaluation shows that RF-Kinect could achieve the average angle and position error as low as 8.7 ◦ and 4.4cm for the limb orientation and joints’ position estimation, respectively. 2 RELATED WORK Existing studies on the gesture/posture recognition can be classified into three main categories: Computer Vision-based. The images and videos captured by the camera could truthfully record the human body movement in different levels of granularity, so there have been active studies on tracking and analyzing the human motion based on the computer vision. For example, Microsoft Kinect [8] provides the fine-grained body movement tracking by fusing the RGB and depth image. Other works try to communicate or sense the human location and activities based on the visible light [15, 16, 18]. LiSense [20] reconstructs the human skeleton in real-time by analyzing the shadows produced by the human body blockage on the encoded visible light sources. It is obvious that the computer vision-based methods are highly light-dependent, so they could fail in tracking the body movement if the line-of-sight (LOS) light channel is unavailable. Besides, the videos may incur the privacy problem of the users in some sensitive scenarios. Unlike the computer vision-based approaches, RF-Kinect relies on the RF device, which can work well in most Non-line-of-sight (NLOS) channel environments. Moreover, given the unique ID for each tag, it can also be easily extended to the body movement tracking scenario involving multiple users. Motion Sensor-based. Previous research has shown that the built-in motion sensors on wearable devices can also be utilized for the body movement recognition [19, 44]. Wearable devices such as the smartwatch and wristband can detect a variety of body movements, including walking, running, jumping, arm movement etc., based on the accelerometer and gyroscope readings [22, 23, 33, 40, 45]. For example, ArmTrack [30] proposes to track the posture of the entire arm solely relying on the smartwatch. However, the motion sensors in wearable devices are only able to track the movement of a particular part of the human body, and more importantly, their availability is highly limited by the battery life. Some academic studies [32] and commercial products (e.g., Vicon [6]) have the whole human body attached with the special sensors, and then rely on the high-speed cameras to capture the motion of different sensors for the accurate gesture recognition. Nevertheless, the high-speed cameras are usually so expensive that are not affordable by everyone, and the tracking process with camera is also highly light-dependent. Different from the above motion sensor-based systems, RF-Kinect aims to track the body movement with RFID tags, which are battery-free and more low-cost. Moreover, since each RFID tag only costs from 5 to 15 U.S. cents today, such price is affordable for almost everyone, even if the tags are embedded into clothes. Wireless Signal-based. More recently, several studies propose to utilize wireless signals to sense human gestures [10, 17, 25, 35, 37, 38, 42, 43]. Pu et al. [25] leverage the Doppler influence from Wi-Fi signals caused by body gestures to recognize several pre-defined gestures; Kellogg et al. [17] recognize a set of gestures by analyzing the amplitude changes of RF signals without wearing any device; Adib et al. [9] propose to reconstruct a human Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 2, No. 1, Article 41. Publication date: March 2018