# Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review
## 4 Emerging Applications  
### Guesture Sensing  
In addition to the various healthcare applications using Doppler radar [78; 79], the radar-related technologies find applications in gesture sensing for human-machine interaction. The interaction between human and smart phones is mainly based on push-button or touch screen technologies, which are reliable and widely perceived as the standard features of the smart phones. However, as smart phones and other smart devices such as tablets are being increasingly used in our daily lives, we are seeing limitations in the interaction interface. For example, we may want to change the music or pick up a phone when we are in the kitchen busy with cooking or cleaning dishes, or we need to change the radio channel or tune up volume while we are driving. In all these scenarios, pushing the touch screen is difficult and can be distracting. As the advancement of technologies, camera-based video technology has emerged as a new gesture sensing approach that serves as a great supplementation to the traditional human-computer interaction methods. However, camera-based gesture sensing has several inherent drawbacks. First, gesture sensing using camera is based on video processing of the recorded digital frames, which usually requires powerful process unit. It is therefore not power efficient. Moreover, many mobile platforms does not have powerful processing unit and they are often powered by battery pack, which does not allow them to use camera for gesture sensing. Second, some of the existing camera approaches require special types of camera. For instance, the camera system used in Microsoft's Kinect sensor is an array of cameras that capture the depth map of the action target. It is not only expensive but also bulky. Third, the optical camera is sensitive to the ambient light. Gesture sensing won't work in weak light or darkness. Forth, optical signal cannot penetrate obstacles such as pocket cloth between the phone and the band [80]. As wireless power transfer (WPT) and near field communication (NFC) using inductive coils are standardized and have become competitive features for smart phones in recent years, it also opens up new opportunities to use inductive coupling to develop new human-machine interface [21]. Either WPT or NFC is based on the electromagnetic coupling between the planar coils. In WPT or NFC, there are coils in both the base station and the mobile device. The coil in the base station acts as the transmitter antenna that transfer power to the receive coil antenna in the mobile device via inductive coupling. In interactive sensing, instead of using the coils for power transfer or conveying message, the coil sends alternating magnetic field to the human hand and the hand motion alternates the field to interact with the mobile device. The hand movement in the magnetic field of the coil in front of the  
Figure 19: The location of the \(I/Q\) trajectory changes as the distance between radar and target changes. The distance change is realized by antenna beam-steering. mobile device changes the coil's conductivity distribution, which lead to the change of the effective coil impedance. The impedance change will result in a drift in the resonant frequency, which can be measured using an LC coupled oscillator structure. Thus, the WPT or NFC coil can detect hand movements, which makes it possible for use to interact with the smart device without touching them. Besides the sensing hardware, machine learning and gesture recognition algorithms are also important for successful interactive sensing. As gesture differs from one person to another, machine learning can be employed as an important approach to improve the accuracy and response time in gesture recognition. Based on the user's specific hand movements, parameters in digital signal processing and hardware configuration could be adjusted specifically so as to quickly predict the incoming gesture command.