# Biometric Authentication and Verification for Medical Cyber Physical Systems
## 6 Device Authentication
### Bracolet (Hand) Wearable Authentication Devices  
Hand wearable devices are considered one of most available and obtainable devices in the market nowadays. That's because of the new generation of smart hand wearable devices (watches) that were developed lately in the market by several companies such as Samsung, Apple, Huawei, and many other tech companies. Most of these devices were designed to be connected with the smartphones. Some of these watches contain accelerometer and gyroscope sensors that might be used for authentication. Several researchers used the idea of the smart watches to create WBAN authentication systems [28; 29; 30; 25; 31].  
Some of the studies created a hand wearable device that contains eight sensors to read the internal impedance geometry of a user's arm using a tomography system [32]. A tomography system can analyze the internal structure of an object such as arm or leg by radiation and electricity. These waves go across the eight sensors and read the interior of the object. This research is an example of a wearable device that does not contain any verification system to validate if it is used by the actual owner. Some other studies presented an authentication scheme based on human body motions to verify a person wearing a wrist worn smart device using Samsung Galaxy Gear (SGG) [28].  
The device is a behavioral biometric-based authentication method using three simple natural gestures and one special: Arm up, arm down, forearm rotation about 90 degree clockwise, and the special one is drawing a circle. In addition, an application was implemented based on the Android platform to apply dynamic time warping method (DTW) and histogram. This research used a smart watch available in the market (SGG) to create a new software and implement it.  
One study used the LG G smart watch to perform gait-based biometric authentication [31]. The authentication models were generated using a variety of machine learning techniques, using the  
Figure 2: A biometric authentication system based off of head movement and auditory response, as presented in [26].  
accelerometer and gyroscope sensors on the watch, but evaluating each one separately. A subsequent study [25] by the same research group examined the simultaneous usage of both the accelerometer and gyroscope sensors, as well as physical activities other than walking to form the biometric signature. In total, this study evaluated the use of eighteen different physical activities (e.g., jogging, stair climbing) for biometric authentication.  
Another example is a paper that describes a bracelet authentication which verifies the users while they are using a computer by monitoring how they type on the keyboard or how the user moves the mouse [29]. The bracelet contains accelerometer and gyroscope sensors that transfer the information to the computer over short range radio. The computer used in this experiment is a Mac, and a Python script was written to capture two different movement sources from the bracelet.  
Another study on WBAN bracelet authentication systems used the bioimpedance technique to verify and authenticate that the WBAN sensors belong to the same body and identify who is wearing them [30]. Bioimpedance is a measurement of how tissue responds when exposed to an electrical current. A wrist-worn device was created with eight electrodes in contact with the wearer's wrist. With two of these electrodes, the device applies a small harmless current to the wrist so that it can measure bioimpedance. The wearable sensor was built on top of the shimmer platform which is an open-source, low-power wireless sensing platform.