Medical diagnosis and healthcare recommendations are often challenging and require the correlation between different health-related inputs from the subject. On the one hand, some of the biometric data inputs are only possible to be determined by 24/7 monitoring, such as physical activity tracking, sleep quality or food intake nutritional information and quantity estimation. On the other hand, it is a big data problem as the correlation between all the inputs is rather challenging and time-consuming to be done by a human being.This work presents the design of wearable wristband electronic device, capable of continuous monitoring of several biometric inputs. The device connects to the Internet via smartphone and sends the data to a server in a secure way, using proper authentication and personal data protection. The biometric data will be available for physicians and at the same time a machine learning algorithm will elaborate and send healthcare related recommendations to the user.The wristband device is capable of tracking the physical activity, sleep quality and food intake of the subject. This is be done by several built-in sensors such as accelerometer, gyroscope, heart-rate sensor and digital camera. The accelerometer is be used to track the physical activity and the gyroscope detects wrist motion in order to recognize when the subject is eating and count the taken bites. Furthermore, the heart-rate sensor detects stress situations and the built-in camera takes a picture of the food. Thus, the picture is send to the smartphone via Bluetooth and then a computer vision algorithm is used to recognize the food.The designed smart wristband circuit is first tested on a protoboard and then on an Arduino nano board. After that, the circuit is fabricated on a RF4 substrate PCB in order to test the final design. After verification, the circuit is finally fabricated on a flexible circuit, which concludes with the wristband prototype design, fabrication and verification.
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