Problem Definition This bachelor final project was an assignment given by the start up Momo Medical named: "Improving health care with technology". This project was to use their existing technology and ideas to create a system to prevent pressure ulcers. Pressure ulcer are the medical condition that happens when a patient is left too long in the same position in bed. In order to combat this the nurses turn the patient onto a new posture every 3 to 4 hours. To improve upon this Momo Medical decided to built a sensor pad that has to be placed under the mattress in order to track the patients bed posture and to alarm the nurses when a patient has been on a side too long. The project group was divided into three parts: the sensor group who would look at the sensor pad, the algorithm group who would use the data from the sensor pad to determine the bed posture and the test group that had to built a system that could verify the algorithm. This report will talk about the test group. Based upon the data that Momo Medical acquired it was also possible to acquire the heart rate and the respiratory rate from the sensor data. A way to acquire this data was also looked at. The design requirement that were set were 95% accuracy for bed posture and 90% for respiratory rate and heart rate. Design Description In order achieve these requirements a few different types of reference signals were looked at that could be used in order to detect the bed posture, respiratory rate and heart rate. In the end an accelerometer and an commercial grade ECG chest strap were chosen. The accelerometer could measure the patients orientation and thus his bed posture. When the accelerometer is attached to the chest it is also able to measure the chest compressing as a result of breathing. The ECG chest strap was chosen as this gave a good platform upon which the accelerometer and the micro controller could be attached. Evaluation A prototype was built upon our design and it was tested in different situations. In the end the accuracy for the bed posture detection was 99%, the accuracy of the respiratory rate was 91% and of the heart rate it was 95%. With these accuracies it meant that the design requirements were met. The disadvantage was that the system influences the sensor pad while the patient is wearing the prototype. Another disadvantage was that due to the low sampling speed it was not possible to attach the heart rate receiver to the testing group's prototype, but had to be attached to the sensor group's prototype. These two disadvantages could be improved in the future by making the prototype smaller and faster.

Power analysis can be used to retrieve key information as secure systems leak data-dependent information over side channels. A proposed solution to break the correlation between side channel information and secret information was to replace a vulnerable part of the cryptography implementation with a neural network. This uses the inherent properties of a neural network to disrupt the correlation by breaking the linear power characteristics assumed by leakage models. To test this neural network without physically creating a hardware implementation a simulation must be performed that provides both the data and the power information. Currently neural network simulators do not generate a power trace and analog circuit simulators generate more information traces than required increasing the simulation time. This thesis describes the creation of a complete SystemC spiking neural network model that generates both data and power information. The information generated by this model was compared and verified with results acquired by the Cadence Spectre  analog circuit simulation platform. The results indicate that the created SystemC SNN model works and generates comparable data and power traces as the Spectre simulator.