The founders of Momo Medical envisioned a health care product that would help nurses worldwide with pressure ulcer prevention. Pressure ulcers are a chronic wound that affects the skin of patients who do not regularly change bed posture. As it currently stands, nurses lack the manpower and time to change the postureof patients in a timely manor. Thus, a goal was set to create a product that would reduce this strain on nurses.The goal was to design a non-intrusive product that keeps track of the posture of a patient on a hospital bed, and send an alarm if this has not changed in a set amount of time. A constraint with this was that the product has to lay directly underneath the mattress of the patient. Furthermore, the product has to make use of as few sensors as possible. With these constrains, the project group was divided in three subgroups: the sensor, the algorithm, and the testing subgroup.This report will focus on the design, implementation, and evaluation of the algorithm. The algorithm has to be designed to process the sensor data and return, with 90% accuracy, the posture of the patient. It also has to be able to determine with 99% accuracy whether or not there is a patient on the bed. An optional objective was to design an algorithm to determine the heart and respiration rate of the patient, with 80% accuracy, but this was of lower priority. It was soon decided to give priority to posture detection and not further investigate detection of the heart and respiration rate.In the final product, the information about the patient’s posture will be displayed in a graphical user interface (GUI). This GUI has to be user-friendly and intuitive to use by the nurses. During the prototyping phase, however, a different GUI was used, one that displayed various debugging information. This prototype GUI was necessary to test the various algorithms.Several different mathematical models have been investigated, implemented, and tested. These models range from taking the variance of the sensor data, to deploying a Fourier transform on a polynomial curve based on the sensor data. With preliminary test data, an accuracy of 93% was achieved using a neural network. With the final testing data however, only the neural network reached the required accuracy of 90%, though only just, at 90.8%. With future iterations of the algorithm, more research and data collection is advised, as neural networks work better with larger data sets.

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.

In this thesis, a test device is designed for the company Momo Medical. Momo Medical is developing a system that can be used to prevent pressure ulcers and adding more functionality is being investigated. For this system they need a device to test the functionality of the six piezoelectric sensors that are used in their product. The sensors’ response to a known pulse needs to be tested in order to give a pass/fail indication of the sensor quality. First, different ways of testing the sensors are investigated. Based on the results of the investigation, a final test setup is chosen and characterized. The test system developed in this thesis is based on a pneumatic setup, using a solenoid valve to control well-defined air pulses directed towards the sensors. Due to the addition of a reference load cell with custom designed read-out electronics, the device is able to test all six sensors one at the time and provides detailed feedback about the individual sensor quality. The test is performed using GUI-based software written in MATLAB, connected to a microcontroller. The software offers a broad variety of settings and can be configured according to Momo Medical’s wishes. After configuration, the test can be performed at the click of a button. The standard deviation of the device precision over three hours is sigma = 4.16, which equals a variation of cv = 0.82% of the mean μ = 504.8. This easily satisfies the requirements set byMomoMedical.

Current hospital protocols dictate patients be turned at least every three hours in the effort of preventing pressure ulcers. To reduce the workload of nurses, Momo Medical has created an embedded sensing device to track the patient's posture and notify nurses when it is time to turn them. The challenge presented and the focus of this thesis is classifying the posture of the patient based on the sensor data sampled, specifically, utilizing neural networks on an embedded platform. Furthermore an optimization inspired by recurrent neural networks and ensemble neural networks is proposed, implemented, and compared against vanilla neural networks and pruned variants.