Problem: Due to high rejection rates regarding prostheses’ use, the assessment of the amputee’s use of the prosthesis has become more critical. Today’s prosthesis research is limited to assessing a users’ performance to perform tasks in a controlled environment. Therefore, these studies cannot wholly assess how the prosthesis is used in the daily lives of amputees. Purpose: The purpose of this thesis project is to create, test, and examine the performance of methods that can be used to enhance prostheses' research and evaluation. Results: We have created several enhancements and extensions regarding prosthesis research and evaluation for three sensor scenarios. In the first scenario, we only use an accelerometer, which allowed us to create a new method for creating a vector magnitude (VM) that can show us what type of arm movement is made; additionally, we created a novel scoring system to determine the intensity of movements that are performed. In our second scenario, we used an additional gyroscope, which opened up possibilities for using more advanced Sensor Fusion (SF) techniques. We created an accurate tilt estimation algorithm that remains robust against high levels of gyroscope noise, accelerometer outliers, and measurement model violations. With the gyroscope, we were also able to create a VM from rotational velocity, which displays information about the arms’ rotational movement. Additionally, we created a novel scoring system that also shows the intensity of the performed movements. Our last scenario considers the use of two Inertial Measurement Units (IMUs); we present a novel algorithm for estimating the relative sensor orientation. This algorithm uses a joint kinematic estimation method that incorporates the connection between adjacent segments within a SF algorithm that remains accurate in the vicinity of common real-world disturbances, among which are; high levels of gyroscope noise, accelerometer outliers, and Soft-Tissue-Artifacts (STA). Conclusion: The best way to enhance the analysis for prostheses research and evaluation is to start incorporating gyroscopes into the research process. This can either be in the form of the single sensor case or the double sensor system. The additional gyroscope(s) will enable the use the SF techniques and methods as discussed in this report.

Objective: This work focuses on the feasibility of using Electrical Impedance Spectroscopy (EIS) to differentiate between cancerous and healthy tissues in real-time alongside the GI tract. This is to see whether it is possible to support surgeons when taking biopsies, or to see if it is possible to make biopsies obsolete with EIS. Methods: The study is limited to three tissue types representing the GI tract. These tissues are the esophagus, ileum, and colon tissue. Impedance measurements are taken with a 4-electrode probe on a Hewlett Packard 4192A LF HP Impedance Analyzer. All measurements are taken over a frequency range of 1 kHz to 7 MHz in 300 steps. Seven patients are included: three esophagus, two leum, and two colon. Forty-eight ex vivo measurements are taken; 32 are on healthy tissue and 16 on cancerous tissue. Almost all measurements are verified by histological assessment (golden standard). In this work, a combination of parameterization with a classification method is made to create a classification strategy. These can be listed as the Cole impedance model, the two-pole Cole impedance model, the two-pole Cole impedance model in combination with a Constant Phase Element (CPE), and PCA. The Cole impedance models are combined with thresholding, and the PCA is combined with a SVM. Results: The thresholding algorithm is created in combination with the α2 parameter (P < 0.05) from the two-pole Cole impedance model in combination with a CPE. The thresholding value is determined in a LOOCV approach via ROC curves created to search for the threshold that gives the most significant summation of sensitivity and specificity. In the combination of PCA and SVM, the PCA uses three principal components (containing 96.37% of the total variance), and the SVM applies an Radial Basis Function (RBF) kernel. For the 2-pole Cole impedance model with CPE in combination with thresholding, we find an accuracy 0.5208, sensitivity 0.4375, specificity 0.5625, Positive Predictive Value (PPV) 0.3333, Negative Predictive Value (NPV) 0.6666, and Mathews Correlation Coefficient (MCC) 0.0000. For the PCA in combination with the SVM, we find an accuracy of 0.4167, a sensitivity of 0.0000, specificity of 0.6250, PPV of 0.0000, NPV of 0.5556, and MCC -0.4082. Conclusion: It is concluded that we cannot create an algorithm that either supports surgeons or replaces a biopsy in the GI tract with the current setup. This is highly likely due to the extremely low amount of data that is included to train the algorithm.