Access to safe drinking water is an important health factor in many developing countries. Studies have shown that unsafe drinking water and poor sanitation practices leads to diarrheal disease, which is one of the leading causes of death of children under five in developing countries. Provision and proper use of household water filters have been shown to effectively improve health. This thesis is focused on the refinement and validation of algorithms for data collected from pressure transducer sensors that are used in household water filters (the Vestergaard Frandsen LifeStraw Family 2.0) deployed in Rwanda by the social enterprise DelAgua Health. Statistical and signal processing techniques were used to detect the use of the LifeStraw water filters and to estimate the amount of water filtered at the time of usage. An algorithm developed by Dr. Carson Wick at Georgia Institute of Technology was the baseline for the analysis of the data. The algorithm was then refined based on data collected in the SweetLab at Portland State University, which was then applied to field data. Laboratory results indicated that the mean error of the improved algorithm is 11.5% as compared with the baseline algorithm mean error of 39%. The validation of the algorithm with field data yielded a mean error of 5%. Errors may be attributed to real-world behavior of the water filter, electronic noise, ambient temperature, and variations in the approximation made to the field data. This work also presents some consideration of the algorithm applied to soft-sided water backpacks.