<div>Soft tissue manipulation is a widely used practice by manual therapists from a variety of healthcare disciplines to evaluate and treat neuromusculoskeletal impairments using mechanical stimulation either by hand massage or specially-designed tools. The practice of a specific approach of targeted pressure application using distinguished rigid mechanical tools to breakdown adhesions, scar tissues and improve range of motion for affected joints is called Instrument-Assisted Soft Tissue Manipulation (IASTM). The efficacy of IASTM has been demonstrated as a means to improve mobility of joints, reduce pain, enhance flexibility and restore function. However, unlike the techniques of ultrasound, traction, electrical stimulation, etc. the practice of IASTM doesn't involve any standard to objectively characterize massage with physical parameters. Thus, most IASTM treatments are subjective to practitioner or patient subjective feedback, which essentially addresses a need to quantify therapeutic massage or IASTM treatment with adequate treatment parameters to document, better analyze, compare and validate STM treatment as an established, state-of-the-art practice.</div><div><br></div><div>This thesis focuses on the development and implementation of Quantifiable Soft Tissue Manipulation (QSTM™) Technology by designing an ergonomic, portable and miniaturized wired localized pressure applicator medical device (Q1), for characterizing soft tissue manipulation. Dose-load response in terms of forces in Newtons; pitch angle of the device with respect to treatment plane; stroke frequency of massage measured within stipulated time of treatment; all in real-time has been captured to characterize a QSTM session. A QSTM PC software (Q-WARE©) featuring a Treatment Record System subjective to individual patients to save and retrieve treatment diagnostics and a real-time graphical visual monitoring system has been developed from scratch on WINDOWS platform to successfully implement the technology. This quantitative analysis of STM treatment without visual monitoring has demonstrated inter-reliability and intra-reliability inconsistencies by clinicians in STM force application. While improved consistency of treatment application has been found when using visual monitoring from the QSTM feedback system. This system has also discriminated variabilities in application of high, medium and low dose-loads and stroke frequency analysis during targeted treatment sessions.</div>

This thesis, structured in two parts, addresses a series of problems of relevance in the field of Spatial Geodesy. The first part delves into the application of satellite gravity data to enhance our understanding of water transport dynamics. Here, we present two significant contributions. Both are based on satellite gravity data but stem from different mission concepts with distinct objectives: time-variable gravity monitoring and high-resolution, accurate static geoid modelling. First, the fundamental notions about gravity are introduced and a brief summary is made of the different gravity satellite missions throughout history, with emphasis on the GRACE/GRACE-FO and GOCE missions, whose data are the basis of this work. The first application focuses on estimating water transport and geostrophic circulation in the Southern Ocean by leveraging a GOCE geoid and altimetry data. The Volume Transport across the Antartic Circumpolar Current is analyzed and the resulsts are validated validated using the in-situ data collected during the multiple campaigns in the DP. The second application uses time-variable gravity data from the GRACE and GRACE-FO missions to estimate the water cycle in the Mediterranean and Black Sea system, a critical region for regional climate and global ocean circulation. The analysis delves into the analysis of the different components of the hydrological cycle within this region, including the water flow across the Gibraltar Strait, examining their seasonal variations, climatic patterns, and their connection with the North Atlantic Oscillation Index. The second part of the thesis is more focused on data analysis, with the objective of developing mathematical methods to estimate the cross correlation function between two time series that are both unevenly spaced spaced (the sampling is not uniform over time) and observed at unequal time scales (the set of time points for the first series is not identical to the set of time points of the second series). Such time series are frequently encountered in geodetic surveys, especially when combining data from different sources. The estimation of the the cross correlation function for these time series presents unique challenges and requires the adaptation of traditional analysis methods designed for evenly spaced and synchronized time series. The two main contributions in this context are: (i) the study of the asymptotic properties of the Guassian Kernel estimator, that is the recommended estimator for the cross correlation function when the two time series are observed at unequal time scales; (ii) an extension of the stationary bootstrap that allows to construct bootstrap-based confidence intervals for the cross correlation function for unevenly spaced time series not sampled on identical time points.