Search

Center-of-pressure (CoP) displacements play a key role in studies assessing postural stability. The accepted instrument to measure CoP trajectories is the force platform, but pressure-sensitive mats (PSMs) are an alternative composed of a matrix of sensitive cells. A typical cell comprises two electrodes with piezoresistive material in between, while a force platform has a force sensor at each of its corners. In this paper, we compare a homemade Velostat-based PSM and an affordable commercial mat with a commercial force platform in a test series with 42 healthy volunteers in single-legged trials (29 males, 13 females, height 1.74 (0.09) m, weight 74.3 (16.34) kg, age 31.21 (12.66) years). The aim of the research was to perform a preliminary study of the performance of our prototype to measure CoP, and more specifically, the standard deviation of the CoP path on both axes, the medial–lateral and anterior–posterior. We could thus discover several improvements for future clinical applications. The intraclass correlation coefficient (ICC) for agreement in the base experiment showed a moderate value for the prototype (0.38 to 0.63) and lower values for the commercial mat (0.11 to 0.12). However, we identified several factors that were relevant to improve ICC and reduce error by considering several processing options: (i) the known crosstalk problem between cells that appears in this kind of mats must be eliminated, (ii) the response time of the sensor has to be taken into account, and (iii) increasing the mat resolution also improves agreement. Therefore, as future work, we plan to test the improved version of the prototype in a clinical environment.

Sleep Apnea (SA) is a common disorder that affects approximately 2% of middle-aged women and 4% of middle-aged men. It is characterized by repetitive cessation of breathing during sleep. SA has significant health and social consequences such as daytime sleepiness, impaired quality of life, and in the worst case, myocardial infarction and sudden cardiac death. It has been estimated that approximately 80% of individuals with moderate to severe SA syndrome have not been diagnosed. The lack of patient sleep histories has caused low identification of SA and referral rates, especially in primary care facilities. Moreover, due to the inadequate prevalence of overnight polysomnography (PSG) as a standard clinical test of SA, patients suspected of having this sleep disorder have to wait several months for diagnosis and treatment. The costly and time-consuming nature of PSG and the lack of sleep clinics have created a demand for suitable home-based health monitoring devices. Over the years, several devices have been developed to monitor sleep unobtrusively, while an individual is lying in bed. However, most of these devices would either disrupt the sleep of the patient or be disrupted by the patient during routine bed sheet changes. Pressure measurement using a Pressure Sensitive Mat (PSM) enables a non-contact approach for monitoring patient vital signs such as respiration rate. The PSM has the potential to replace obtrusive breathing sensors in the sleep lab and to be used as a pre-screening tool for patients suspected of having sleep apnea. This thesis proposes multiple algorithms applicable to PSM in order to assess sleep quality. First, fusion techniques are proposed to extract a breathing signal from PSM. Second, a wide range of machine learning approaches including a simple threshold-based algorithm, a linear support vector machine (SVM) and two deep learning methods (i.e., a temporal convolutional network (TCN) and a bidirectional long short-term memory (BiLSTM) network) are compared to find a good- iii performing method for automatically detecting central sleep apnea (CSA) events from PSM signals. The results show that the accuracy of the model with the best performance is 95.1% and it is achieved by the BiLSTM network. Finally, by applying SVM, personalized systems are optimized to investigate long-term sleep pattern changes such as central apnea index (CAI), bed occupancy (BO), day-clock, and night-clock from previously recorded data.