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The purpose of this study is the application of pressure sensors in diagnostics and evaluation of the accuracy diagnostics of lumbar disc herniation at levels L4/L5 and L5/S1 using the aforementioned platform. The motivation behind the idea to apply the pressure measurement platform is the fact that the motor weakness of plantar and dorsal flexia of the feet is one of the absolute indications for the operative treatment of patients with lumbar disc herniation at the indicated levels. In patients, MRI diagnosis of the lumbosacral spine served as the ground truth in the diagnosis of herniation at L4/L5 and L5/S1 levels. The inclusive criteria for the study were the proven muscle weakness based on manual muscle tests performed prior to surgery, after seven days of surgery and after physical therapy. The results obtained with the manual muscular test were compared with the results obtained using our platform. The study included 33 patients who met the inclusion criteria. The results of the measurements indicate that the application of our platform with pressure sensors has the same sensitivity diagnostics as a manual muscle test, when done preoperatively and postoperatively. After physical therapy, pressure sensors show statistically significantly better sensitivity compared to the clinical manual muscle test. The obtained results are encouraging in the sense that the pressure platform can be an additional diagnostic method for lumbar disc herniation detection and can indicate the effectiveness of operative treatment and physical therapy after operation. The main advantage of the system is the cost; the whole system with platform and sensors is not expensive.

This article presents geographic information system usage in transportation management for comfort calculations, analysing vehicle vibration data measured during patient transportation. The main goal of this article is to develop a methodology for automatic discomfort cause recognition (DCR) that may be tested under real life conditions. We analysed differences between uncomfortable locations detected by passengers during patient transportation (according to their subjective opinions) and uncomfortable locations detected by the measurement system. The measuring system is based on the three-axis accelerometer which is used to determine road comfort values for specific locations gathered by a GPS module. The results obtained were compared with data collected by passengers. During driving, when they experienced discomfort, passengers marked locations in near real time using the GIS. The data thus obtained were analysed with the data obtained by DCR. For the first time, the application of GIS provides analytical tools to create spatial data and define spatial data relations that determine comfort. Testing under real conditions, involving three separate cases, shows a high degree of correlation between the results. The proposed system allows dynamic comfort threshold criteria management and provides a visual representation of summarized tabular data.

The aim of this study was to create a decision support system for disc hernia diagnostics based on real measurements of foot force values from sensors and fuzzy logic, as well as to implement the system on Field Programmable Gate Array (FPGA). The results show that the created fuzzy logic system had the 92.8% accuracy for pre-operational diagnosis and very high match between the Matlab and FPGA output (94.2% match for pre-operational condition, and 100% match for the post-operational and after physical therapy conditions). Interestingly enough, our system is also able to detect improvements in patient condition after the surgery and physical therapy. The main benefit of using FPGAs in this study is to create an inexpensive, portable expert system for real time acquisition, processing and providing the objective recommendation for disc hernia diagnosis and tracking the condition improvement.

The aim of this research was to analyze objectively the process of disc herniation identification using Bayes Theorem. One of the symptoms of discus hernia is muscle weakness on the foot that is caused by displaced discs in the space of two vertebrae. This fact is used by experts in initial diagnosis of herniated discs and we used it to create non-invasive platform for the same purposes by measuring force values from four sensors placed on both feet (first, second, and fourth metatarsal head as well as the heel). Dataset consisted of several minute force recordings of 56 subjects with discus hernia and 15 healthy individuals during normal standing, standing on forefeet and heels. The subjects were diagnosed by a specialist with either L4/L5 or L5/S1 discus hernia. Collected recordings were processed in several steps including filtering, extraction of forefeet and heel recordings, classification of average values for forefeet, and heel sensors to the groups with or without foot muscle weakness. Application of Bayes Theorem on the attributes of interest showed average 78.3 accuracy with 62.6 sensitivity and 80.9 specificity, while application of naive Bayes Network showed average 83.1 accuracy with 57.6 sensitivity and 88.2 specificity. Very weak or no correlation was observed between gender and disc hernia diagnosis (or obesity type and disc hernia diagnosis). Obtained results show that this method can be used in initial screening of patients and be a supportive tool to doctors to send the same patients for further examination.

This paper presents the improved technique for classification of the type of lumbar discus hernia based on fuzzy logic. The reduced mobility of the foot is one of the symptoms of the disease that occurs because of the displaced discs in the space of two vertebrae. This fact was used for non-invasive discus hernia diagnosis by measuring force values from four sensors placed on both feet (first, second and fourth metatarsal head as well as the heel). Hardware and software systems were constructed for the doctor to perform the measurements and have a graphical representation during the measuring procedure. The procedure included measuring force values of 18 subjects during normal standing, standing on forefeet and heels. All subjects were diagnosed by a specialist with either L4/L5 or L5/S1 discus hernia. Filtering and further preprocessing of acquired values included separation of forefeet and heel segments that were used as inputs to fuzzy system. The results showed that the accuracy of such a fuzzy system was around 72%, and the proposed system correctly recognizes healthy individuals. Obtained information about forces on characteristic points on the foot represents useful data in diagnosis which further can be processed in order to be a supportive tool to doctors.