Computerized compliance of Personal Protective Equipment (PPE) is an emerging topic in academic literature that aims to enhance workplace safety through the automation of compliance and prevention of PPE misuse (which currently relies on manual employee supervision and reporting). Although trends in the scientific literature indicate a high potential for solving the compliance problem by employing computer vision (CV) techniques, the practice has revealed a series of barriers that limit their wider applications. This article aims to contribute to the advancement of CV-based PPE compliance by providing a comparative review of high-level approaches, algorithms, datasets, and technologies used in the literature. The systematic review highlights industry-specific challenges, environmental variations, and computational costs related to the real-time management of PPE compliance. The issues of employee identification and identity management are also discussed, along with ethical and cybersecurity concerns. Through the concept of CV-based PPE Compliance 4.0, which encapsulates PPE, human, and company spatio-temporal variabilities, this study provides guidelines for future research directions for addressing the identified barriers. The further advancements and adoption of CV-based solutions for PPE compliance will require simultaneously addressing human identification, pose estimation, object recognition and tracking, necessitating the development of corresponding public datasets.

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.

In the process of biogas production, various types of substrates with suitable energy potential are utilized to generate biogas in plants designed for cogeneration (CHP) of electricity and heat. This paper presents a literature review focused on different substrates involved in biogas production, emphasizing their optimization potential. Data for this research were gathered through a comprehensive review of scientific and scholarly literature from global databases. The study examines the biogas production capabilities of various feedstocks employed in cogeneration plants, highlighting the energy potential of substrates, including livestock byproducts such as liquid and solid manure, energy crops, organic waste from the food and slaughterhouse industries, as well as municipal wastewater and solid organic waste. Furthermore, we conducted a practical case study in the municipality of Čačak, which provides valuable insights into effective practices and strategies that can be broadly applied to enhance biogas production in similar contexts. The findings reveal significant variations in biogas production potential among different substrates, emphasizing the importance of strategic selection and management practices. This study contributes to the field by providing a clearer understanding of the substrate optimization process and practical insights that can inform the development of more effective biogas production strategies in local municipalities.

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.

In this paper, we present a method for determining the mobility of the spinal column using a network of sensors. The sensors consist of accelerometers and gyroscopes, and mutual communication is accomplished using a I2C bus. The main sensor node collects data from all the sensors and sends them to a computer using Bluetooth communication. The collected data is then filtered and converted to the values of the angles that are of interest to quantify the movement. The experimental part of this work method is applied to determine the range of motion of patients in the Clinical Center in Kragujevac.

In this study we modeled a patient specific 3D knee after anterior cruicate ligament (ACL) reconstruction. The purpose of the ACL reconstruction is to achieve stability in the entire range of motion of the knee and the establishment of the normal gait pattern. We present a new reconstruction technique that generates patient-specific 3D knee models from patient?s magnetic resonant images (MRIs). The motion of the ACL reconstruction patients is measured by OptiTrack system with six infrared cameras. Finite element model of bones, cartilage and meniscus is used for determination stress and strain distribution at different body postures during gait analysis. It was observed that the maximum effective von Mises stress distribution up to 8 MPa occurred during 30% of the gait cycle on the meniscus. The biomechanical model of the knee joint during gait analysis can provide insight into the underlying mechanisms of knee function after ACL reconstruction.