This paper concerns the kinematics and dynamics of an arm exoskeleton used for human rehabilitation. The biomechanics of the upper arm was studied, and the nine degrees of freedom model of upper arm was obtained using Denavit–Hartenberg notation. The mass and inertial parameters were obtained from recent literature, and these parameters were used for modelling human arm in SolidWorks and MATLAB-Simulink packages. The inverse kinematics of the arm exoskeleton was solved in the previous paper Winter (Biomechanics and motor control of human movement. Wiley, New York, 2009 , and this model was implemented in this study. The arm angular velocity profile was selected within the time and speed restriction. By comparing three variants of motion with visualization, we indicated the change of joints angles. Then, the torques in each arm joints with and without exoskeleton were calculated. The obtained results demonstrate the efficiency of the proposed approach that can be utilized to analyse the kinematics and dynamics of exoskeletons for the purpose of selection of their actuators.

Facial recognition technology has significantly advanced in recent years, with promising applications in fields ranging from security to consumer electronics. Its importance extends beyond convenience, offering enhanced security measures for sensitive areas and seamless user experiences in everyday devices. This study focuses on the development and validation of a facial recognition system utilizing a Haar cascade classifier and the AdaBoost machine learning algorithm. The system leverages characteristic facial features—distinct, measurable attributes used to identify and differentiate faces within images. A biometric facial recognition system was implemented on a Raspberry Pi microcomputer, capable of detecting and identifying faces using a self-contained reference image database. Verification involved selecting the similarity threshold, a critical factor influencing the balance between accuracy, security, and user experience in biometric systems. Testing under various environmental conditions, facial expressions, and user demographics confirmed the system’s accuracy and efficiency, achieving an average recognition time of 10.5 s under different lighting conditions, such as daylight, artificial light, and low-light scenarios. It is shown that the system’s accuracy and scalability can be enhanced through testing with larger databases, hardware upgrades like higher-resolution cameras, and advanced deep learning algorithms to address challenges such as extreme facial angles. Threshold optimization tests with six male participants revealed a value that effectively balances accuracy and efficiency. While the system performed effectively under controlled conditions, challenges such as biometric similarities and vulnerabilities to spoofing with printed photos underscore the need for additional security measures, such as thermal imaging. Potential applications include access control, surveillance, and statistical data collection, highlighting the system’s versatility and relevance.

Purpose: This paper presents an aspect of asset tracking and storage conditions. This paper aims to fill the gap in the development of Industry 4.0 in terms of fully digital asset tracking to be implemented by medium and large-size manufacturing and logistics facilities. The article presents an innovative technology for the remote monitoring of chemical raw materials, including fertilizers, during their storage and transport from the place of manufacture to the local distributor or recipient. Methods: The method assumes the monitoring and identification of special transport bags, so-called “big-bags,” through embedded RFID tags or LEB labels and monitoring the key parameters of their content, i.e., temperature, humidity, insolation, and pressure, using a measuring micro-station that is placed in the transported raw material. Results: The automation of inference based on the collected information about the phenomenon in question (the distribution of parameters: pressure, temperature, and humidity), and expert knowledge, allows the creation of an advisory system prototype indicating how to manage the measuring devices. Conclusions: No similar solution in the field of monitoring environmental parameters has been implemented in the Polish market. The developed system enables the monitoring of 10,000 pieces of big bags in at least 30 locations simultaneously.

(1) Background: The purpose of this study was to evaluate the analysis of measurements of bioelectric signals obtained from electromyographic sensors. A system that controls the speed and direction of rotation of a brushless DC motor (BLDC) was developed; (2) Methods: The system was designed and constructed for the acquisition and processing of differential muscle signals. Basic information for the development of the EMG signal processing system was also provided. A controller system implementing the algorithm necessary to control the speed and direction of rotation of the drive rotor was proposed; (3) Results: Using two muscle groups (biceps brachii and triceps), it was possible to control the direction and speed of rotation of the drive unit. The control system changed the rotational speed of the brushless motor with a delay of about 0.5 s in relation to the registered EMG signal amplitude change; (4) Conclusions: The prepared system meets all the design assumptions. In addition, it is scalable and allows users to adjust the signal level. Our designed system can be implemented for rehabilitation, and in exoskeletons or prostheses.

The aim of this work was to design and program a low-cost universal multichannel measurement card from scratch. The constructed device has analog inputs with the possibility of using them as differential inputs. This makes it possible to measure the analog signals for most of the available sensors. Thus, universality of the device is achieved. Simultaneously, the main assumption of the project and its novelty was to develop a measurement module. It is characterized by high measurement parameters, comparable to commercial products available on the market, with a very low production cost. The usability and assumed features of the measurement module were verified and tested using a functional generator and constructed test stand. During the tests, a sampling rate of at least 250 kS/s and a resolution of at least 14 bit were used. The module enables the acquisition of analog signals with voltages in the range of ±10 V and digital signals in the transistor–transistor logic (TTL) 5 V standard with a frequency of at least 250 kS/s. In addition, our device can be controlled via a computer, and data can be downloaded via the USB interface. It has 16 input channels with the possibility of differential measurements. The proposed solution is several times cheaper than commercial solutions while maintaining comparable parameters, as shown in the conclusion of the work.

This article presents a novel, innovative, open multi-domain platform for early warning against adverse events in reservoirs and watercourses, which can measure temperature, pH, redox, conductivity, turbidity, chlorophyll and phycocyanin. These parameters are key indicators of cyanobacteria bloom. This platform allows remote and distributed monitoring of important locations on lakes and rivers. The station's design enables the use of both wired sensors directly connected to the station and wireless data collection from locally dispersed measurement points that communicate with the station-buoy. The data aggregation system is open, and the technological solution of the station is universal, which means it can use different sensors for chemical and biological parameters that are required by, for example, the Water Framework Directive, from the market and industry standards. The platform also has built-in machine learning and data analysis mechanisms that can optimise the number of stations needed to achieve the desired level of data acquisition. The sensor dispersion and station autonomy ensure the flexibility and scalability of the measurements.