This paper establishes an integrated framework combining self-induced vibration measurements with deep learning for vibration-based remaining useful life (RUL) prediction of mechanical frame structures in mobile robots. The main innovations comprise (1) a self-induced vibration excitation system that utilizes the robot’s drive wheels to generate controlled mechanical oscillations, using a five-sensor micro-electro-mechanical system (MEMS) accelerometer array to capture non-uniform vibration mode shapes across the robot’s structure, and (2) a processing pipeline for RUL prediction using accelerometer data and early feature fusion in two machine-learning models (long short-term memory (LSTM) and a convolutional neural network (CNN)). Our research methodology includes (i) modal analysis to identify the robot’s natural frequencies, (ii) verification platform evaluation, comparing low-cost MEMS accelerometers against a reference integrated electronic piezoelectric (IEPE) accelerometer, demonstrating industrial-grade measurement quality (coherence > 98%, uncertainty 4.79–7.21%), and (iii) data-driven validation using real data from the mechanical frame, showing that the LSTM model outperforms the CNN with a 2.61× root-mean-square error (RMSE) improvement (R² = 0.99). Our solution demonstrates that early feature fusion provides sufficient information to model degradation and detect faults early at a lower cost, offering a feasible alternative to classical maintenance procedures through combined hardware validation and lightweight software suitable for Industrial Internet-of-Things (IIoT) deployment.

In this paper, we present a mechatronic platform that must be used for the handling of thermally processed samples using laser equipment, after which it is cooled at low temperatures. In addition to the laser and cryogenic equipment, the mechatronic platform includes one robotic arm (with a new modular structure that allows it to adapt to different working places) for sample transfer between storage areas, a controlling system for the robotic arm based on a new haptic device with physical feedback, a laser system, a cryogenic system, and an optical thermal processing measurement system. A new VR application enables remote control of a robotic arm, ensuring user safety using a haptic device based on the VR model. We exemplified the process of manufacturing the parts for the robotic arm and glove using a 3D printing method.

Functional electrical stimulation (FES) is an effective method for correcting foot drop and facilitating forefoot elevation during walking. As a subset of neuromuscular electrical stimulation (NMES), FES enhances functional movements by stimulating muscles to compensate for neurological deficits. Recent technological advancements focus on developing more compact, efficient, and customizable devices that cater to individual patient needs, improving rehabilitation outcomes. This study developed a hybrid device combining electrical stimulation with ankle-foot orthoses (AFO) to enhance gait rehabilitation. A customized stimulation unit with adjustable parameters was created to deliver electrical impulses. An Arduino nano microcontroller was programmed to process data from an MPU6050 sensor, which measures accelerations and ankle angles (plantar flexion and dorsiflexion). This setup allows for precise control of stimulation patterns, offering a cost-effective and adaptable solution for patients with foot drop, aligning with current scientific efforts to enhance gait rehabilitation technologies.

This paper presents a gripper, part of the mechatronic system for positioning a parallelepiped sample from a cryogenic treatment system to the devices for evaluating and investigating the properties arising from the application of heat treatment. The system is part of a complex educational framework that enables students to research the effects of combined treatments, where a component is selectively heat-treated in specific areas using a laser system, followed by cooling the entire component with a cryogenic system. Also, the investigation methodology performed by students is illustrated. The system allows structural changes to be investigated with other methods, and the paper exemplified the analysis of the material using the SEM-EDX and XRD systems.