This paper presents an energetically autonomous IoT sensor powered via thermoelectric harvesting. The operation of thermal harvesting is based on maintaining a temperature gradient of at least 26.31 K between the thermoelectric-generator sides. While the hot side employs a metal plate, the cold side is attached with a phase-change material acting as an effective passive dissipative material. The desired temperature gradient allows claiming power conversion efficiencies of about 26.43%, without efficiency reductions associated with heating and soiling. This work presents the characterization of a low-cost off-the-shelf thermoelectric generator that allows estimating the production of at least 407.3 mW corresponding to 2.44 Wh of available energy considering specific operation hours—determined statistically for a given geographic location. Then, the energy production is experimentally verified with the construction of an outdoor IoT sensor powered by a passively-cooled thermoelectric generator. The prototype contains a low-power microcontroller, environmental sensors, and a low-power radio to report selected environmental variables to a central node. This work shows that the proposed supply mechanism provides sufficient energy for continuous operation even during times with no solar resource through an on-board Li-Po battery. Such a battery can be recharged once the solar radiation is available without compromising sensor operation.

In this paper, an architecture based on computational intelligence for time series modeling is proposed to guarantee the automatic adjustability of trained models no matter the dynamic behavior of the modeled phenomena. The proposed method can assess the performance; and then proposes a maintenance routine for the time-series model. Thus, an auditor is devised to identify when a model must be updated before losing forecast performance. It has been determined that the MAPE (Mean Absolute Percentage Error) metric could not reveal changes in the model predicted curve contributing to invalidating the model, in particular if a non-stationary behavior is expected in the studied phenomena. Therefore, the novel rMAPE performance metric is proposed, so that the auditor does detect that the updating process does not achieve better performance; the system opts for replacing the time-series modeling techniques included in an available knowledge base. The intelligent system allows building time-series models automatically considering exogenous variables such as weather, calendar, and statistical transformations that can lead to the number of models required for a particular application. The proposed approach has been experimentally tested for power consumption and energy price via simulation. The forecasting results showed an improvement in the MAPE of up to 23% in the tests performed.

Occupancy control is a fundamental aspect of managing spaces and services effectively. It aims to ensure safety, compliance with regulations, emergency preparedness, and overall satisfaction for individuals and businesses. To align with the described need, this paper presents a computer vision based system for automatic people counting in gates. The system is divided in five stages: video capture, motion analysis, human detection, human tracking and people counting. An RGB camera captures the top-view image of the gate and analyze the change or movement in the objects in scene. When motion is detected, the frame is sent to the object detector, which is a convolutional neural network. Then, a tracking algorithm analyzes the movement patterns of people. According to the route, it is determined whether the person arrives or leaves and the count is updated. Two test scenarios are analyzed: the entry of a public bus and a building gate. The people detection module is tested, showing a mAP of 95.2% and a mean IoU (50%) of 55.9%. Also, the counting is tested showing an average precision of 96.8%, a recall of 92% and an F1-Score of 94.3%. Finally, the system performance is evaluated, showing an average processing time of 34.2 ms and 29.2 FPS.

Robot-Assisted Gait Training (RAGT) has emerged as a promising approach to improve motor recovery for stroke survivors. Among RAGT devices, exoskeletons offer precise joint actuation, but they are costly, mechanically complex and present risks related to joint misalignment. End-effector systems present a more affordable and simpler alternative, but face limitations in workspace and adaptability for assist-as-needed therapy. Cable-Driven End-Effector Gait Rehabilitation Robots (CDEGRs) combine the strengths of both approaches, offering low inertia, flexible configurations, and scalable designs. This review systematically examines the current landscape of CDEGRs, encompassing their kinematic classifications, control strategies, and platform configurations. Unlike previous reviews that broadly addressed exoskeletons or upper-limb rehabilitation devices, this work provides a focused and detailed analysis of lower-limb end-effector systems. In doing so, it identifies persistent gaps in design and control frameworks and highlights future research directions toward more efficient and clinically validated CDEGR architectures.

The application of intelligent systems in humanoid robots provides research and development alternatives, as is the case with human-robot interaction. This paper focuses on the design and implementation of an intelligent system in the NAO robot to plan and execute moves in the board game known as dominoes. This system uses the NAO robot’s vision to determine the distribution of tiles on the board, as well as those available in hand. The appropriate moves are planned using a computational intelligence technique, and a kinematics model executes them. The results show that the vision system has an average error of 5.62%, in addition to 3.37% for the decision-making system and 7.87% for the kinematics of the robot. This leads to the NAO robot being capable of making successful plays through the proposed system, with an average effectiveness of 83.15%.