This paper presents a novel Human Activity Recognition (HAR) framework using wearable sensors, specifically targeting applications in gait rehabilitation and assistive robots. The new methodology includes the usage of an open-source dataset. This dataset includes surface electromyography (sEMG) and inertial measurement units (IMUs) signals for the lower limb of 22 healthy subjects. Several activities of daily living (ADLs) were included, such as walking, stairs up/down and ramp walking. A new framework for signal conditioning, denoising, filtering, feature extraction and activity classification is proposed. After testing several signal conditioning approaches, such as Wavelet transform (WT), Principal Component Analysis (PCA) and Empirical Mode Decomposition (EMD), an autocepstrum analysis (ACA)-based approach is chosen. Such a complex and effective approach enables the usage of supervised classifiers like K-nearest neighbor (KNN), neural networks (NN) and random forest (RF). The random forest classifier has shown the best results with an accuracy of 97.63% for EMG signals extracted from the soleus muscle. Additionally, RF has shown the best results for IMU signals with 98.52%. These results emphasize the potential of the new framework of wearable HAR systems in gait rehabilitation, paving the way for real-time implementation in lower limb assistive devices.

Background subtraction method is widely used for vehicle detection. One of the issues in this method is to find a suitable and accurate background model th

Smart agricultural systems are continually evolving to provide high-quality planting and defend against threats such as climate change, which necessitate improved adaptation and resource allocation. IoT technology offers a cost-effective approach to monitoring and managing system performance. However, this approach faces challenges, including connectivity issues and complex decision-making. While researchers have studied these problems individually, no fully automated solution has addressed them simultaneously. There is still a need for an offline solution that manages multiple processes and reduces human error. This paper introduces an AI-powered edge computing system that serves as an early-warning solution for climate impacts. This system enables autonomous management through an Agentic AI model that observes, predicts, decides, and adapts. It provides a low-cost AIoT platform for data forecasting, classification, and decision-making, converting sensor data into actionable insights. The system integrates forecast evaluation with real-time data comparisons to optimize scheduling, efficiency, sustainability, and yields. Moreover, this solution is totally autonomous and independent of internet connectivity. Demonstrating its superior performance, it reduced errors by 50% and achieved an R-squared value of 0.985.

Accurate recognition of lower limb activities is essential for wearable rehabilitation systems and assistive robotics like exoskeletons and prosthetics. This study introduces SDALLE, a custom hardware data acquisition system that integrates surface electromyography sensors (EMGs) and inertial measurement sensors (IMUs) into a wireless, portable platform for locomotor monitoring. Using this system, data were collected from nine healthy subjects performing four fundamental locomotor activities: walking, jogging, stair ascent, and stair descent. The recorded signals underwent an offline structured preprocessing pipeline consisting of time-series augmentation (jittering and scaling) to increase data diversity, followed by wavelet-based denoising to suppress high-frequency noise and enhance signal quality. A temporal one-dimensional convolutional neural network (1D-TCNN) with three convolutional blocks and fully connected layers was trained on the prepared dataset to classify the four activities. Classification using IMU sensors achieved the highest performance, with accuracies ranging from 0.81 to 0.95. The gyroscope X-axis of the left Rectus Femoris achieved the best performance (0.95), while accelerometer signals also performed strongly, reaching 0.93 for the Vastus Medialis in the Y direction. In contrast, electromyography channels showed lower discriminative capability. These results demonstrate that the combination of SDALLE hardware, appropriate data preprocessing, and a temporal CNN provides an effective offline sensing and activity classification pipeline for lower limb activity recognition and offers an open-source dataset that supports further research in human activity recognition, rehabilitation, and assistive robotics.

The smart agriculture system requires high efficiency to automatically maximize crop yields and minimize losses. Wireless sensor networks (WSNs) are essential for maintaining system sustainability through sensing and connectivity. However, they encounter challenges related to cost, interoperability, and reliability. Efforts have been made to expand sensing capabilities while managing costs and addressing variability in sensor communication and power consumption. Despite these efforts, a comprehensive solution—especially for orchard fields—remains undeveloped. This study introduces a coordinated WSN design to optimize sensing and connectivity in agricultural fields. We employ an integrated sensing and connectivity (ISAC) strategy to create a complete solution. Our hybrid approach combines graphical computation with distance-vector algorithms for reliable, cost-effective deployment. Additionally, resilient connectivity is achieved through effective channel modeling and adaptive beamforming. The proposed method, combined with quantitative heterogeneous network selection using MLR-AHP, addresses interoperability issues and improves network resilience. Results indicate improved sensor placement and wireless ranking, even with only 5 nodes. The solution extends sensor battery life, maintains 99% coverage, and empirical tests validate its effectiveness for designing and deploying WSNs in orchard fields.