Background: With the projected upsurge in the percentage of people with some form of disability, there has been a significant increase in the need for assistive mobility devices. However, for mobility aids to be effective, such devices should be adapted to the user’s needs. This can be achieved by improving the confidence of the acquired information (interaction between the user, the environment, and the device) following design specifications. Therefore, there is a need for literature review on the adaptability of assistive mobility devices. Objective: In this study, we aim to review the adaptability of assistive mobility devices and the role of the internet of medical things in terms of the acquired information for assistive mobility devices. We review internet-enabled assistive mobility technologies and non–internet of things (IoT) assistive mobility devices. These technologies will provide awareness of the status of adaptive mobility technology and serve as a source and reference regarding information to health care professionals and researchers. Methods: We performed a literature review search on the following databases of academic references and journals: Google Scholar, ScienceDirect, Institute of Electrical and Electronics Engineers, Springer, and websites of assistive mobility and foundations presenting studies on assistive mobility found through a generic Google search (including the World Health Organization website). The following keywords were used: assistive mobility OR assistive robots, assistive mobility devices, internet-enabled assistive mobility technologies, IoT Framework OR IoT Architecture AND for Healthcare, assisted navigation OR autonomous navigation, mobility AND aids OR devices, adaptability of assistive technology, adaptive mobility devices, pattern recognition, autonomous navigational systems, human-robot interfaces, motor rehabilitation devices, perception, and ambient assisted living. Results: We identified 13,286 results (excluding titles that were not relevant to this study). Then, through a narrative review, we selected 189 potential studies (189/13,286, 1.42%) from the existing literature on the adaptability of assistive mobility devices and IoT frameworks for assistive mobility and conducted a critical analysis. Of the 189 potential studies, 82 (43.4%) were selected for analysis after meeting the inclusion criteria. On the basis of the type of technologies presented in the reviewed articles, we proposed a categorization of the adaptability of smart assistive mobility devices in terms of their interaction with the user (user system interface), perception techniques, and communication and sensing frameworks. Conclusions: We discussed notable limitations of the reviewed literature studies. The findings revealed that an improvement in the adaptation of assistive mobility systems would require a reduction in training time and avoidance of cognitive overload. Furthermore, sensor fusion and classification accuracy are critical for achieving real-world testing requirements. Finally, the trade-off between cost and performance should be considered in the commercialization of these devices.

Three-dimensional (3D) visual perception is pivotal for understanding surrounding environments in applications such as autonomous driving and mobile robotics. While LiDAR-based models dominate due to accurate depth sensing, their cost and sparse outputs have driven interest in camera-based systems. However, challenges like cross-domain degradation and depth estimation inaccuracies persist. This paper introduces BEVCAM3D, a unified bird’s-eye view (BEV) architecture that fuses monocular cameras and LiDAR point clouds to overcome single-sensor limitations. BEVCAM3D integrates a deformable cross-modality attention module for feature alignment and a fast ground segmentation algorithm to reduce computational overhead by 40%. Evaluated on the nuScenes dataset, BEVCAM3D achieves state-of-the-art performance, with a 73.9% mAP and a 76.2% NDS, outperforming existing LiDAR-camera fusion methods like SparseFusion (72.0% mAP) and IS-Fusion (73.0% mAP). Notably, it excels in detecting pedestrians (91.0% AP) and traffic cones (89.9% AP), addressing the class imbalance in autonomous driving scenarios. The framework supports real-time inference at 11.2 FPS with an EfficientDet-B3 backbone and demonstrates robustness under low-light conditions (62.3% nighttime mAP).

Human Activity Recognition (HAR) using wearable sensor data plays a vital role in health monitoring, context-aware computing, and smart environments. Many existing deep learning models for HAR incorporate MaxPooling layers after convolutional operations to reduce dimensionality and computational load. While this approach is effective in image-based tasks, it is less suitable for the sensor signals used in HAR. MaxPooling introduces a form of temporal downsampling that can discard subtle yet crucial temporal information. Also, traditional CNNs often struggle to capture long-range dependencies within each window due to their limited receptive fields, and they lack effective mechanisms to aggregate information across multiple windows without stacking multiple layers, which increases computational cost. In this study, we introduce Retentive-HAR, a model designed to enhance feature learning by capturing dependencies both within and across sliding windows. The proposed model intentionally omits the MaxPooling layer, thereby preserving the full temporal resolution throughout the network. The model begins with parallel dilated convolutions, which capture long-range dependencies within each window. Feature outputs from these convolutional layers are then concatenated along the feature dimension and transposed, allowing the Retentive Module to analyze dependencies across both window and feature dimensions. Additional 1D-CNN layers are then applied to the transposed feature maps to capture complex interactions across concatenated window representations before including Bi-LSTM layers. Experiments on PAMAP2, HAPT, and WISDM datasets achieve a performance of 96.40%, 94.70%, and 96.16%, respectively, which outperforms the existing methods with minimal computational cost.

Intrusion detection in IoT networks is challenged by data heterogeneity, label scarcity, and privacy constraints. Traditional federated learning (FL) methods often assume IID data or require supervised labels, limiting their practicality. We propose G-PFL-ID, a graph-driven personalized federated learning framework for unsupervised intrusion detection in non-IID IoT systems. Our method trains a global graph encoder (GCN or GAE) with a DeepSVDD objective under a federated regularizer (FedReg) that combines proximal and variance penalties, then personalizes local models via a lightweight fine-tuning head. We evaluate G-PFL-ID on the IoT-23 (Mirai-based captures) and N-BaIoT (device-level dataset) under realistic heterogeneity (Dirichlet-based partitioning with concentration parameters $\alpha \in \{0.1,0.5,\infty \}$ and client counts $K\in \{10,15,20\}$ for IoT-23, and natural device-based partitioning for N-BaIoT). G-PFL-ID outperforms global FL baselines and recent graph-based federated anomaly detectors, achieving up to 99.46% AUROC on IoT-23 and 97.74% AUROC on N-BaIoT. Ablation studies confirm that the proximal and variance penalties reduce inter-round drift and representation collapse, and that lightweight personalization recovers local sensitivity—especially for clients with limited data. Our work bridges graph-based anomaly detection with personalized FL for scalable, privacy-preserving IoT security.

Background: Accurate ground segmentation in 3D point clouds is critical for robotic perception, enabling robust navigation, object detection, and environmental mapping. However, existing methods struggle with over-segmentation, under-segmentation, and computational inefficiency, particularly in dynamic or complex environments. Methods: This study proposes FASTSeg3D, a novel two-stage algorithm for real-time ground filtering. First, Range Elevation Estimation (REE) organizes point clouds efficiently while filtering outliers. Second, adaptive Window-Based Model Fitting (WBMF) addresses over-segmentation by dynamically adjusting to local geometric features. The method was rigorously evaluated in four challenging scenarios: large objects (vehicles), pedestrians, small debris/vegetation, and rainy conditions across day/night cycles. Results: FASTSeg3D achieved state-of-the-art performance, with a mean error of <7%, error sensitivity < 10%, and IoU scores > 90% in all scenarios except extreme cases (rainy/night small-object conditions). It maintained a processing speed 10× faster than comparable methods, enabling real-time operation. The algorithm also outperformed benchmarks in F1 score (avg. 94.2%) and kappa coefficient (avg. 0.91), demonstrating superior robustness. Conclusions: FASTSeg3D addresses critical limitations in ground segmentation by balancing speed and accuracy, making it ideal for real-time robotic applications in diverse environments. Its computational efficiency and adaptability to edge cases represent a significant advancement for autonomous systems.

Innovative mobility aids like smart wheelchairs and walkers are transforming the lives of individuals with mobility challenges. These devices use sensors and in-vehicle network technologies, such as Controller Area Network (CAN), to enable real-time data exchange. While CAN is cost-effective, robust, and low-latency, it is vulnerable to cyber-attacks due to issues like message transmission on a shared bus, ID-based priority arbitration, and lack of encryption or authentication mechanisms. To mitigate these vulnerabilities, researchers have developed machine and deep learning-based intrusion detection systems (IDS). However, these systems often struggle with low feature learning and high model complexity, making them unsuitable for resource-constrained CAN networks. This paper introduces ABA-IDS, a low-footprint IDS that uses a ConvLSTM autoencoder with an attention mechanism to detect anomalies in assistive mobility networks. The model’s design allows it to efficiently capture key spatial and temporal features of the network. By preprocessing CAN data based on CAN ID and transmission frequency, ABA-IDS combines frequency-based and content-based approaches to enhance anomaly detection. Anomaly scores are generated from reconstruction errors, and testing on a benchmark dataset showed that ABA-IDS outperformed existing models with fewer parameters. This makes ABA-IDS effective for deployment in resource-constrained in-vehicle networks, significantly improving the security of assistive mobility devices against cyber threats. The link to the project repository can be found here.