Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring, clinical diagnosis, and robotic applications. Nevertheless, it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility, adhesion, self-healing, and environmental robustness with excellent sensing metrics. Herein, we report a multifunctional, anti-freezing, self-adhesive, and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes (CoN CNT) embedded in a polyvinyl alcohol-gelatin (PVA/GLE) matrix. Fabricated using a binary solvent system of water and ethylene glycol (EG), the CoN CNT/PVA/GLE organogel exhibits excellent flexibility, biocompatibility, and temperature tolerance with remarkable environmental stability. Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range (40%-95% RH). Freeze-tolerant conductivity under sub-zero conditions (-20 °C) is attributed to the synergistic role of CoN CNT and EG, preserving mobility and network integrity. The CoN CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 kPa^-1 in the detection range from 0 to 20 kPa, ideal for subtle biomechanical motion detection. A smart human-machine interface for English letter recognition using deep learning achieved 98% accuracy. The organogel sensor utility was extended to detect human gestures like finger bending, wrist motion, and throat vibration during speech.

Video instance segmentation (VIS) is crucial for robotic perception but state-of-the-art transformer-based methods require computational resources for edge deployment. We propose Edge-VIS, the first knowledge distillation framework specifically designed for video instance segmentation, addressing the previously unexplored challenge of transferring sophisticated spatiotemporal reasoning, spatial segmentation fidelity, and temporal instance tracking consistency simultaneously from a transformer-based teacher (GenVIS-Swin-L, 1,229 M parameters) to a lightweight CNN-based student (modified YOLOv8n-seg, 2.7 M parameters) for real-time edge deployment in robotic systems. Our multilevel distillation strategy includes feature-level, reaction-level, and attention-based distillation to preserve both partition quality and temporal stability. On the challenging Occluded Video Instance Segmentation (OVIS) dataset emphasizing severe constraints, Edge-VIS achieves 46.0% mAP@0.5, while requiring only 14 GFLOPs (36.4% reduction) and 2.7 M parameters (15.6% reduction from baseline YOLOv8n-seg). With respect to the teacher model, Edge-VIS achieves 66.5% accuracy while being 455x more parameter efficient and achieving 10.4x faster inference (52 vs 5 FPS). The deployment on NVIDIA Jetson AGX Orin demonstrates real-time performance at 250 FPS with only 29 W power consumption, confirming practical feasibility for battery-powered robotic systems.

Electric Vehicle Energy Prediction (EVEP) is vital for optimizing smart grid operations and charging infrastructure management. Traditional forecasting approaches primarily rely on historical tabular data, often overlooking the semantic and contextual richness embedded in categorical features. This study introduces TEF-PLM (Tabular Embeddings Fusion using Pretrained Language Model), a novel hybrid framework that combines structured numerical data with semantic embeddings derived from the e5-base-v2 language model to enhance multi-horizon forecasting performance. The framework is evaluated across both Non-Sequential Models (NSM), including MLP, XGB, LGBM, TabNet, and TabTransformer, and Sequential Models (SM), including BiLSTM, BiGRU, Transformer Encoder, and Temporal Fusion Transformer (TFT), under diverse data configurations: embeddings-only, dimensionally reduced representations (Principal Component Analysis (PCA) and AutoEncoder (AE)), and fused feature settings. In the NSM setup, the MLP model using PCA-reduced fused features attained the best results, achieving a 64% reduction in MAE and a 52% reduction in RMSE over the baseline. In the SM setup, the TFT model with AE-reduced fused features delivered the most consistent performance for longer horizons, achieving up to 27% improvement in RMSE, 26% in MAE, and 14% gain in R 2 compared to traditional tabular models. These outcomes validate the hypothesis that integrating pretrained semantic embeddings with observed variables significantly improves prediction accuracy, offering a robust and generalizable approach for EVEP tasks. • Proposes TEF-PLM, a hybrid framework combining PLM-based semantic embeddings with tabular features for improved EV energy prediction. • Evaluates sequential and non-sequential models with PCA- and AE-reduced embeddings for efficient forecasting. • Achieves up to 64% MAE and 27% RMSE improvement, validated through statistical significance tests. • Analyzes training overhead and latency, confirming scalability and robustness for real-world EV systems.

Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring, clinical diagnosis, and robotic applications. Nevertheless, it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility, adhesion, self-healing, and environmental robustness with excellent sensing metrics. Herein, we report a multifunctional, anti-freezing, self-adhesive, and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes (CoN CNT) embedded in a polyvinyl alcohol-gelatin (PVA/GLE) matrix. Fabricated using a binary solvent system of water and ethylene glycol (EG), the CoN CNT/PVA/GLE organogel exhibits excellent flexibility, biocompatibility, and temperature tolerance with remarkable environmental stability. Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range (40%-95% RH). Freeze-tolerant conductivity under sub-zero conditions (-20 °C) is attributed to the synergistic role of CoN CNT and EG, preserving mobility and network integrity. The CoN CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 kPa^-1 in the detection range from 0 to 20 kPa, ideal for subtle biomechanical motion detection. A smart human-machine interface for English letter recognition using deep learning achieved 98% accuracy. The organogel sensor utility was extended to detect human gestures like finger bending, wrist motion, and throat vibration during speech.