The conventional Human Activity Recognition (HAR) systems use smartwatches, smartphones, and other wearable devices to autonomously derive consumer physical activities. However, implementing real-time HAR in resource-constrained applications is challenging. Computational offloading approaches must address network reliance, latency, and data privacy, which affect system performance. The present study proposes a low-cost, wearable HAR device that collects and transmits the Inertial Measurement Unit (IMU) telemetry signals to smartphones, enabling real-time on-device activity detection. Human actions are subjective and affected by physiological, environmental, and behavioural factors, complicating HAR systems' performance in personalized contexts. Generalized methods for existing systems reduce performance for new consumers or due to alterations in consumer locomotive signatures. Thus, our second goal is to enable the on-device personalization of wearable HAR models with minimum customer calibration. The HAR device, which is in the form of a smart-band, transfers data to and fro smartphones through Bluetooth Low Energy (BLE). A lightweight 1D convolutional neural network (CNN) is built and, using transfer learning, the network model is fine-tuned from real-time sensed data. On-device HAR inferences ensure real-time processing on smartphones without computational offloading. Smartphone-based on-device training for fine-tuning the model allows the HAR system to get customized without compromising privacy or computational costs. The experimental results show that the personalized classification achieves on average 98% accuracy when assessed using four benchmark datasets.

Deep.

As the final link in the fuel supply chain, gas stations integrate transportation, storage, and retail operations. From a consumer technology perspective, safety, efficiency, and service are key determinants of gas station usability and ensure smooth fueling processes. To boost the intelligent management and control capabilities of gas stations, this paper presents AiGuard, an AI-driven supervision platform designed to enhance safety management, improve service quality, and optimize operational efficiency at gas stations. It integrates existing algorithms for object detection, real-time tracking, and pose estimation with a purpose-designed model for interactive behavior recognition, enabling comprehensive analysis of key gas station zones like the fuel unloading area, forecourt, convenience store, and financial office. The platform enhances safety through real-time monitoring and early hazard detection, improves service quality by reducing waiting time and transactional errors, and increases operational efficiency through intelligent scheduling and compliance tracking. To the best of our knowledge, AiGuard is the first comprehensive platform to provide full-site monitoring and intelligent management for gas station operations, offering a practical and scalable solution for future consumer-oriented smart stations.

Gen-Presenter is a cost-effective, AI-powered interactive presentation system designed for consumer-facing environments such as museums, exhibitions, and public installations. Addressing the limitations of traditional kiosks and robotic guides, it leverages Retrieval-Augmented Generation (RAG), large language models (LLMs), and edge AI to deliver adaptive multimodal content tailored to visitor demographics and engagement levels. Running primarily on an edge device, the system integrates modules for gaze detection, age estimation, automated speech recognition (ASR), text-to-speech (TTS), and content management. Two LLMs on a backend NLP server handle slide selection and explanation generation. While LLM2 with RAG enriched responses with contextual content, LLM1 (LLaMA 3.3 without RAG) demonstrated superior slide selection performance, achieving 0.95, 0.91, and 0.96 accuracy with only 1s of latency. User suitability testing demonstrates strong alignment with diverse age groups (scores between 0.81 to 0.96), and the multithreaded design delivers a 15.7x improvement in slide processing efficiency. These results highlight the system’s scalability, responsiveness, and potential as an intelligent consumer electronics solution for immersive public engagement.

Recently, smart contract, a code script that autonomously runs on blockchains, has facilitated numerous decentralized applications and accelerated the boost of the Web3 ecosystem. However, the current underutilization of smart contracts and a large number of duplicate smart contracts have caused significant challenges to blockchains. Considering the reusability and composability of smart contracts, we can innovatively address these issues, promote diversity for decentralized projects, and increase efficiency for Web3 developers. In this tutorial paper, we first propose a novel service-oriented paradigm: Smart Contracts as a Service (SCaaS), then analyze the feasibility of SCaaS, delve into its application scenarios and finally explore the challenges and limitations and propose the potential solutions accordingly.