This paper presents an experimental evaluation of a wearable light-emitting diode (LED) transmitter in an optical camera communications (OCC) system. The evaluation is conducted under conditions of controlled user movement during indoor physical exercise, encompassing both mild and intense exercise scenarios. We introduce an image processing algorithm designed to identify a template signal transmitted by the LED and detected within the image. To enhance this process, we utilize the dynamics of controlled exercise-induced motion to limit the tracking process to a smaller region within the image. We demonstrate the feasibility of detecting the transmitting source within the frames, and thus limit the tracking process to a smaller region within the image, achieving an reduction of 87.3% for mild exercise and 79.0% for intense exercise.

This paper presents an experimental evaluation of two types of light-emitting diode (LED)-based distributed transmitters, namely an LED strip and an LED-coupled side-emitting optical fiber, in both laboratory and wearable optical camera communication (OCC) systems. We study the system performance in terms of success of reception (SoR) with regard to the transmission distance. The best value of SoR is achieved when the camera is facing directly to the transmitter (T _x ) from a close distance of 1 m. Additionally, we compare the power consumption, the signal-to-noise ratio performance (SNR) and all the obtained values under optimal conditions are better than the forward error correction (FEC) limit in OCC systems.

This article presents an ontology-driven Digital Twin (DT) for hotel front-desk operations that fuses two real-time data streams: (i) physiological and activity signals from wrist-worn wearables assigned to staff, and (ii) 3D people-positioning and occupancy events captured by reception-area cameras using a proprietary implementation of Optical Camera Communication (OCC). Building on a previously proposed front-desk ontology, the semantic model is extended with positional events, zone semantics, and wearable-derived workload indices to estimate queue state, staff workload, and service demand in real time. A vendor-agnostic, property-based REST API specifies the DT interface in terms of observable properties, including authentication and authorization, idempotent ingestion, timestamp conventions, version negotiation, integrity protection for signed webhooks, rate limiting and backoff, pagination and filtering, and privacy-preserving identifiers, enabling any compliant backend to implement the specification. The proposed layered architecture connects ingestion, spatial reasoning, and decision services to dashboards and key performance indicators (KPIs). This article details the positioning pipeline (calibration, normalized 3D coordinates, zone mapping, and confidence handling), the wearable workload pipeline, and an evaluation protocol covering localization error, zone classification, queue-length estimation, and workload accuracy. The results indicate that a spatially aware, ontology-based DT can support more balanced staff allocation and improved guest experience while remaining technology-agnostic and privacy-conscious.

In this paper, we present a proof of concept for an indoor optical camera communication (OCC) system utilising a deep learning network to detect and identify humans wearing light‐emitting diode (LED) strips. Specifically, we propose using the You Only Look Once (YOLO) version 8 object detection algorithm, which is built on convolutional neural networks (CNNs), to identify wearable LED transmitters in challenging scenarios such as low visibility, mobility and multiple users, followed by image processing to effectively decode the transmitted data. The red‐green‐blue (RGB) LED strip's colours (red, green, blue and white) serve as indicators of the user's status. By combining communication and monitoring functionalities, the LEDs facilitate not only the transmission of user data but also accurate detection, tracking and identification within the environment. This demonstrates the feasibility of utilising widely available devices like LED strips and cameras, commonly found in many buildings, with potential applications in high‐risk environments where monitoring individuals' physical conditions is crucial. The obtained results indicate our system's effectiveness, as it achieved up to 100% success of reception (SoR) in a static experimental setup, 96.2% in a walking experimental setup with one user and showed no effectiveness with two users.