A visible light communication (VLC)-based sensing system is proposed for gait recognition and classification. Human-induced reflections are modeled using a time-varying channel representation, enabling the capture of gait dynamics without requiring wearable devices. A low-cost sensing module with embedded processing and multi-channel photodetectors is implemented. Filtered signals are transformed into spectral-spatial features and analyzed using deep learning models. Experiments involving ten participants across eight gait types demonstrate that contrastive and multi-scale models achieve over 98% accuracy, highlighting the potential of VLC-based sensing for unobtrusive, privacy-preserving, and real-time human gait recognition.

As human-computer interaction (HCI) advances, gesture recognition has emerged as a transformative technology for human-computer interaction. Traditional methods, often camera or glove-based, are restricted by various environmental conditions and user-specific demands, highlighting the need for more universal, non-intrusive, and sustainable solutions. Addressing this, we present PowerGest, a self-powered gesture recognition system based on a solar cell array. This innovative system leverages the dual functionalities of solar cells: energy harvesting and gesture sensing, providing an alternative to conventional methods. It integrates a designed low-powered data acquisition chip with a wireless transmission module and a user-friendly interface. PowerGest employs a series of signal processing methods and utilizes several machine learning algorithms, achieving over 97% accuracy for both numeric input and activity control gesture recognition tasks. With its broad applications in robotic control, text input, and more, PowerGest contributes to a more sustainable and intuitive HCI experience. Project demo: https://drive.google.com/drive/folders/10KEul8PAfvTUomi0JvZ8u411JyScCXQI?usp=sharing.

This research focuses on ubiquitous sensing systems for activity and gesture recognition through novel optical sensing and energy harvesting technologies such as triboelectric nanogenerators (TENG), solar cells, and visible light communication (VLC). The primary goal is to address the limitations of existing sensing systems by creating a low-cost, energy-efficient, comprehensive solution that enhances sensor integration and communication. Thus, this study utilizes TENG for contact sensing, solar cells for non-contact sensing, and VLC for spatial sensing. The applied methodologies achieve activity and gesture recognition, with accuracies up to 99.4% and 97.3%, respectively. This work has potential applications in smart home automation, health monitoring, and intelligent control by providing a more sustainable and user-friendly approach to ubiquitous sensing.

The demand for accurate and real-time indoor localization and human sensing is rising with the development of smart environments, with applications in security and smart homes. Effective systems enhance safety, energy efficiency, and user experience by leveraging existing infrastructure, reducing deployment costs, and integrating seamlessly. Traditional methods rely on dedicated hardware, while communication or lighting infrastructure can provide dual-purpose solutions. This research focuses on Visible Light Communication (VLC) technology, which uses visible light for data transmission. Our VLC system utilizes existing lighting infrastructure to transmit data, providing localization and human sensing functionalities. The system design strategically incorporates specific VLC transmitters and receivers to enhance sensing performance. The collected data is processed using advanced algorithms and machine learning models, ensuring robust, real-time, and cost-effective localization with an accuracy of 96.6% and human activity recognition with an accuracy of 98.3%. This multi-functionality system demonstrates the potential for VLC in healthcare monitoring and home automation applications.

In response to the challenges presented by conventional material and texture recognition methods, our research introduces a system using visible light communication (VLC) technology. This approach provides a non-contact, non-destructive, dual-functional solution, overcoming the limitations of cost, safety, and environmental adaptability associated with traditional methods. Through a comprehensive design integrating hardware and software, our system utilizes VLC for precise and efficient recognition. Extensive testing confirms its effectiveness, achieving 97.7% accuracy in material identification and 93.8% in texture detection. This study highlights VLC's potential in enhancing automated recognition systems across various applications.

SolarSense is a self-powered sensing system for gesture recognition using solar cell arrays, thereby offering a sustainable approach to human-computer interaction (HCI) within industrial settings. The system effectively employed the sensing and energy harvesting capabilities of solar cells, achieving over 97.0% accuracy in recognizing diverse gestures. The design incorporates a low-power wireless data acquisition chip, a signal processing framework, and a user interface to realize robotic control and text input applications. SolarSense enhances HCI with its eco-friendly and user-centric approach, which is suitable for Internet of things (IoT) scenarios. Demo: https://youtu.be/RmPolChw_c4.