An IoT Based Smart Vault Security and Monitoring System with Zero UI

Recent advances have introduced IoT as one of the key technologies globally. As safety remains a critical issue for those who spend much time outside. Automated security systems are very useful where safety is an important issue. With a prospect of a Zero User Interface (UI) model this work represents a novel IoT based smart vault security system. The system is built and designed based on IoT combining with Arduino-Uno and Bluetooth module. This system involves LDR sensor, IR sensor and Sonar sensor for monitoring. The vault provides security on three levels. Password protected entry to connect with the smartphone using Bluetooth module, IR sensor array to use 'secret gesture pattern' to unlock the door, tracking number of transactions from the vault using Sonar sensor and LDR was used as a switch. To avoid the replication of physical unlocking of objects IR sensor array was used to introduce 'secret gesture pattern' unlocking system through touchless interfaces for the avoidance of transmissive diseases like COVID-19. This novel system has substantial possibility as a security vault system for industrial and residential use in a contactless manner. © 2023 IEEE.

Object Selection Using LSTM Networks for Spontaneous Gaze-Based Interaction

Two years on with Covid-19, touchless technology has evolved from a device that symbolizes luxury to something that is necessary. Eye tracker is one type of touchless technologies that uses user's gaze to interact with computer without touching the screen. Development of spontaneous gazebased interaction is progressing very rapidly. Researchers have developed various object selection methods without prior gazeto-screen calibration. Recently, the conventional approach of setting threshold was developed as a gaze-based object selection method. However, the use of threshold values is considered non-adaptive and requires additional data pre-processing to handle noises. To overcome this problem, deep learning is used as an object selection method for spontaneous gaze-based interaction. Deep learning does not require any data preprocessing method to achieve accurate object selection results. Out of five deep learning algorithms that were evaluated, LSTM (Long Short-Term Memory) and BiLSTM (Bidirectional Long Short-Term Memory) networks achieved comparable accuracy of 95.17 pm 0.95% and 95.15 pm 1.17%, respectively. In future, our research is promising for development of real-time object selection technique for touchless public display. © 2022 IEEE.

Spontaneous Gaze-Based Application Using 2D Correlation and Velocity Threshold Identification

Demand for touchless technology has grown with the surge of Covid-19 pandemic. Spontaneous gaze-based application is one of several promising technologies for touchless interaction. Despite of this potential, little attention has been paid to the performance of traditional eye movements classification methods on improving accuracy of gaze-based object selection. To handle this research gap, we proposed a novel workflow of spontaneous gaze-based object selection using 2D Correlation as similarity measure and Velocity Threshold Identification (I-VT) as a method for eye movements classification. We compared our method with Pearson Product-Moment Correlation (PPMC) as similarity measure and Dispersion Threshold Identification (I-DT) for eye movements classification. Our experimental results showed that the proposed method yielded object selection accuracy up to 95:62%+/-3:48%. In future, our proposed method can be implemented in the development of touchless interactive technologies that adhere to the World Health Organization guidelines, especially during the Covid-19 pandemic.

From luxury to necessity: Progress of touchless interaction technology.

Touchless Technology is facilitating the move to Zero User Interface(UI) propelled by the COVID-19 pandemic which has accelerated the use of this technology due to hygiene requirements. Zero UI can be defined as a controlled interface that enables user interaction with technology through voice, gestures, hand interaction, eye tracking, and biometrics such as facial recognition and contactless fingerprints. Smart devices, IoT sensors, smart appliances, smart TVs, smart assistants and consumer robotics are predominant examples of devices in which Zero UI is becoming increasingly adopted. These control interfaces include natural interaction modes such as voice or gestures. Touchscreens and shared devices such as kiosks, self-service counters and interactive displays are present in our everyday lives. Each of these interactions however is a concern for consumers in a post-COVID-19 world where hygiene is of utmost importance. The one-stop solution to hygienic interactions includes touchless technology such as voice control, remote mobile screen take over, biometric, and gesture control as Zero User interfaces. With the breakthroughs in image recognition and natural language processing, powered by advanced computer vision and machine learning, "Zero UI" is becoming a new normal. This paper is focusing on the progress of the touchless interaction technology during the COVID-19 pandemic, which actually accelerated development in this concept and moved it from being a luxury to a life necessity.