Towards Contactless, Hand Gestures-Based Control of Devices

Czuszynski, Krzysztof; Rumiński, Jacek

doi:10.1109/iecon.2018.8591446

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Gesture detection utilizes four directional photodiodes integrated with visible light blocking filters to accurately sense simple up -down -right -gestures or more complex gestures. [2]…”

Section: Guesture Sensormentioning

confidence: 99%

An intelligent dimming method based on human expression and gesture fusion recognition for smart home

Gao

Zhang

et al. 2023

Ninth Symposium on Novel Photoelectronic Detection Technology and Applications

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According to the demand of future smart home for lighting experience, an embedded intelligent LED dimming system based on the Raspberry Pi 4B is proposed. The system detects the human proximity distance and captures the gesture movements and other parameters through one ADPS-9960 sensor. An embedded miniature CSI camera OV5647 is used to collect the user's facial expression image data. PWM signal is generated from Raspberry Pi GPIO pin to control the LED illumination. PWM duty cycle range is determined by the fusion results of normalized weighted modulation from the expression type recognized from the facial images and the gesture detected byADPS-9960. Under the basic 3-tier CNN recognition model framework, the expression recognition is further enhanced by introducing the fusion with Face landmarks and HOG features. The experimental results show that the proposed dimming system can adaptively control the luminaire brightness according to the user's psychological state and behavior, and can light off when the person leaves the light, which meets the requirements of modern smart home for user light experience and energy saving.

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“…Gesture detection utilizes four directional photodiodes integrated with visible light blocking filters to accurately sense simple up -down -right -gestures or more complex gestures. [2]…”

Section: Guesture Sensormentioning

confidence: 99%

An intelligent dimming method based on human expression and gesture fusion recognition for smart home

Gao

Zhang

et al. 2023

Ninth Symposium on Novel Photoelectronic Detection Technology and Applications

View full text Add to dashboard Cite

show abstract

“…Regarding 3D-camera-based recognition, Handosa et al used the Kinect sensor to control lights [ 39 ]. In sensor-based recognition, Czuszynski et al [ 40 ] used a contactless linear gesture sensor for the navigation of a smart lighting system to propose two methods of active light source selection: continuous dimming, and turning on and off based on discrete gestures. They achieve a gesture recognition accuracy of up to 97.58%.…”

Section: State-of-the-artmentioning

confidence: 99%

Smart Assistive Architecture for the Integration of IoT Devices, Robotic Systems, and Multimodal Interfaces in Healthcare Environments

Brunete

Gambao

Hernando

et al. 2021

Sensors

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This paper presents a new architecture that integrates Internet of Things (IoT) devices, service robots, and users in a smart assistive environment. A new intuitive and multimodal interaction system supporting people with disabilities and bedbound patients is presented. This interaction system allows the user to control service robots and devices inside the room in five different ways: touch control, eye control, gesture control, voice control, and augmented reality control. The interaction system is comprised of an assistive robotic arm holding a tablet PC. The robotic arm can place the tablet PC in front of the user. A demonstration of the developed technology, a prototype of a smart room equipped with home automation devices, and the robotic assistive arm are presented. The results obtained from the use of the various interfaces and technologies are presented in the article. The results include user preference with regard to eye-base control (performing clicks, and using winks or gaze) and the use of mobile phones over augmented reality glasses, among others.

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Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

et al. 2022

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Smart lighting has become a universal smart product solution, with global revenues of up to US$5.9 billion by 2021. Six main factors drive the technology: light-emitting diode (LED) lighting, sensors, control, analytics, and intelligence. The Internet of things (IoT) concept with the end device, platform, and application layer plays an essential role in optimizing the advantages of LED lighting in the emergence of smart lighting. The ultimate aim of smart lighting research is to introduce low energy efficiency and high user comfort, where the latter is still in the infancy stage. This paper presents a systematic literature review (SLR) from a bird's eye view covering full-length research topics on smart lighting, including issues, implementation targets, technological solutions, and prospects. In addition to that, this paper also provides a detailed and extensive overview of emerging machine learning techniques as a key solution to overcome complex problems in smart lighting. A comprehensive review of improving user comfort is also presented, such as the methodology and taxonomy of activity recognition as a promising solution and user comfort metrics, including light utilization ratio, unmet comfort ratio, light to comfort ratio, power reduction rate, flickering perception, Kruithof's comfort curve, correlated color temperature, and relative mean square error. Finally, we discuss in-depth open issues and future challenges in increasing user comfort in smart lighting using activity recognition.

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Towards Contactless, Hand Gestures-Based Control of Devices

Cited by 4 publications

References 21 publications

An intelligent dimming method based on human expression and gesture fusion recognition for smart home

An intelligent dimming method based on human expression and gesture fusion recognition for smart home

Smart Assistive Architecture for the Integration of IoT Devices, Robotic Systems, and Multimodal Interfaces in Healthcare Environments

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

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