User centered gesture development for smart lighting

Park, Donggun; Lee, Yushin; Song, Seung Jin; Rhiu, Ilsun; Kwon, Sanghyun; An, Yongdae; Yun, Myung Hwan

doi:10.17210/hcik.2016.01.146

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…In such an environment, the blind person can use binary gestures instead of voice. In [28], a set of hand gestures is proposed to control the smart lighting system. ese vision-based hand gestures are more complex and difficult in terms of their recognition.…”

Section: Prototype Application and Discussionmentioning

confidence: 99%

Your Knock Is My Command: Binary Hand Gesture Recognition on Smartphone with Accelerometer

Zhang

Chen

et al. 2020

Mobile Information Systems

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Motion-based hand gesture is an important scheme to allow users to invoke commands on their smartphones in an eyes-free manner. However, the existing scheme is facing some problems. On the one hand, the expression ability of one single gesture is limited. As a result, a gesture set consisting of multiple gestures is typically adopted to represent different commands. Users must memorize all gestures in order to make interaction successfully. On the other hand, the design of gestures needs to be complicated to express diverse intensions. However, complex gestures are difficult to learn and remember. In addition, complex gestures set a high recognition barrier to smart APPs. This leads to an imbalance problem. Different gestures have different recognition accuracy levels, which may result in instability of recognition precision in practical applications. To address these problems, this paper proposes a novel scheme using binary motion gestures. Only two simple gestures are required to express bit “0” and “1,” and rich information can be expressed through the permutation and combination of the two binary gestures. Firstly, four kinds of candidate binary gestures are evaluated for eyes-free interactions. Then, an online signal cutting and merging algorithm is designed to split accelerometer signals sequence into multiple separate gesture signal segments. Next, five algorithms, including Dynamic Time Warping (DTW), Naive Bayes, Decision Tree, Support Vector Machine (SVM), and Bidirectional Long Short-Term Memory (BLSTM) Network, are adopted to recognize these segments of knock gestures. The BLSTM achieves the top performance in terms of both recognition accuracy and recognition imbalance. Finally, an Android application is developed to illustrate the usability of the proposed binary gestures. As binary gestures are much simpler than traditional hand gestures, they are more efficient and user-friendly. Our scheme eliminates the imbalance problem and achieves high recognition accuracy.

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Section: Prototype Application and Discussionmentioning

confidence: 99%

Your Knock Is My Command: Binary Hand Gesture Recognition on Smartphone with Accelerometer

Zhang

Chen

et al. 2020

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Hand gesture recognition is one of the machine learning applications applied to smart lighting. Park et al [286] said that hand gesture recognition is vital in smart lighting to get the full functionality of the proposed system. Mohammed et al [287] utilizes smart clothing, namely wearable sensors attached to clothes, as a medium for executing gesture recognition.…”

Section: ) Gesture Recognition For Controlmentioning

confidence: 99%

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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“…In this study, we conducted a literature review regarding mid-air gestures and voice commands. A list of mid-air gestures used in previous works [56], [57], [58], [59], [60], [61], [62], [63], [64] was selected based on the context of indoor working. In terms of voice interface design, there are no common standard commands; voice commands can include words, sentences, or conversations [65].…”

Section: ) Elicitation Studymentioning

confidence: 99%

ARiana: Augmented Reality Based In-Situ Annotation of Assembly Videos

et al. 2022

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Annotated videos are commonly produced for documenting assembly and maintenance processes in the manufacturing industry. However, according to a semi-structured interview we conducted with industrial experts, the current process of creating annotated assembly videos, in which the annotator annotates the video capturing the expert's demonstration of assembly and maintenance process, is cumbersome and time-consuming. The key challenges include three key problems in annotation: (1) unnecessary extra communications between field workers and annotators, (2) lack of suitable camera gear, and (3) wasting time in the manual removal of non-informative portions of captured videos. Because annotation always follows video capture, the problem 1 remains out of scope for state-of-the-art video annotation tools. And making the assumption of a perfect captured video, which covers no occlusion and contains only relevant assembly or maintenance information, causes problem 2 and 3. As a result, we have developed ARiana, a wearable augmented reality-based in-situ video annotation tool that guides field experts to create annotations efficiently while conducting the assembly or maintenance tasks. ARiana has three key features that include context-awareness enabled by hand-object interaction, multimodal interaction for annotation on the fly, and real-time audiovisual guidance enabled by edge offloading. We have implemented ARiana on Android-based smart glasses, equipped with first-person camera and microphone. In a usability test based on attempting to assemble a toy model and to annotate the recorded video simultaneously, ARiana demonstrated higher efficiency and effectiveness compared to one of the state-of-the-art video annotation tools, in which the assembling process is followed by the annotation process. In particular, ARiana helps users finish annotation tasks four times faster, and increase the annotation accuracy by 23%.

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User centered gesture development for smart lighting

Cited by 7 publications

References 21 publications

Your Knock Is My Command: Binary Hand Gesture Recognition on Smartphone with Accelerometer

Your Knock Is My Command: Binary Hand Gesture Recognition on Smartphone with Accelerometer

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

ARiana: Augmented Reality Based In-Situ Annotation of Assembly Videos

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