Gestures for Smart Rings

Gheran, Bogdan-Florin; Vanderdonckt, Jean; Vatavu, Radu-Daniel

doi:10.1145/3196709.3196741

Cited by 92 publications

(18 citation statements)

References 50 publications

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“…(1) Explore diverse input modalities for controlling drone aerial photography and video. Examples include modalities made available by emerging wearables, such as smart rings [17], but also multimodal input, such as combined gesture and speech for finer control while taking photographs. Other drone application areas have considered specific input modalities, including brain-computer interfaces [26], emotion recognition [31], and somaesthetic interactions [27], which could also be examined in the context of the artistic process of taking photographs and filmmaking.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Taking That Perfect Aerial Photo: A Synopsis of Interactions for Drone-based Aerial Photography and Video

Şiean

Vatavu

Vanderdonckt

2021

ACM International Conference on Interactive Media Experiences

Self Cite

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Personal drones are more and more present in our lives and acting as "flying cameras" is one of their most prominent applications. In this work, we conduct a synopsis of the scientific literature on humandrone interaction to identify system functions and corresponding commands for controlling drone-based aerial photography and video, from which we compile a dictionary of interactions. We also discuss opportunities for more research at the intersection of drone computing, augmented vision, and personal photography. CCS CONCEPTS• Human-centered computing → User interface design; • Applied computing → Avionics.

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Section: Limitations and Future Workmentioning

confidence: 99%

Taking That Perfect Aerial Photo: A Synopsis of Interactions for Drone-based Aerial Photography and Video

Şiean

Vatavu

Vanderdonckt

2021

ACM International Conference on Interactive Media Experiences

Self Cite

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“…Since there is no gesture catalog that is universally applicable and intuitive for different users, a set of 12 different gestures is proposed, inspired by Gheran et al [22,23]. All considered gestures based on direction information implicitly introduce a gesture in the opposite direction.…”

Section: Relevant Gesturesmentioning

confidence: 99%

Smart Rings vs. Smartwatches: Utilizing Motion Sensors for Gesture Recognition

2021

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Since electronic components are constantly getting smaller and smaller, sensors and logic boards can be fitted into smaller enclosures. This miniaturization lead to the development of smart rings containing motion sensors. These sensors of smart rings can be used to recognize hand/finger gestures enabling natural interaction. Unlike vision-based systems, wearable systems do not require a special infrastructure to operate in. Smart rings are highly mobile and are able to communicate wirelessly with various devices. They could potentially be used as a touchless user interface for countless applications, possibly leading to new developments in many areas of computer science and human–computer interaction. Specifically, the accelerometer and gyroscope sensors of a custom-built smart ring and of a smartwatch are used to train multiple machine learning models. The accuracy of the models is compared to evaluate whether smart rings or smartwatches are better suited for gesture recognition tasks. All the real-time data processing to predict 12 different gesture classes is done on a smartphone, which communicates wirelessly with the smart ring and the smartwatch. The system achieves accuracy scores of up to 98.8%, utilizing different machine learning models. Each machine learning model is trained with multiple different feature vectors in order to find optimal features for the gesture recognition task. A minimum accuracy threshold of 92% was derived from related research, to prove that the proposed system is able to compete with state-of-the-art solutions.

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“…That evidence helps to fuel elicitation methodology's increase in popularity. Out of this popularity, elicitation has seen use across a variety of domains including: multi-touch [9], internet of things [21], augmented reality [10,11,19], and interactive rings [22].…”

Section: Motivationmentioning

confidence: 99%

The Cost of Production in Elicitation Studies and the Legacy Bias-Consensus Trade off

Williams

Garcia

Zayas

et al. 2020

MTI

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Gesture elicitation studies are a popular means of gaining valuable insights into how users interact with novel input devices. One of the problems elicitation faces is that of legacy bias, when elicited interactions are biased by prior technologies use. In response, methodologies have been introduced to reduce legacy bias. This is the first study that formally examines the production method of reducing legacy bias (i.e., repeated proposals for a single referent). This is done through a between-subject study that had 27 participants per group (control and production) with 17 referents placed in a virtual environment using a head-mounted display. This study found that over a range of referents, legacy bias was not significantly reduced over production trials. Instead, production reduced participant consensus on proposals. However, in the set of referents that elicited the most legacy biased proposals, production was an effective means of reducing legacy bias, with an overall reduction of 11.93% for the chance of eliciting a legacy bias proposal.

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Gestures for Smart Rings

Cited by 92 publications

References 50 publications

Taking That Perfect Aerial Photo: A Synopsis of Interactions for Drone-based Aerial Photography and Video

Taking That Perfect Aerial Photo: A Synopsis of Interactions for Drone-based Aerial Photography and Video

Smart Rings vs. Smartwatches: Utilizing Motion Sensors for Gesture Recognition

The Cost of Production in Elicitation Studies and the Legacy Bias-Consensus Trade off

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