Digits: freehand 3D interactions anywhere using a wrist-worn gloveless sensor

By David Kim, Otmar Hilliges, Shahram Izadi, Alex Butler, Jiawen Chen, I. Oikonomidis, Patrick L. Olivier. ACM Symposium on User Interface Software and Technology, 2012

512 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/6e64943d33fda4f4f12c4557dd620e8e44d424a8

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Citations in the corpus

Ensembles of Deep LSTM Learners for Activity Recognition using Wearables (2017, background): “…innovative sensing and analysis approaches that target a plethora of application areas, including but not limited to novel interaction techniques [Kim et al. 2012], situated support in smart environments [Hoey et al. 2011], automated health [Plötz et al. 2012] and wellbeing assessments [Kranz…”
Interacting with Soli: Exploring Fine-Grained Dynamic Gesture Recognition in the Radio-Frequency Spectrum (2016, methodology): “Here many methods are based on images [20, 31, 35] and rely on spatial information.”
Whole-home gesture recognition using wireless signals (2013, background): “Inertial sensing and other on-body sensing methods on the other hand, require the users to wear multiple sensors or carry a device such as a wrist band [7, 14, 12].”
Capturing Hands in Action Using Discriminative Salient Points and Physics Simulation (2015, background): “Although the problemcanbe simplified bymeans of data-gloves (Ekvall andKragic 2005), color-gloves (Wang and Popović 2009), markers (Vaezi and Nekouie 2011) or wearable sensors (Kim et al. 2012), the ideal solution pursued is the unintrusive, marker-less capture of hand motion.”
FingerIO: Using Active Sonar for Fine-Grained Finger Tracking (2016, methodology): “Systems such as Digits [22] and SideSight [9] do not require instrumenting the finger with sensors but use vision/infrared sensors and hence do not work with occlusions.”
WiGest: A ubiquitous WiFi-based gesture recognition system (2014, background): “These systems, along with research enhancing them by exploiting the wide range of sensors available on such devices, generally adopt various techniques for recognizing gestures including computer vision [4], inertial sensors [5]–[7], ultra-sonic [1], and infrared (e.g. on the Samsung S4 phone).”
Accurate, Robust, and Flexible Real-time Hand Tracking (2015, background): “RELATED WORK Given our aims described above, we avoid encumbering the user’s hand with data gloves [7], colored gloves [32], wearable cameras [13], or markers [38], all of which can be barriers for natural interaction.”
Tomo: Wearable, Low-Cost Electrical Impedance Tomography for Hand Gesture Recognition (2015, methodology): “In order to compare the accuracy of our approach to previous research, we adopted a widely used [8,15,24] thumb-to-finger pinch gesture set : Index Pinch, Middle Pinch, Ring Pinch, and Little Pinch (illustrated in Figure 3).”
A Survey on Human Motion Analysis from Depth Data (2013, background): “While the volume that can be captured by the controller is very small, a wrist-worn sensor for hand pose estimation has been proposed in [143].”
Embodied hands (2017, methodology): “…2009; Sudderth et al. 2004], Particle Swarm Optimization (PSO) [Oikono-midis et al. 2011a], sampling [Oikonomidis et al. 2014], inverse-kinematics [Kim et al. 2012; Wang and Popović 2009], probabilistic line matching [Athitsos and Sclaroff 2003], reduced linear search sub-spaces [Heap and Hogg…”
ViBand: High-Fidelity Bio-Acoustic Sensing Using Commodity Smartwatch Accelerometers (2016, background): “A popular approach for hand gesture recognition takes advantage of optical sensors such as cameras [25] and IR sensors [16, 34, 35, 52].”
3D Gestural Interaction: The State of the Field (2013, methodology): “uses a wrist worn sensing device called Digits [15].”
Realtime and Robust Hand Tracking from Depth (2014, background): “Other realtime and robust systems are limited in recognizing discrete hand gestures only [31] [5] [6] [29] without optimization, supporting a small number of DOFs [22], or under a fixed viewpoint [12].”
Soli (2016, background): “…and a multitude of sensors embedded in various objects, the human body, clothes and environments or distributed as 3D interfaces (see for example [Kim et al. 2012; Harrison et al. 2010; Rekimoto 2001; Saponas et al. 2009; Smith et al. 1998; Strickon and Paradiso 1998; Dietz and Leigh 2001;…”