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Youssef Ben Taleb

dblp:344/9432 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-0826-8226ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Wearable and physiological sensing · 77% Interaction techniques and input · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wearable and physiological sensing › biosignal sensing
bioimpedance sensing
0.712023
Z-Ring: Single-Point Bio-Impedance Sensing for Gesture, Touch, Object and User Recognition · CHI 2023
Interaction techniques and input › input device
wearable input device
0.212023
Z-Ring: Single-Point Bio-Impedance Sensing for Gesture, Touch, Object and User Recognition · CHI 2023

Methods — techniques the papers use, named apart from their topics

user study · 0.7machine learning classification · 0.7
YearPublicationVenuePosition
2023 Z-Ring: Single-Point Bio-Impedance Sensing for Gesture, Touch, Object and User Recognition
abstract
We present Z-Ring, a wearable ring that enables gesture input, object detection, user identification, and interaction with passive user interface (UI) elements using a single sensing modality and a single point of instrumentation on the finger. Z-Ring uses active electrical field sensing to detect changes in the hand’s electrical impedance caused by finger motions or contact with external surfaces. We develop a diverse set of interactions and evaluate them with 21 users. We demonstrate: (1) Single- and two-handed gesture recognition with up to 93% accuracy (2) Tangible input with a set of passive touch UI elements, including buttons, a continuous 1D slider, and a continuous 2D trackpad with 91.8% accuracy, <4.4 cm MAE, and <4.1cm MAE, respectively (3) Object recognition across six household objects with 94.5% accuracy (4) User identification among 14 users with 99% accuracy. Z-Ring’s sensing methodology uses only a single co-located electrode pair for both receiving and sensing, lending itself well to future miniaturization for use in on-the-go scenarios.
Anandghan Waghmare, Youssef Ben Taleb, Ishan Chatterjee, Arjun Narendra, Shwetak N. Patel
CHI2