Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Nipuna Samarasekara

dblp:161/3072 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

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

Human-computer interaction and ubiquitous computing · 1

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
Interaction techniques and input · 61% Wearable and physiological sensing · 39%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Interaction techniques and input
expressive input
0.212015
zSense: Enabling Shallow Depth Gesture Recognition for Greater Input Expressivity on Smart Wearables · CHI 2015
Interaction techniques and input › input sensing
gesture recognition
0.212015
zSense: Enabling Shallow Depth Gesture Recognition for Greater Input Expressivity on Smart Wearables · CHI 2015
Wearable and physiological sensing
smart wearable
0.212015
zSense: Enabling Shallow Depth Gesture Recognition for Greater Input Expressivity on Smart Wearables · CHI 2015
Wearable and physiological sensing › optical sensing
infrared sensing
0.112015
zSense: Enabling Shallow Depth Gesture Recognition for Greater Input Expressivity on Smart Wearables · CHI 2015

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

non-linear spatial sampling · 0.2
YearPublicationVenuePosition
2015 zSense: Enabling Shallow Depth Gesture Recognition for Greater Input Expressivity on Smart Wearables
abstract
In this paper we present zSense, which provides greater input expressivity for spatially limited devices such as smart wearables through a shallow depth gesture recognition system using non-focused infrared sensors. To achieve this, we introduce a novel Non-linear Spatial Sampling (NSS) technique that significantly cuts down the number of required infrared sensors and emitters. These can be arranged in many different configurations; for example, number of sensor emitter units can be as minimal as one sensor and two emitters. We implemented different configurations of zSense on smart wearables such as smartwatches, smartglasses and smart rings. These configurations naturally fit into the flat or curved surfaces of such devices, providing a wide scope of zSense enabled application scenarios. Our evaluations reported over 94.8% gesture recognition accuracy across all configurations.
Anusha Withana, Roshan Lalintha Peiris, Nipuna Samarasekara, Suranga Nanayakkara
CHI3