Demo Data from SIGIR 2026 / reviewers in the wild / expert
Nipuna Samarasekara
dblp:161/3072
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Interaction techniques and input
expressive input |
0.2 | 1 | 2015 | zSense: Enabling Shallow Depth Gesture Recognition for Greater Input Expressivity on Smart Wearables · CHI 2015 |
Interaction techniques and input › input sensing
gesture recognition |
0.2 | 1 | 2015 | zSense: Enabling Shallow Depth Gesture Recognition for Greater Input Expressivity on Smart Wearables · CHI 2015 |
Wearable and physiological sensing
smart wearable |
0.2 | 1 | 2015 | zSense: Enabling Shallow Depth Gesture Recognition for Greater Input Expressivity on Smart Wearables · CHI 2015 |
Wearable and physiological sensing › optical sensing
infrared sensing |
0.1 | 1 | 2015 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | zSense: Enabling Shallow Depth Gesture Recognition for Greater Input Expressivity on Smart WearablesabstractIn 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 |
CHI | 3 |