Paper Info
Title
Privacy-Preserving Gesture Recognition with Explainable Type-2 Fuzzy Logic Based Systems
Abstract
Smart homes are a growing market in need of privacy preserving sensors paired with explainable, interpretable and reliable control systems. The recent boom in Artificial Intelligence (AI) has seen an ever-growing persistence to incorporate it in all spheres of human life including the household. This growth in AI has been met with reciprocal concern for the privacy impacts and reluctance to introduce sensors, such as cameras, into homes. This concern has led to research of sensors not traditionally found in households, mainly short range radar. There has been also increasing awareness of AI transparency and explainability. Traditional AI black box models are not trusted, despite boasting high accuracy scores, due to the inability to understand what the decisions were based on. Interval Type-2 Fuzzy Logic offers a powerful alternative, achieving close to black box levels of performance while remaining completely interpretable. This paper presents a privacy preserving short range radar sensor coupled with an Explainable AI system employing a Big Bang Big Crunch (BB-BC) Interval Type-2 Fuzzy Logic System (FLS) to classify gestures performed in an indoor environment.
Year
DOI
Venue
2020
10.1109/FUZZ48607.2020.9177768
2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Keywords
DocType
ISSN
Type-2 Fuzzy Logic,Big Bang - Big Crunch,Privacy Preserving Sensing,Explainable Artificial Intelligence (XAI)
Conference
1544-5615
ISBN
Citations 
PageRank 
978-1-7281-6933-0
1
0.43
References 
Authors
16
6
Name
Order
Citations
PageRank
Josip Rozman110.43
Hani Hagras21747129.26
Javier Andréu Pérez321013.55
Damien Clarke410.43
Beate Müller510.43
Steve Fitz Data610.43