Autonomous sensor-context learning in dynamic human-centered internet-of-things environments

Seyed Ali Rokni, Hassan Ghasemzadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Human-centered Internet-of-Things (IoT) applications utilize computational algorithms such as machine learning and signal processing techniques to infer knowledge about important events such as physical activities and medical complications. The inference is typically based on data collected with wearable sensors or those embedded in the environment. A major obstacle in large-scale utilization of these systems is that the computational algorithms cannot be shared between users or reused in contexts different than the setting in which the training data are collected. For example, an activity recognition algorithm trained for a wrist-band sensor cannot be used on a smartphone worn on the waist. We propose an approach for automatic detection of physical sensor-contexts (e.g., on-body sensor location) without need for collecting new labeled training data. Our techniques enable system designers and end-users to share and reuse computational algorithms that are trained under different contexts and data collection settings. We develop a framework to autonomously identify sensor-context. We propose a gating function to automatically activate the most accurate computational algorithm among a set of shared expert models. Our analysis based on real data collected with human subjects while performing 12 physical activities demonstrate that the accuracy of our multi-view learning is only 7.9% less than the experimental upper bound for activity recognition using a dynamic sensor constantly migrating from one on-body location to another. We also compare our approach with several mixture-of-experts models and transfer learning techniques and demonstrate that our approach outperforms algorithms in both categories.

Original languageEnglish (US)
Title of host publication2016 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450344661
DOIs
StatePublished - Nov 7 2016
Externally publishedYes
Event35th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2016 - Austin, United States
Duration: Nov 7 2016Nov 10 2016

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
Volume07-10-November-2016
ISSN (Print)1092-3152

Other

Other35th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2016
Country/TerritoryUnited States
CityAustin
Period11/7/1611/10/16

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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