Autonomous collaborative learning in wearable IoT applications

Seyed Ali Rokni, Ramin Fallahzadeh, Hassan Ghasemzadeh

Research output: Chapter in Book/Report/Conference proceedingChapter


This chapter briefly overviews robust machine-learning solutions for wearable IoT applications. Furthermore, it presents one of the earliest attempts in presenting an autonomous learning framework for wearables. The focus, in particular, is on cases where a new sensor is added to the system and the new (untrained) sensor is worn/used on various body locations. The process of autonomous learning automatically leads to a new collaborative decision-making algorithm. Addressing the problem of expanding pattern-recognition capabilities from a single setting algorithm with a predefined configuration to a dynamic setting where sensors can be added, displaced, and used unobtrusively is challenging. In such cases, successful knowledge transfer is needed to improve the learning performance by avoiding expensive data collection and labeling efforts. In this chapter, a novel and generic approach to transfer learning capabilities of an existing static sensor to a newly added dynamic sensor is described.

Original languageEnglish (US)
Title of host publicationBig Data-Enabled Internet of Things
PublisherInstitution of Engineering and Technology
Number of pages18
ISBN (Electronic)9781785616365
StatePublished - Jan 1 2020
Externally publishedYes


  • Autonomous collaborative learning
  • Autonomous learning framework
  • Body sensor networks
  • Collaborative decision-making algorithm
  • Decision making
  • Dynamic sensor
  • Internet of things
  • Knowledge transfer
  • Learning (artificial intelligence)
  • Pattern-recognition capabilities
  • Robust machine-learning solutions
  • Static sensor
  • Wearable IoT applications

ASJC Scopus subject areas

  • Computer Science(all)


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