mlsanityData to 7 Oct 2026

TOM: A Development Platform For Wearable Intelligent Assistants

arXiv 2407.15523 ↗First posted 22 Jul 2024cs.HC cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

Advanced digital assistants can significantly enhance task performance, reduce user burden, and provide personalized guidance to improve users' abilities. However, the development of such intelligent digital assistants presents a formidable challenge. To address this, we introduce TOM, a conceptual architecture and software platform (https://github.com/TOM-Platform) designed to support the development of intelligent wearable assistants that are contextually aware of both the user and the environment. This system was developed collaboratively with AR/MR researchers, HCI researchers, AI/Robotic researchers, and software developers, and it continues to evolve to meet the diverse requirements of these stakeholders. TOM facilitates the creation of intelligent assistive AR applications for daily activities and supports the recording and analysis of user interactions, integration of new devices, and the provision of assistance for various activities. Additionally, we showcase several proof-of-concept assistive services and discuss the challenges involved in developing such services.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 46 references that are in the map. First posted on arXiv 22 Jul 2024.

Builds on

6 of 46 references.

References · 6

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  3. 2023The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)cited by 677
  4. 2023XAIR: A Framework of Explainable AI in Augmented Realitycited by 17
  5. 2021Platform for Situated Intelligencecited by 9
  6. 2018A Survey of Attention Management Systems in Ubiquitous Computing Environmentscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.