Indoor Anti-Collision Alarm System Based on Wearable Internet of Things for Smart Healthcare
Abstract
Smart healthcare, instead of traditional healthcare, has attracted tremendous attention all over the world, and calls for integrated design bringing together interdisciplinary technological approaches and solutions with the aim of supporting affordable and high-quality patient care. With the increasing number of visually impaired people, including the blind, the elderly, and patients with eye diseases, it is vital to help them explore their outside environment with the assistance of smart technologies so that they can adapt to the environment more easily. The smart indoor anti-collision system is one typical application. Previous works normally use video monitors or deployed sensors to provide outside environment information for visually impaired people. However, these methods suffer from privacy disclosure and inconvenience. In this article, we present IAAS, a smart indoor anti-collision system based on RFID, which identifies and tracks passive RFID tags by analyzing the received backscatter signals. We extract RSSI based on the LWLR algorithm and phase profiles as fingerprints to help the user guide from obstacles without observations of eyes. Experiments are conducted to verify our system, and results show that IAAS could achieve high accuracy of 94 percent in obstacle avoidance.
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By extracting RSSI and phase profiles as fingerprints, the system estimates distance to obstacles without observations of eyes and thus guides users to move safely.
“By extracting the RSSI and phase profiles as fingerprints it estimates the distance to obstacles without observations of eyes and thus guiding the users to move in the domain safely.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [126]
Indoor positioning is used to protect visually impaired, elderly people from collisions.
“Further indoor positioning is used to protect visually impaired, elderly people from collisions [126].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [126]
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5G-based smart healthcare network: architecture, taxonomy, challenges and future research directions
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- Authors
- Xiao, F., Miao, Q., Xie, X., Sun, L., & Wang, R.
- Affiliations
- Nanjing University of Posts and Telecommunications (CN)
- Venue
- IEEE Communications Magazine
- Published
- 2018-04
- Volume
- 56
- Issue
- 4
- Pages
- 53-59
- ISSN
- 0163-6804 (ISSN-L)
- 1558-1896
- OpenAlex
- W2797485305
- Cited by
- 40
- References
- 12
- Citations by year
- 2026: 1 · 2024: 3 · 2023: 4 · 2022: 10 · 2021: 11 · 2020: 6 · 2019: 5
- FWCI
- 2.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Communications Magazine (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Physical Sciences › Engineering › Media Technology › RFID technology advancements
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Keywords
- smart healthcare, visually impaired assistance, RSSI fingerprinting, obstacle avoidance
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