RFID Systems in Healthcare Settings and Activity of Daily Living in Smart Homes: A Review

Alsinglawi, Belal; Nguyễn, Quang Vinh; Gunawardana, Upul; Maeder, Anthony; Simoff, Simeon

doi:10.4236/etsn.2017.61001

Cited by 20 publications

(12 citation statements)

References 46 publications

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“…Ambient AR systems aim to collect and process continuous data from various sensors installed in the environments for ambient assisted living (AAL). Numerous research studies have used ambient sensors to recognize PADLs and home tasks [11,12,[44][45][46]. Vanus et al [13] performed the fusion of gas (i.e., carbon dioxide) and audio sensors with humidity and temperature sensors to detect any person in the smart room.…”

Section: Related Workmentioning

confidence: 99%

“…Ambient sensors (including a pressure sensor, infra-red sensor, light sensor, temperature sensor, etc.) are generally adopted for indoor activity monitoring in smart homes and buildings [11][12][13]. In contrast, wearable and smartphone-embedded sensors enable pervasive and continuous monitoring of human activities in diverse environments [14].…”

Section: Introductionmentioning

confidence: 99%

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Daily Living Activity Recognition In-The-Wild: Modeling and Inferring Activity-Aware Human Contexts

et al. 2022

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Advancement in smart sensing and computing technologies has provided a dynamic opportunity to develop intelligent systems for human activity monitoring and thus assisted living. Consequently, many researchers have put their efforts into implementing sensor-based activity recognition systems. However, recognizing people’s natural behavior and physical activities with diverse contexts is still a challenging problem because human physical activities are often distracted by changes in their surroundings/environments. Therefore, in addition to physical activity recognition, it is also vital to model and infer the user’s context information to realize human-environment interactions in a better way. Therefore, this research paper proposes a new idea for activity recognition in-the-wild, which entails modeling and identifying detailed human contexts (such as human activities, behavioral environments, and phone states) using portable accelerometer sensors. The proposed scheme offers a detailed/fine-grained representation of natural human activities with contexts, which is crucial for modeling human-environment interactions in context-aware applications/systems effectively. The proposed idea is validated using a series of experiments, and it achieved an average balanced accuracy of 89.43%, which proves its effectiveness.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Daily Living Activity Recognition In-The-Wild: Modeling and Inferring Activity-Aware Human Contexts

et al. 2022

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“…Widely used approach for recognizing ADL is to attach different sensors to objects of daily use and use the interactions of users with these objects to recognize the activity. Different sensors have been used for this purpose but RFID tags and accelerometer are among the most common ones [88]. Buettner et al [89] proposed a technique using Wireless Identification and Sensing Platform (WISP) which combines passive RFID tag and accelerometer.…”

Section: ) Fall Detectionmentioning

confidence: 99%

Different Approaches for Human Activity Recognition: A Survey

Hussain,

Sheng,

Zhang

2019

Preprint

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Human activity recognition has gained importance in recent years due to its applications in various fields such as health, security and surveillance, entertainment, and intelligent environments. A significant amount of work has been done on human activity recognition and researchers have leveraged different approaches, such as wearable, object-tagged, and devicefree, to recognize human activities. In this article, we present a comprehensive survey of the work conducted over the period 2010-2018 in various areas of human activity recognition with main focus on device-free solutions. The device-free approach is becoming very popular due to the fact that the subject is not required to carry anything, instead, the environment is tagged with devices to capture the required information. We propose a new taxonomy for categorizing the research work conducted in the field of activity recognition and divide the existing literature into three sub-areas: action-based, motion-based, and interactionbased. We further divide these areas into ten different sub-topics and present the latest research work in these sub-topics. Unlike previous surveys which focus only on one type of activities, to the best of our knowledge, we cover all the sub-areas in activity recognition and provide a comparison of the latest research work in these sub-areas. Specifically, we discuss the key attributes and design approaches for the work presented. Then we provide extensive analysis based on 10 important metrics, to give the reader, a complete overview of the state-of-the-art techniques and trends in different sub-areas of human activity recognition. In the end, we discuss open research issues and provide future research directions in the field of human activity recognition.

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“…However, people have concerns about the security and privacy of RFIDbased healthcare systems. And RFID is widely used in healthcare monitoring system for patient safety and traceability [1][2][3][4][5]. With the feature of noncontact automatic identification, in recent years, RFID technology has been applied in healthcare systems for providing intelligent services such as patient monitoring, drug administration, and medical asset tracking.…”

Section: Introductionmentioning

confidence: 99%

“…(1) Active tag and AES encryption are used to ensure the RFID systems' security to resist the typical wireless attacks, and we present a novel security authentication protocol based on Physical Unclonable Function is proposed for RFID healthcare systems (2) In order to deduce the security goal and prove the security of the proposed protocol, we use BAN (proposed by Burrows et al in reference [17]) logic, which is a security analysis model and is used for RFID application systems, to perform security analysis for our proposed protocol (3) The proposed protocol updates the key at real time with the PUF and Cloud technology, which is more effective for privacy protection in RFID healthcare systems…”

Section: Introductionmentioning

confidence: 99%

A Novel Security Authentication Protocol Based on Physical Unclonable Function for RFID Healthcare Systems

Chen

Zhu

et al. 2021

Wireless Communications and Mobile Computing

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The Radio Frequency Identification (RFID) technology has been integrated into healthcare systems for the purpose of improving healthcare management. However, people have concerns about the security and privacy of this kind of RFID systems. In order to solve the security problems faced by RFID-based healthcare systems, a novel security authentication protocol based on Physical Unclonable Function (PUF) and Advanced Encryption Standard (AES) encryption algorithm is designed. The protocol uses PUF technology to output unique and random responses to different excitation inputs, encrypts the authentication information sent by the tag, and uses the AES encryption algorithm to encrypt the authentication information between the cloud database and the reader. At the same time, in the authentication process, once the communicating entity completes the identity authentication of the other two entities, it immediately starts to update the key. The security analysis and formal analysis of BAN (proposed by Burrows et al.) logic prove the security and correctness of the protocol. Analysis results show that the computation cost and security performance of the proposed protocol are better than the compared protocols. Our findings will contribute to further enhancing the security for RFID healthcare systems.

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RFID Systems in Healthcare Settings and Activity of Daily Living in Smart Homes: A Review

Cited by 20 publications

References 46 publications

Daily Living Activity Recognition In-The-Wild: Modeling and Inferring Activity-Aware Human Contexts

Daily Living Activity Recognition In-The-Wild: Modeling and Inferring Activity-Aware Human Contexts

Different Approaches for Human Activity Recognition: A Survey

A Novel Security Authentication Protocol Based on Physical Unclonable Function for RFID Healthcare Systems

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