Now showing 1 - 3 of 3
  • Publication
    An Emergency Situation Detection System for Ambient Assisted Living
    This paper proposes “An Emergency Situation Detection System for Ambient Assisted Living (AAL)”, to support elderly people and patients with chronic conditions and potential health-related emergencies to live independently. It implements an Internet of Things (IoT) network that continuously monitors the health conditions of these people. The network includes mobile phones, to transmit the data generated by the IoT sensors to the cloud server. Especially, the paper proposes the 3 rd party unknown mobile relays instead of dedicated gateways as opposed to many existing solutions for IoT healthcare applications. The wireless communication technology used to provide the connectivity between the sensor nodes and mobile relays is Bluetooth Low Energy (BLE). To establish a secure end-to-end connectivity between low power IoT sensor nodes and cloud servers, the paper proposes several techniques. After the medical data transmission to the cloud server, it is responsible for emergency detection and alert generation accordingly. The type of emergency is not limited to a specific health issue, but new emergency situations can be defined and added to the proposed system. Ultimately, the interested parties such as family members, caretakers and doctors receive these alerts. The development of a prototype of the system as a part of the work using commercial off-the-shelf devices verifies the validity of the proposing system and evaluates the performance advantage over the existing systems.
      339Scopus© Citations 11
  • Publication
    Security enhanced Emergency Situation Detection System for Ambient Assisted Living
    Typical wearable devices use a dedicated mobile phone as relay node to transfer the collected sensor data to a server. However, such relay nodes can be faulty or inactive due to various reasons, leading to interruptions of the communication link. To mitigate this challenge, we propose a novel security-enhanced emergency situation detection system, where 3\textsuperscript{rd} party unknown mobile relays are used instead of dedicated gateways as opposed to many existing solutions for IoT healthcare applications. The proposed underlying key agreement and authentication scheme ensures anonymity and untraceability for both sensors (wearable devices) and relay nodes, and relies on symmetric key-based operations to function under resource-constrained environments. We have also developed a prototype of the system using commercial off-the-shelf devices to verify the proposed method's validity and evaluate the performance advantage over existing approaches. Bluetooth Low Energy (BLE) communication technology is used to connect sensor nodes (wearable devices) and mobile relays. After sending medical data to the cloud server, the relay node is responsible for emergency detection and alert generation.
      24Scopus© Citations 8
  • Publication
    Social Interaction Tracking and Patient Prediction System for Potential COVID-19 Patients
    Coronavirus disease 2019 (COVID-19) virus is an infectious disease which has spread globally since 2019, resulting in an ongoing pandemic. Since it is a new virus, it takes some time to develop a vaccine against it. Until then, the best way to prevent the fast spread of the virus is to enable the proper social distancing and isolation or containment to identify potential patients. Since the virus has up to 14 days of the incubation period, it is important to identify all the social interactions during this period and enforce social isolation for such potential patients. However, proper social interaction tracking methods and patient prediction methods based on such data are missing for the moment. This paper focuses on tracking the social interaction of users and predict the infection possibility based on social interactions. We first developed a BLE (Bluetooth Low Energy) and GPS based social interaction tracking system. Then, we developed an algorithm to predict the possibility of being infected with COVID-19 based on the collected data. Finally, a prototype of the system is implemented with a mobile app and a web monitoring tool. In addition, we performed a simulation of the system with a graph-based model to analyze the behaviour of the proposed algorithm and it verifies that self-isolation is important in slowing down the disease progression.
      10Scopus© Citations 13