A Biomechatronic Approach to Evaluating the Security of Wearable Devices in the Internet of Medical Things
<|reference_start|>A Biomechatronic Approach to Evaluating the Security of Wearable Devices in the Internet of Medical Things: The Internet of Medical Things (IoMT) has the potential to revolutionize healthcare by reducing human error and improving patient health. For instance, wearable smart infusion pumps can accurately administer medication and integrate with electronic health records. These pumps can alert healthcare professionals or remote servers when an operation fails, preventing distressing incidents. However, as the number of connected medical devices increases, so does the risk of cyber threats. Wearable medication devices based on IoT attached to patients' bodies are particularly vulnerable to significant cyber threats. Since they are connected to the internet, these devices can be exposed to potential harm, which can disrupt or degrade device performance and harm patients. Therefore, it is crucial to establish secure data authentication for internet-connected medical devices to ensure patient safety and well-being. It is also important to note that the wearability option of such devices might downgrade the computational resources, making them more susceptible to security risks. We propose implementing a security approach for a wearable infusion pump to mitigate cyber threats. We evaluated the proposed architecture with 20, 50, and 100 users for 10 minutes and repeated the evaluation 10 times with two infusion settings, each repeated five times. The desired volumes and rates for the two settings were 2 ml and 4 ml/hr and 5 ml and 5 ml/hr, respectively. The maximum error in infusion rate was measured to be 2.5%. We discuss the practical challenges of implementing such a security-enabled device and suggest initial solutions.<|reference_end|>
@article{vaseghi2024a,
    title={A Biomechatronic Approach to Evaluating the Security of Wearable Devices
  in the Internet of Medical Things},
    author={Yas Vaseghi, Behnaz Behara, and Mehdi Delrobaei},
    journal={arXiv preprint arXiv:2406.14996},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14996},
    primaryClass={cs.CR cs.SY eess.SY}
}