Exploiting Security Issues in Human Activity Recognition Systems (HARSs)

Human activity recognition systems (HARSs) are vital in a wide range of real-life applications and vibrant academic research area. Although they adopted many fields, such as the environment, agriculture, healthcare considered assistive technology, seem to neglect aspects security privacy. This problem occurs due pervasive nature sensor-based HARSs. Sensors devices with low power computational capabilities, joining machine learning application that lies dynamic heterogeneous communication there is no generalized unified approach evaluate their security/privacy, but rather only individual solutions. In this work, we studied HARSs particular tried extend existing techniques for these considering security/privacy all participating components. Initially, present architecture medical IoT data flow across entities. Then, briefly review privacy issues possible vulnerabilities each system layer. We introduce an over layer offers mutual authentication, solving issues, particularly man-in-the-middle attack (MitM). Relying on proposed solutions, manage prevent unauthorized access critical information by providing trustworthy application.