![]() ![]() It is characterised by seizures that can vary in presentation, from short absences to protracted convulsions. Therefore, by focusing upon personalised parameters that make epilepsy patients distinct from each other this paper proposes an IoT based Epilepsy monitoring model that endorses a more accurate and refined way of remotely monitoring and managing the ‘individual’ patient.Įpilepsy is a neurological disorder that affects 50 million people worldwide. Consequently, paradigms are needed to personalise the information being defined by the condition of these patients each with their very individual signs and symptoms of epilepsy. These extremely varied kind of patients should be monitored precisely according to their key symptoms, hence specific characteristics of each patient should be identified, and medical treatment tailored accordingly. In epilepsy, the most common and complex patients to deal with correspond to those with multiple strands of epilepsy, it is these patients that require long term monitoring assistance. The emerging approach of personalised healthcare is known to be facilitated by the Internet of Things (IoT) and sensor-based IoT devices are in popular demand for healthcare providers due to the constant need for patient monitoring. This paper contributes a perspective on technological stewardship and innovation it identifies the compounding nature of barriers to fairness in the current digital technology ecosystem, and contrasts these with the non-compounding fairness drivers that, in general, establish minimum requirements. It is critical that these failings are identified and addressed to better evolve a fairer future digital technology ecosystem. Additionally, there are concerns about unethical and illegal practices amongst digital technology providers: for example, in planned obsolescence and anti-competitive behaviors, failings in data practices and security, and in responses to problematic use and behaviors. Amongst these are barriers and omissions at the earliest stages of technology intentionality and design systemic inadequacies in sensing systems that deteriorate performance for individuals based on ethnicity, age and physicality system design, co-requisite and interface decisions that limit access biases and inequities in datasets and algorithms and limiting factors in system function and security. Despite drivers supporting and motivating ethical practice in the digital technology ecosystem, there are compounding barriers to fairness that, at every level, impact technology innovation, delivery and access. A growing sense of unfairness permeates our quasi-digital society. ![]()
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