Smartphone technology has become more popular and innovative over the last few years, and technology companies are now introducing wearable devices into the market. By emerging and converging with technologies such as Cloud, Internet of Things (IoT) and Virtualization, requirements to personal sensor devices are immense and essential to support existing networks, e.g. mobile health (mHealth) as well as IoT users. Traditional physiological and biological medical sensors in mHealth provide health data either periodically or on-demand. Both of these situations can cause rapid battery consumption, consume significant bandwidth, and raise privacy issues, because these sensors do not consider or understand sensor status when converged together. The aim of this research is to provide a novel approach and solution to managing and controlling personal sensors that can be used in various areas such as the health, military, aged care, IoT and sport. This paper presents an inference system to transfer health data collected by personal sensors efficiently and effectively to other networks in a secure and effective manner without burdening workload on sensor devices.
Since innovative smart devices and body sensors including wearables have become prevalent with health informatics such as in Mobile Health (mHealth), we proposed to infer sensed data in sensor nodes to reduce the battery power consumption and bandwidth usage in wireless body area networks. It is critical to raise an alarm when the user is in an urgent situation, which can be done by analysing the sensed data against the user's activity status utilizing accelerometer sensors. However, when the activity changes frequently, there may be an increase in false alarms, which increases sensing and transferring of data, resulting in higher resource consumption. To reduce and mitigate the problem, we propose verifying the alarm and sending a user feedback using a smart device or smartwatch application so that a user can respond to whether the alarm is true or false. This paper presents a user-feedback system for use in activity recognition to mitigate and improve possible false alarm situations, which will consequently result in helping sensors to reduce the frequency of transactions and transmissions in wireless body area networks. As a contribution, the alarm determination can not only improve the accuracy of the alarm by utilising mobile app screen and speech recognition but can also reduce possible false alarms. It may also communicate with their physician in real-time who can assess the status with health data provided by the sensors.
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