Abstract
Self-powered or autonomously driven wearable devices are touted to revolutionize the personalized healthcare industry, promising sustainable medical care for a large population of healthcare seekers. Current wearable devices rely on batteries for providing the necessary energy to the various electronic components. However, to ensure continuous and uninterrupted operation, these wearable devices need to scavenge energy from their surroundings. Different energy sources have been used to power wearable devices. These include predictable energy sources such as solar energy and radio frequency, as well as unpredictable energy from the human body. Nevertheless, these energy sources are either intermittent or deliver low power densities. Therefore, being able to predict or forecast the amount of harvestable energy over time enables the wearable to intelligently manage and plan its own energy resources more effectively. Several prediction approaches have been proposed in the context of energy harvesting wireless sensor network (EH-WSN) nodes. In their architectural design, these nodes are very similar to self-powered wearable devices. However, additional factors need to be considered to ensure a deeper market penetration of truly autonomous wearables for healthcare applications, which include low-cost, low-power, small-size, high-performance and lightweight. In this paper, we review the energy prediction approaches that were originally proposed for EH-WSN nodes and critique their application in wearable healthcare devices. Our comparison is based on their prediction accuracy, memory requirement, and execution time. We conclude that statistical techniques are better designed to meet the needs of short-term predictions, while long-term predictions require the hybridization of several linear and non-linear machine learning techniques. In addition to the recommendations, we discuss the challenges and future perspectives of these technique in our review.
Highlights
For better management of chronic diseases such as ischemic heart diseases and diabetes, the demand for developing personalized real-time patient monitoring systems is critical [1]
We investigate the relative advantages of each mechanism, while only considering the energy sources that are commonly harvested in wearables
Recent advances in microelectronics, soft computing and artificial intelligence (AI) techniques have fueled the emergence of wearable devices that can be used to monitor vital human signs, detect abnormal behavior or to make the elderly live more independent lives
Summary
For better management of chronic diseases such as ischemic heart diseases and diabetes, the demand for developing personalized real-time patient monitoring systems is critical [1]. Batteries are the most common energy source for wearable devices, but their use impedes perpetual operation due to the battery’s limited lifetime [13], which means that frequent recharging or replacement is necessary In this regard, energy harvesting from the surroundings or from the human body is considered a promising method for deepening the penetration of self-powered wearable devices for patient monitoring using power-efficient and self-sustainable portable devices [14]. In the context of self-powered wearable devices for healthcare applications, the most common sources are solar irradiation [16], radio frequency (RF) [17], thermoelectric energy [18], and mechanical [19] energy owing to their large power conversion density, which is an important attribute for achieving lightweight and small-sized wearable devices Despite their potential and due to the fluctuating nature of these energy sources, all the previously mentioned energy harvesting techniques are either location or time dependent.
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