Abstract
The rapid development of Internet of Things (IoT) has opened new opportunities for healthcare systems through so-called eHealth systems. With the help of monitoring using portable IoT devices with biomedical sensors, disease diagnoses can be conducted in real time. However, there is a challenge in that monitoring is an always-on activity that requires constant power supply and IoT devices are battery-powered and face heavy resource constraints. This work addresses a realistic implementation of a low-power eHealth device using both hardware and software approaches. We realize various lightweight eHealth applications (particularly monitoring applications) using a memory-conscious dynamic time warping (DTW) algorithm to be deployed on a small and low-power embedded processor. Actual prototypes of the processor are currently being fabricated. Our evaluation showcases the effectiveness of our work compared with other state-of-the-art embedded processors in terms of circuit area (fabrication cost) and power efficiency. We also demonstrated the scalability of the software implementation by varying the amount of data used in DTW for various eHealth monitoring applications.
Highlights
Advances in Internet of Things (IoT) technologies have highlighted the value of IoT devices in healthcare systems
We provide brief descriptions of our target eHealth applications that can be processed in a lightweight manner, the algorithm with a memory-oriented optimization we adopted, and the SubRISC+-compatible software development flow we built in this work
SETUP In our evaluation, we first demonstrate the effectiveness of the SubRISC+ processor by quantitatively comparing it against other state-of-the-art processors as shown in Table 5 (ARM Cortex-M0, RISC-V zero-riscy, and RISC-V microriscy) in terms of circuit area
Summary
Advances in Internet of Things (IoT) technologies have highlighted the value of IoT devices in healthcare systems. With the help of information and communication technologies in the healthcare domain (i.e., eHealth), a variety of monitoring applications can be realized using real-time data such as accelerometer and biomedical signals like electrocardiogram (ECG), electroencephalogram (EEG), and blood pressure to help with diagnoses and decision making [1]. Applications must be processed in real time (i.e., before the deadline given by an individual application) on small and low-power IoT devices. IoT eHealth devices generally consist of sensing (hereafter, sensor), communication, and processing (hereafter, hardware architecture) subsystems. A variety of small-scale and low-power sensors are expected to be used in eHealth systems such as microelectromechanical
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