In the last years, remote health monitoring is becoming an essential branch of health care with the rapid development of wearable sensors technology. To meet the demand of new more complex applications and ensuring adequate battery lifetime, wearable sensors have evolved into multicore systems with advanced power-saving capabilities and additional heterogeneous components. In this article, we present an approach that applies optimization and parallelization techniques uncovered by modern ultralow power (ULP) platforms in the SW layers with the goal of improving the mapping and reducing the energy consumption of biomedical applications. Additionally, we investigate the benefit of integrating domain-specific accelerators to further reduce the energy consumption of the most computationally expensive kernels. Using 30-s excerpts of signals from two public databases, we apply the proposed optimization techniques on well-known modules of biomedical benchmarks from the state-of-the-art and two complete applications. We observe speed-ups of 5.17× and energy savings of 41.6% for the multicore implementation using a cluster of 8 cores with respect to single-core wearable sensor designs when processing a standard 12-lead electrocardiogram (ECG) signal analysis. Additionally, we conclude that the minimum workload required to take advantage of parallelization for a heartbeat classifier corresponds to the processing of 3-lead ECG signals, with a speed-up of 2.96× and energy savings of 19.3%. Moreover, we observe additional energy savings of up to 7.75% and 16.8% by applying power management and memory scaling to the multicore implementation of the 3-lead beat classifier and 12-lead ECG analysis, respectively. Finally,by integrating hardware (HW) acceleration we observe overall energy savings of up to 51.3% for the 12-lead ECG analysis.

In the last years, the need for enhancing health and preventing problems with remote monitoring is increasing. A non-invasive low-cost technique for processing bio-signals and monitoring vital parameters, at rest and during physical activity, is the use of wearable PhotoPlethysmoGraphic (PPG) systems. However, in order to detect a relevant vital parameter, such as the heart rate during demanding exercises, motion artifacts must be removed from the signals retrieved. In this paper, we present a fast and easy to implement algorithm to estimate the heart rate value which does not need to reconstruct the noise-free signal nor does it apply adaptive filtering as existing algorithms, thus gaining computational time and stored memory space. The method consists of applying the Fast Fourier Transform on short windows of data and removing motion artifacts relying on single-sided amplitude spectrum analysis of PPG and 3-axis accelerometer signals. The results show that our algorithm manages to remove a wide range of motion artifacts achieving an average absolute error of only 1.27 BPM between the heart rate estimated by the algorithm every second and the ground-truth value. The method was successfully implemented on a wearable PPG device achieving an execution time of 226 ms per second, hence obtaining a battery lifetime of 9.37 days.