48 research outputs found
Convolutional-Recurrent Neural Networks on Low-Power Wearable Platforms for Cardiac Arrhythmia Detection
Low-power sensing technologies, such as wearables, have emerged in the
healthcare domain since they enable continuous and non-invasive monitoring of
physiological signals. In order to endow such devices with clinical value,
classical signal processing has encountered numerous challenges. However,
data-driven methods, such as machine learning, offer attractive accuracies at
the expense of being resource and memory demanding. In this paper, we focus on
the inference of neural networks running in microcontrollers and low-power
processors which wearable sensors and devices are generally equipped with. In
particular, we adapted an existing convolutional-recurrent neural network,
designed to detect and classify cardiac arrhythmias from a single-lead
electrocardiogram, to the low-power embedded System-on-Chip nRF52 from Nordic
Semiconductor with an ARM's Cortex-M4 processing core. We show our
implementation in fixed-point precision, using the CMSIS-NN libraries, yields a
drop of score from 0.8 to 0.784, from the original implementation, with a
memory footprint of 195.6KB, and a throughput of 33.98MOps/s.Comment: Accepted for presentation in the 2nd IEEE International Conference on
Artificial Intelligence Circuits and Systems (AICAS2020
Learning a Physical Activity Classifier for a Low-power Embedded Wrist-located Device
This article presents and evaluates a novel algorithm for learning a physical
activity classifier for a low-power embedded wrist-located device. The overall
system is designed for real-time execution and it is implemented in the
commercial low-power System-on-Chips nRF51 and nRF52. Results were obtained
using a database composed of 140 users containing more than 340 hours of
labeled raw acceleration data. The final precision achieved for the most
important classes, (Rest, Walk, and Run), was of 96%, 94%, and 99% and it
generalizes to compound activities such as XC skiing or Housework. We conclude
with a benchmarking of the system in terms of memory footprint and power
consumption.Comment: Submitted to the 2018 IEEE International Conference on Biomedical and
Health Informatic
Embedded Deep Learning for Sleep Staging
The rapidly-advancing technology of deep learning (DL) into the world of the
Internet of Things (IoT) has not fully entered in the fields of m-Health yet.
Among the main reasons are the high computational demands of DL algorithms and
the inherent resource-limitation of wearable devices. In this paper, we present
initial results for two deep learning architectures used to diagnose and
analyze sleep patterns, and we compare them with a previously presented
hand-crafted algorithm. The algorithms are designed to be reliable for consumer
healthcare applications and to be integrated into low-power wearables with
limited computational resources
Secure Stream Processing for Medical Data
Medical data belongs to whom it produces it. In an increasing manner, this
data is usually processed in unauthorized third-party clouds that should never
have the opportunity to access it. Moreover, recent data protection regulations
(e.g., GDPR) pave the way towards the development of privacy-preserving
processing techniques. In this paper, we present a proof of concept of a
streaming IoT architecture that securely processes cardiac data in the cloud
combining trusted hardware and Spark. The additional security guarantees come
with no changes to the application's code in the server. We tested the system
with a database containing ECGs from wearable devices comprised of 8 healthy
males performing a standarized range of in-lab physisical activities (e.g.,
run, walk, bike). We show that, when compared with standard Spark Streaming,
the addition of privacy comes at the cost of doubling the execution time
Respiratory and cardiac monitoring at night using a wrist wearable optical system
Sleep monitoring provides valuable insights into the general health of an
individual and helps in the diagnostic of sleep-derived illnesses.
Polysomnography, is considered the gold standard for such task. However, it is
very unwieldy and therefore not suitable for long-term analysis. Here, we
present a non-intrusive wearable system that, by using photoplethysmography, it
can estimate beat-to-beat intervals, pulse rate, and breathing rate reliably
during the night. The performance of the proposed approach was evaluated
empirically in the Department of Psychology at the University of Fribourg. Each
participant was wearing two smart-bracelets from Ava as well as a complete
polysomnographic setup as reference. The resulting mean absolute errors are
17.4 ms (MAPE 1.8%) for the beat-to-beat intervals, 0.13 beats-per-minute (MAPE
0.20%) for the pulse rate, and 0.9 breaths-per-minute (MAPE 6.7%) for the
breath rate.Comment: Submitted to the 40th International Conference of the IEEE
Engineering in Medicine and Biology Society (EMBC