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

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

Detection of Beat-to-Beat Intervals from Wrist Photoplethysmography in Patients with Sinus Rhythm and Atrial Fibrillation after Surgery

Wrist photoplethysmography (PPG) allows unobtrusive monitoring of the heart rate (HR). PPG is affected by the capillary blood perfusion and the pumping function of the heart, which generally deteriorate with age and due to presence of cardiac arrhythmia. The performance of wrist PPG in monitoring beat-to-beat HR in older patients with arrhythmia has not been reported earlier. We monitored PPG from wrist in 18 patients recovering from surgery in the post anesthesia care unit, and evaluated the inter-beat interval (IBI) detection accuracy against ECG based R-to-R intervals (RRI). Nine subjects had sinus rhythm (SR, 68.0y$\pm$10.2y, 6 males) and nine subjects had atrial fibrillation (AF, 71.3y$\pm$7.8y, 4 males) during the recording. For the SR group, 99.44% of the beats were correctly identified, 2.39% extra beats were detected, and the mean absolute error (MAE) was 7.34 ms. For the AF group, 97.49% of the heartbeats were correctly identified, 2.26% extra beats were detected, and the MAE was 14.31 ms. IBI from the PPG were hence in close agreement with the ECG reference in both groups. The results suggest that wrist PPG provides a comfortable alternative to ECG and can be used for long-term monitoring and screening of AF episodes.Comment: Submitted to the 2018 IEEE International Conference on Biomedical and Health Informatic

Wearable biosensing: signal processing and communication architectures issues, Journal of Telecommunications and Information Technology, 2005, nr 4

Long-term monitoring of human vital signs is becoming one of the most important fields of research of biomedical engineering. In order to achieve weeks to months of monitoring, new strategies for sensing, conditioning, processing and communication have to be developed. Several strategies are emerging and show different possible architectures. This paper essentially focuses on issues in wearable biosignal processing and communication architecture currently running at the Swiss Center for Electronics andMicrotechnology (CSEM) in the framework of several European projects

Resurgence of Ebola virus in 2021 in Guinea suggests a new paradigm for outbreaks

These authors contributed equally: Alpha K. Keita, Fara R. Koundouno, Martin Faye, Ariane Düx, Julia Hinzmann.International audienc

Domestic electricity consumption data for research and service development

During the last few decades, citizens around western countries became more and more sensible to energy saving. However, while home electricity consumption is a source of concern, the means to reduce this consumption are not easy to _nd and implement for private individuals. Di_erent studies show that displaying the home consumption could lead to a reduction of electricity use. However, the global consumption doesn't provide the consumer with su_cient information about what counts for the main part of their electricity invoice. Systems able to display the energy used by the main appliances would greatly help the consumer to _nd out which equipment should be replaced and/or which behavior should be modi_ed. Implementing such systems requires the disaggregation of the consumption of the main appliances. One solution consists in measuring the global electricity consumption and extracting the most important information from the general load curve using signal processing methods and detection algorithms. The HES-SO Valais-Wallis (University of Applied Sciences Western Switzerland) and the CSEM (Swiss Center for Electronics and Microtechnology) are currently working in this direction. To develop recognition algorithms from an aggregated load curve, an acquisition system able to measure the three phases of a standard household has been built. This system has been deployed in seven households and has been acquiring data sampled at 1Hz for over two years for the _rst deployment site. In two households Ecowizz [1] plugs are used to acquire disaggregated data of the main appliances in parallel to the central measure. In parallel the HES-SO Valais-Wallis is deploying the system in _fty households for one month. The collected data allow a better understanding of the main contributors to the load curve as well as the useful characteristics to recognize them. To _rst tackle the complexity of the aggregated load curve, a simulator of the main contributors (washing machine, dishwasher, tumble dryer, oven, stove, etc.) was also created, thus allowing to initially test the disaggregation algorithms with an a priori knowledge of the contributors. Both the database of real signals and the simulator are new tools that will allow for new research and development of algorithms for the analysis of aggregated load curves

Sun Workstation and SwissNet Platform for Speech Recognition and Speaker Verification over the Telephone

this paper we describe an applied research project entitled "Automatic Speech Recognition in French on Workstation with SwissNet Connection". This cooperative project involves specialists from two research institutes: the Signal Processing Laboratory (LTS) of the Swiss Federal Institute of Technology Lausanne (EPFL) and the Dalle Molle Institute for Perceptive Artificial Intelligence, Martigny (IDIAP), and three industrial partners: aComm,SunMicrosystems (Switzerland) and the Swiss Telecom PTT. The project is supported by the Commission for Technology and Innovation (CTI, formerly CERS). The goal of the project is to make available basic technologies for automatic speech recognition (ASR) and speaker verification (SV) on multi-processor SunSPARCstation 20 and SwissNet platform to industrial partners and particularly to Swiss industry for Swiss French

An Adaptive Organization Index to Characterize Atrial Fibrillation using Wrist-Type Photoplethysmographic Signals

The performance of photoplethysmography (PPG)- based wearable monitors to diagnose atrial fibrillation (AF) remains unknown to date. This study aims at assessing the performance of new indices quantifying the level of organization in PPG signals to diagnose AF. A database made of 18 adult patients undergoing catheter ablation of various cardiac arrhythmias was used. PPG signals were recorded using a wrist-type sensor. A 12-lead ECG was used as gold standard. ECGs were annotated by experts and selected segments were divided into 4 categories: sinus rhythm (SR), regularly paced rhythm (RPR), irregularly paced rhythm (IPR) and AF. The level of organization of the various PPG signals was measured using an adaptive organization index (AOI), defined as the ratio of the power of the fundamental frequency and the first harmonic to the total power of the PPG signal, computed with adaptive band-pass filters. A total of 2806/803/852/287 10-second epochs were considered for AF/SR/RPR/IPR classes. The following mean AOI values were measured: 0.45±0.11 for AF, 0.73±0.19 for SR, 0.78±0.20 for RPR and 0.610.19 for IPR classes. Importantly, the AF AOI was significantly smaller than that of the other categories (p<0.001), indicating a higher degree of disorganization

Can one detect atrial fibrillation using a wrist-type photoplethysmographic device?

This study aims at evaluating the potential of a wrist-type photoplethysmographic (PPG) device to discriminate between atrial fibrillation (AF) and other types of rhythm. Data from 17 patients undergoing catheter ablation of various arrhythmias were processed. ECGs were used as ground truth and annotated for the following types of rhythm: sinus rhythm (SR), AF, and ventricular arrhythmias (VA). A total of 381/1370/415 10-s epochs were obtained for the three categories, respectively. After pre-processing and removal of segments corresponding to motion artifacts, two different types of feature were derived from the PPG signals: the interbeat interval-based features and the wave-based features, consisting of complexity/organization measures that were computed either from the PPG waveform itself or from its power spectral density. Decision trees were used to assess the discriminative capacity of the proposed features. Three classification schemes were investigated: AF against SR, AF against VA, and AF against (SR&VA). The best results were achieved by combining all features. Accuracies of 98.1/95.9/95.0 %, specificities of 92.4/88.7/92.8 %, and sensitivities of 99.7/98.1/96.2 % were obtained for the three aforementioned classification schemes, respectively