One of the greatest challenges in the design of modern embedded devices is energy efficiency and memory footprint. While the highly efficient microcontrollers and transmission devices have received a lot of attention from the industry and academia, sensor data acquisition and processing in medical devices is still based on a conservative paradigm that requires regular sampling at a high rate, in the order of hundreds or even thousands of times per second. Thus, smart algorithms and technologies are required to optimize the energy consumption and memory footprint while assuring data quality and integrity.
We work on developing novel biosignal sampling strategies that exploit the temporal properties and the expert knowledge we have to reduce the amount of acquired data by orders of magnitude. For this, we adopt the so-called event-based paradigm, in which the signal is only acquired and processed when something relevant is observed.
Following the event-based strategy it is possible to go even further by adapting the signal quality to the target task. For example, in the image above, we can see an electrocardiographic signal that is sampled with a traditional approach at 360 Hz. Below, the same signal is sampled with an adaptive event-based approach that takes into account the heart rate and the presence of physiological abnormalities. In this way, we are able to reduce the sampling rate to around 6Hz for most of the time, and increase it only when something unexpected is observed in the signal.
|SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices|
|Masinelli, Giulio; Dell'Agnola, Fabio Isidoro Tiberio; Arza Valdes, Adriana; Atienza Alonso, David|
|An Event-Based System for Low-Power ECG QRS Complex Detection|
|2020-03-09||DATE 2020 - Design, Automation & Test In Europe Conference & Exhibition|
|Event-Triggered Sensing for High-Quality and Low-Power Cardiovascular Monitoring Systems|
|Surrel, Grégoire ; Teijeiro, Tomas ; Chevrier, Matthieu ; Aminifar, Amir ; Atienza Alonso, David|
|2020||IEEE Design & Test|
|REWARD: Design, Optimization, and Evaluation of a Real-Time Relative-Energy Wearable R-Peak Detection Algorithm *|
|Orlandic, Lara; De Giovanni, Elisabetta; Arza, Adriana; Yazdani, Sasan; Vesin, Jean-Marc; Atienza, David|
|2019-10-07||2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)|
|e-Glass: A Wearable System for Real-Time Detection of Epileptic Seizures|
|D. Sopic, A. Aminifar, D. Atienza|
|2018||International Symposium on Circuits and Systems (ISCAS)|
|Self-Aware Wearable Systems in Epileptic Seizure Detection|
|F. Forooghifar, A. Aminifar, and D. Atienza Alonso|
|2018||Euromicro Conference on Digital System Design (DSD)|
|A wearable device for physical and emotional health monitoring|
|S. Murali, F. Rincon, D. Atienza|
|2015||Computing in Cardiology Conference (CinC)|