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An Optimized Heart Rate Detection System Based onLow-Power Microcontroller Platforms forBiosignal Processing
(2023)Lecture Notes in Networks and Systems ~ Advances in System-Integrated IntelligenceThe real-time detection of the R peaks of the ECG signal is crucial to provide information on cardiac functionality, and several strategies have been presented in the past. In this work, we adapt the classical Pan and Tompkins (PT) algorithm for efficient execution on low-power microcontroller (MCU) platforms to design a full-fledged heart rate detection system. We target a commercial MCU based on ARM Cortex-M4 and an ultra-low-power ...Conference Paper -
ControlPULP: A RISC-V Power Controller for HPC Processors with Parallel Control-Law Computation Acceleration
(2022)Lecture Notes in Computer Science ~ Embedded Computer Systems: Architectures, Modeling, and SimulationConference Paper -
EEGformer: Transformer-Based Epilepsy Detection on Raw EEG Traces for Low-Channel-Count Wearable Continuous Monitoring Devices
(2022)2022 IEEE Biomedical Circuits and Systems Conference (BioCAS)The development of a device for long-term and continuous monitoring of epilepsy is a very challenging objective, due to the high accuracy standards and nearly zero false alarms required by clinical practices. To comply with such requirements, most of the approaches in the literature rely on a high number of acquisition channels and exploit classifiers operating on pre-processed features, hand-crafted considering the available data, currently ...Conference Paper -
Bioformers: Embedding Transformers for Ultra-Low Power sEMG-based Gesture Recognition
(2022)2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)Human-machine interaction is gaining traction in rehabilitation tasks, such as controlling prosthetic hands or robotic arms. Gesture recognition exploiting surface electromyographic (sEMG) signals is one of the most promising approaches, given that sEMG signal acquisition is non-invasive and is directly related to muscle contraction. However, the analysis of these signals still presents many challenges since similar gestures result in ...Conference Paper -
Energy-Efficient Tree-Based EEG Artifact Detection
(2022)2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)In the context of epilepsy monitoring, EEG artifacts are often mistaken for seizures due to their morphological simi-larity in both amplitude and frequency, making seizure detection systems susceptible to higher false alarm rates. In this work we present the implementation of an artifact detection algorithm based on a minimal number of EEG channels on a parallel ultra-low-power (PULP) embedded platform. The analyses are based on the TUH ...Conference Paper -
A Wireless System for EEG Acquisition and Processing in an Earbud Form Factor with 600 Hours Battery Lifetime
(2022)2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)In recent years, in-ear electroencephalography (EEG) was demonstrated to record signals of similar quality compared to standard scalp-based EEG, and clinical applications of objective hearing threshold estimations have been reported. Existing devices, however, still lack important features. In fact, most of the available solutions are based on wet electrodes, require to be connected to external acquisition platforms, or do not offer ...Conference Paper -
Low-latency detection of epileptic seizures from IEEG with temporal convolutional networks on a low-power parallel MCU
(2021)2021 IEEE Sensors Applications Symposium (SAS)Epilepsy is a severe neurological disorder that affects about 1% of the world population, and one-third of cases are drug-resistant. Apart from surgery, drug-resistant patients can benefit from closed-loop brain stimulation, eliminating or mitigating the epileptic symptoms. For the closed-loop to be accurate and safe, it is paramount to couple stimulation with a detection system able to recognize seizure onset with high sensitivity and ...Conference Paper -
UStEMG: an Ultrasound Transparent Tattoo-based sEMG System for Unobtrusive Parallel Acquisitions of Muscle Electro-mechanics
(2021)2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)Human machine interfaces follow machine learning approaches to interpret muscles states, mainly from electrical signals. These signals are easy to collect with tiny devices, on tight power budgets, interfaced closely to the human skin. However, natural movement behavior is not only determined by muscle activation, but it depends on an orchestration of several subsystems, including the instantaneous length of muscle fibers, typically ...Conference Paper -
Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices
(2021)2021 IEEE Biomedical Circuits and Systems Conference (BioCAS)We present the implementation of seizure detection algorithms based on a minimal number of EEG channels on a parallel ultra-low-power embedded platform. The analyses are based on the CHB-MIT dataset, and include explorations of different classification approaches (Support Vector Machines, Random Forest, Extra Trees, AdaBoost) and different pre/post-processing techniques to maximize sensitivity while guaranteeing no false alarms. We analyze ...Conference Paper -
Tackling time-variability in semg-based gesture recognition with on-device incremental learning and temporal convolutional networks
(2021)2021 IEEE Sensors Applications Symposium (SAS)Human-machine interaction is showing promising results for robotic prosthesis control and rehabilitation. In these fields, hand movement recognition via surface electromyographic (sEMG) signals is one of the most promising approaches. However, it still suffers from the issue of sEMG signal's variability over time, which negatively impacts classification robustness. In particular, the non-stationarity of input signals and the surface ...Conference Paper