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Found 14 papers, 2 papers with code
A Heterogeneous RISC-V based SoC for Secure Nano-UAV Navigation
To the best of the authors' knowledge, it is the first silicon prototype of a ULP SoC coupling the RV64 and RV32 cores in a heterogeneous host+accelerator architecture fully based on the RISC-V ISA.
A Wearable Ultra-Low-Power sEMG-Triggered Ultrasound System for Long-Term Muscle Activity Monitoring
Assuming a muscle contraction of 200 ms at a contraction rate of 1 Hz, the proposed approach enables more than 59% energy saving (with a full-system average power consumption of 12. 2 mW) as compared to operating both sEMG and US continuously.
EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems
The EpiDeNet-SSWCE method demonstrates effective and accurate seizure detection performance on heavily imbalanced datasets, while being suited for implementation on energy-constrained platforms.
BioGAP: a 10-Core FP-capable Ultra-Low Power IoT Processor, with Medical-Grade AFE and BLE Connectivity for Wearable Biosignal Processing
Wearable biosignal processing applications are driving significant progress toward miniaturized, energy-efficient Internet-of-Things solutions for both clinical and consumer applications.
Long-term stable Electromyography classification using Canonical Correlation Analysis
Here we propose a novel statistical method based on canonical correlation analysis (CCA) that stabilizes EMG classification performance across multiple days for long-term control of prosthetic devices.
Energy-Efficient Tree-Based EEG Artifact Detection
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.
Robust and Energy-efficient PPG-based Heart-Rate Monitoring
A wrist-worn PPG sensor coupled with a lightweight algorithm can run on a MCU to enable non-invasive and comfortable monitoring, but ensuring robust PPG-based heart-rate monitoring in the presence of motion artifacts is still an open challenge.
Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable Devices
Our most accurate quantized network achieves 4. 41 Beats Per Minute (BPM) of Mean Absolute Error (MAE), with an energy consumption of 47. 65 mJ and a memory footprint of 412 kB.
Bioformers: Embedding Transformers for Ultra-Low Power sEMG-based Gesture Recognition
Human-machine interaction is gaining traction in rehabilitation tasks, such as controlling prosthetic hands or robotic arms.
Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoring
Photoplethysmography (PPG) sensors allow for non-invasive and comfortable heart-rate (HR) monitoring, suitable for compact wrist-worn devices.
A Fully-Integrated 5mW, 0.8Gbps Energy-Efficient Chip-to-Chip Data Link for Ultra-Low-Power IoT End-Nodes in 65-nm CMOS
The increasing complexity of Internet-of-Things (IoT) applications and near-sensor processing algorithms is pushing the computational power of low-power, battery-operated end-node systems.
Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices
We present the implementation of seizure detection algorithms based on a minimal number of EEG channels on a parallel ultra-low-power embedded platform.
Analysis of Contraction Effort Level in EMG-Based Gesture Recognition Using Hyperdimensional Computing
Varying contraction levels of muscles is a big challenge in electromyography-based gesture recognition.
An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier
We present an end-to-end system combating this variability using a large-area, high-density sensor array and a robust classification algorithm.
