1,721,076 research outputs found
Compressed sensing based seizure detection for an ultra low power multi-core architecture
Full text linkExtracting information from brain signals in advanced Brain Machine Interfaces (BMI) often requires computationally demanding processing. The complexity of the algorithms traditionally employed to process multi-channel neural data, such as Principal Component Analysis (PCA), dramatically increases while scaling-up the number of channels and requires more power-hungry computational platforms. This could hinder the development of low-cost and low-power interfaces which can be used in wearable or implantable real-Time systems. This work proposes a new algorithm for the detection of epileptic seizure based on compressively sensed EEG information, and its optimization on a low-power multi-core SoC for near-sensor data analytics: Mr. Wolf. With respect to traditional algorithms based on PCA, the proposed approach reduces the computational complexity by 4.4x in ARM Cortex M4-based MCU. Implementing this algorithm on Mr.Wolf platform allows to detect a seizure with 1 ms of latency after acquiring the EEG data for 1 s, within an energy budget of 18.4 μJ. A comparison with the same algorithm on a commercial MCU shows an improvement of 6.9x in performance and up to 18.4x in terms of energy efficiency
Balancing Accuracy and Energy Efficiency on Ultra-Low-Power Platforms for ECG Analysis
No full textThe widespread diffusion of long-term cardiac monitoring using wearable devices is a key opportunity for analyzing the health conditions of chronic patients. The continuous analysis of the heartbeat, even reduced to minimal configuration (i.e., two leads), can diagnose and keep track of many severe cardiac conditions, such as abnormal atrial and ventricular contractions. Since wearable devices are battery-powered, it is essential to design solutions that can improve the power efficiency of this monitoring, leveraging HW/SW optimization on low-power platforms. State-of-the-art algorithms based on advanced machine learning (ML) approaches achieve high accuracy but are extremely demanding in terms of energy consumption. In the context of battery-powered devices, determining a trade-off between accuracy and energy consumption is paramount to extending battery lifetime. This work presents a system design for analyzing the Electrocardiogram (ECG) signal to detect pathological conditions using an energy-efficient methodology based on Convolutional Neural Networks (CNNs). We assessed our solution on GAP9, a parallel microcontroller-class platform based on the RISC-V architecture. We achieved a 95.0% accuracy on the MIT-BIH Arrhythmia dataset, which includes five classes of pathological conditions. This value is marginally lower (3%) than the current state-of-the-art based on transformers. However, we identified the best energy-accuracy trade-off configuration, reducing the energy consumption of 3 x (0.03 mJ vs. 0.09 mJ) which guarantees a longer battery lifetime for critical applications
BioWolf16: a 16-channel, 24-bit, 4kSPS Ultra-Low Power Platform for Wearable Clinical-grade Bio-potential Parallel Processing and Streaming
No full textLow-power wearable systems are essential for medical and industrial applications, but they face crucial implementation challenges when providing energy-efficient compact design while increasing the number of available channels, sampling rate and overall processing power. This work presents a small (39×41mm) wireless embedded low-power HMI device for ExG signals, offering up to 16 channels sampled at up to 4kSPS. By virtue of the high sampling rate and medical-grade signal quality (i.e. compliant with the IFCN standards), BioWolf16 is capable of accurate gesture recognition and enables the possibility to acquire data for neural spikes extraction. When employed over an EMG gesture recognition paradigm, the system achieves 90.24% classification accuracy over nine gestures (16 channels @4kSPS) while requiring only 16mW of power (57h of continuous operation) when deployed on Mr. Wolf MCU, part of the system architecture. The system can also provide up to 14h of real-time data streaming (4kSPS), which can further be extended to 23h when reducing the sampling rate to 1kSPS. Our results also demonstrate that this design outperforms many features of current state-of-the-art systems. Clinical Relevance - This work establishes that BioWolf16 is a wearable ultra-low power device enabling advanced multi-channel streaming and processing of medical-grade EMG signal, that can expand research opportunities and applications in healthcare and industrial scenarios
Real-Time Motor Unit Tracking from sEMG Signals with Adaptive ICA on a Parallel Ultra-Low Power Processor
No full textSpike extraction by blind source separation (BSS) algorithms can successfully extract physiologically meaningful information from the sEMG signal, as they are able to identify motor unit (MU) discharges involved in muscle contractions. However, BSS approaches are currently restricted to isometric contractions, limiting their applicability in real-world scenarios. We present a strategy to track MUs across different dynamic hand gestures using adaptive independent component analysis (ICA): first, a pool of MUs is identified during isometric contractions, and the decomposition parameters are stored; during dynamic gestures, the decomposition parameters are updated online in an unsupervised fashion, yielding the refined MUs; then, a Pan-Tompkins-inspired algorithm detects the spikes in each MUs; finally, the identified spikes are fed to a classifier to recognize the gesture. We validate our approach on a 4-subject, 7-gesture + rest dataset collected with our custom 16-channel dry sEMG armband, achieving an average balanced accuracy of 85.58±14.91% and macro-F1 score of 85.86±14.48%. We deploy our solution onto GAP9, a parallel ultra-low-power microcontroller specialized for computation-intensive linear algebra applications at the edge, obtaining an energy consumption of 4.72 mJ @ 240 MHz and a latency of 121.3 ms for each 200 ms-long window of sEMG signal
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
sEMG-driven Hand Dynamics Estimation with Incremental Online Learning on a Parallel Ultra-Low-Power Microcontroller
Full text linkSurface electromyography (sEMG) is a State-of-the-Art (SoA) sensing modality for non-invasive human-machine interfaces for consumer, industrial, and rehabilitation use cases. The main limitation of the current sEMG-driven control policies is the sEMG’s inherent variability, especially cross-session due to sensor repositioning; this limits the generalization of the Machine/Deep Learning (ML/DL) in charge of the signal-to-command mapping. The other hot front on the ML/DL side of sEMG-driven control is the shift from the classification of fixed hand positions to the regression of hand kinematics and dynamics, promising a more versatile and fluid control. We present an incremental online-training strategy for sEMG-based estimation of simultaneous multi-finger forces, using a small Temporal Convolutional Network suitable for embedded learning-on-device. We validate our method on the HYSER dataset, cross-day. Our incremental online training reaches a cross-day Mean Absolute Error (MAE) of (9.58 ± 3.89)% of the Maximum Voluntary Contraction on HYSER’s RANDOM dataset of improvised, non-predefined force sequences, which is the most challenging and closest to real scenarios. This MAE is on par with an accuracy-oriented, non-embeddable offline training exploiting more epochs. Further, we demonstrate that our online training approach can be deployed on the GAP9 ultra-low power microcontroller, obtaining a latency of 1.49 ms and an energy draw of just 40.4 uJ per forward-backward-update step. These results show that our solution fits the requirements for accurate and real-time incremental training-on-device
An Adaptive Dynamic Mixing Model for sEMG Real-Time ICA on an Ultra-Low Power Processor
No full textSurface electromyography (sEMG) is a State-of-the-Art (SoA) data source for natural and dexterous control in human-machine interaction for industrial, commercial, and rehabilitation use cases. Despite non-invasiveness and versatility, a major challenge for sEMG-based control is the inherent presence of many signal variability factors, which hamper the generalization of automated learning models. In this work, we propose an unsupervised adaptation technique for sEMG classification and apply it to arm posture variability. The approach relies on aligning the Principal Components (PCs) of new data with the PCs of the training set. No classifier retraining is required, and the PCs are estimated online, consuming one sample at a time without storing any data. We validate our method on the UniBo-INAIL dataset, showing that it recovers 37% to 51% of the inter-posture accuracy drop. We deploy our solution on GAP9, a parallel ultra-low-power microcontroller, obtaining a latency within 3.57 ms and an energy consumption within 0.125 mJ per update step. These values satisfy the constraints for real-time operation on embedded devices. Our solution is unsupervised and thus suitable for real incremental learning conditions where ground truth is not available
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