47 research outputs found
Online Alpha Wave detector: an Embedded hardware-software implementation
The recent trend on embedded system development opens a new prospect for applications that in the past were not possible. The eye tracking for sleep and fatigue detection has become an important and useful application in industrial and automotive scenarios since fatigue is one of the most prevalent causes of earth-moving equipment accidents. Typical applications such as cameras, accelerometers and dermal analyzers are present on the market but have some inconvenient. This thesis project has used EEG signal, particularly, alpha waves, to overcome them by using an embedded software-hardware implementation to detect these signals in real tim
Using Low-Power, Low-Cost IoT Processors in Clinical Biosignal Research: An In-depth Feasibility Check
Research on biosignal (ExG) analysis is usually performed with expensive systems requiring connection with external computers for data processing. Consumer-grade low-cost wearable systems for bio-potential monitoring and embedded processing have been presented recently, but are not considered suitable for medical-grade analyses. This work presents a detailed quantitative comparative analysis of a recently presented fully-wearable low-power and low-cost platform (BioWolf) for ExG acquisition and embedded processing with two researchgrade acquisition systems, namely, ANTNeuro (EEG) and the Noraxon DTS (EMG). Our preliminary results demonstrate that BioWolf offers competitive performance in terms of electrical properties and classification accuracy. This paper also highlights distinctive features of BioWolf, such as real-time embedded processing, improved wearability, and energy-efficiency, which allows devising new types of experiments and usage scenarios for medical-grade biosignal processing in research and future clinical studies
An Energy-Efficient IoT node for HMI applications based on an ultra-low power Multicore Processor
Developing wearable sensing technologies and unobtrusive devices is paving the way to the design of compelling applications for the next generation of systems for a smart IoT node for Human Machine Interaction (HMI). In this paper we present a smart sensor node for IoT and HMI based on a programmable Parallel Ultra-Low-Power (PULP) platform. We tested the system on a hand gesture recognition application, which is a preferred way of interaction in HMI design. A wearable armband with 8 EMG sensors is controlled by our IoT node, running a machine learning algorithm in real-time, recognizing up to 11 gestures with a power envelope of 11.84 mW. As a result, the proposed approach is capable to 35 hours of continuous operation and 1000 hours in standby. The resulting platform minimizes effectively the power required to run the software application and thus, it allows more power budget for high-quality AFE
Enhancing Performance, Calibration Time and Efficiency in Brain-Machine Interfaces through Transfer Learning and Wearable EEG Technology
Brain-machine interfaces (BMIs) have emerged as a transformative force in
assistive technologies, empowering individuals with motor impairments by
enabling device control and facilitating functional recovery. However, the
persistent challenge of inter-session variability poses a significant hurdle,
requiring time-consuming calibration at every new use. Compounding this issue,
the low comfort level of current devices further restricts their usage. To
address these challenges, we propose a comprehensive solution that combines a
tiny CNN-based Transfer Learning (TL) approach with a comfortable, wearable EEG
headband. The novel wearable EEG device features soft dry electrodes placed on
the headband and is capable of on-board processing. We acquire multiple
sessions of motor-movement EEG data and achieve up to 96% inter-session
accuracy using TL, greatly reducing the calibration time and improving
usability. By executing the inference on the edge every 100ms, the system is
estimated to achieve 30h of battery life. The comfortable BMI setup with tiny
CNN and TL paves the way to future on-device continual learning, essential for
tackling inter-session variability and improving usability
Low-Power Human-Machine Interfaces: Analysis And Design
Human-Machine Interaction (HMI) systems, once used for clinical applications, have recently reached a broader set of scenarios, such as industrial, gaming, learning, and health tracking thanks to advancements in Digital Signal Processing (DSP) and Machine Learning (ML) techniques. A growing trend is to integrate computational capabilities into wearable devices to reduce power consumption associated with wireless data transfer while providing a natural and unobtrusive way of interaction. However, current platforms can barely cope with the computational complexity introduced by the required feature extraction and classification algorithms without compromising the battery life and the overall intrusiveness of the system. Thus, highly-wearable and real-time HMIs are yet to be introduced.
Designing and implementing highly energy-efficient biosignal devices demands a fine-tuning to meet the constraints typically required in everyday scenarios. This thesis work tackles these challenges in specific case studies, devising solutions based on bioelectrical signals, namely EEG and EMG, for advanced hand gesture recognition.
The implementation of these systems followed a complete analysis to reduce the overall intrusiveness of the system through sensor design and miniaturization of the hardware implementation. Several solutions have been studied to cope with the computational complexity of the DSP algorithms, including commercial single-core and open-source Parallel Ultra Low Power architectures, that have been selected accordingly also to reduce the overall system power consumption. By further adding energy harvesting techniques combined with the firmware and hardware optimization, the systems achieved self-sustainable operation or a significant boost in battery life.
The HMI platforms presented are entirely programmable and provide computational power to satisfy the requirements of the studies applications while employing only a fraction of the CPU resources, giving the perspective of further application more advanced paradigms for the next generation of real-time embedded biosignal processing
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. However, scaling toward high-density
multi-channel front-ends is only feasible by performing data processing and
machine Learning (ML) near-sensor through energy-efficient edge processing. To
tackle these challenges, we introduce BioGAP, a novel, compact, modular, and
lightweight (6g) medical-grade biosignal acquisition and processing platform
powered by GAP9, a ten-core ultra-low-power SoC designed for efficient
multi-precision (from FP to aggressively quantized integer) processing, as
required for advanced ML and DSP. BioGAPs form factor is 16x21x14 mm and
comprises two stacked PCBs: a baseboard integrating the GAP9 SoC, a wireless
Bluetooth Low Energy (BLE) capable SoC, a power management circuit, and an
accelerometer; and a shield including an analog front-end (AFE) for ExG
acquisition. Finally, the system also includes a flexibly placeable
photoplethysmogram (PPG) PCB with a size of 9x7x3 mm and a rechargeable
battery ( 12x5 mm). We demonstrate BioGAP on a Steady State Visually
Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) application. We
achieve 3.6 uJ/sample in streaming and 2.2 uJ/sample in onboard processing
mode, thanks to an efficiency on the FFT computation task of 16.7 Mflops/s/mW
with wireless bandwidth reduction of 97%, within a power budget of just 18.2 mW
allowing for an operation time of 15 h.Comment: 7 pages, 9 figures, 1 table, accepted for IEEE COINS 202
Mean Field Effects In The Quark-Gluon Plasma
A transport model based on the mean free path approach for an interacting
meson system at finite temperatures is discussed. A transition to a quark gluon
plasma is included within the framework of the MIT bag model. The results
obtained compare very well with Lattice QCD calculations when we include a mean
field in the QGP phase due to the Debye color screening. In particular the
cross over to the QGP at about 175 MeV temperature is nicely reproduced. We
also discuss a possible scenario for hadronization which is especially
important for temperatures below the QGP phase transition
LA VERDAD EN EL PROCESO Y EN LA SENTENCIA CIVIL
El presente trabajo aborda la cuestión relacionada a la verdad en el contexto de los procesos y en la sentencia civil. El abordaje de dicho tema se impone ante la constante pregunta de los justiciables sobre el alcance del concepto “verdad” dentro de los procesos judiciales y si con los mismos se logra desentrañar la verdad substancial o al contrario se conforme con una verdad formal. La presente investigación se dilucida en base a los conceptos filosóficos de verdad y especialmente a la luz de la teoría fenomenológica sobre la verdad, basada en un análisis bibliográfico. Como resultado de la investigación se ha arribado a la conclusión de que en los procesos judiciales, si bien desde el punto de vista de finalidad se apunta a la verdad substancial y absoluta, la misma apenas se logra desde una perspectiva formal, ya que en muchas ocasiones las partes no aportan todos los elementos necesarios para llegar a la verdad substancial, estando vedado al juzgador aun normas ordenatorias de por medio, suplir el silencio o la negligencia de las partes para llegar a ella. Teniendo en cuenta que en el proceso civil, se dilucida un conflicto de intereses antes que una cuestión conducente a lograr la verdad a través de los medios probatorios, la verdad substancial queda relegada por la verdad formal o la que las partes en conflicto quieren mostrar a fin de dilucidar a su favor el pleito así instaurado
Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis
Recent studies show that the integrity of core perceptual and cognitive functions may be tested in a short time with Steady-State Visual Evoked Potentials (SSVEP) with low stimulation frequencies, between 1 and 10 Hz. Wearable EEG systems provide unique opportunities to test these brain functions on diverse populations in out-of-the-lab conditions. However, they also pose significant challenges as the number of EEG channels is typically limited, and the recording conditions might induce high noise levels, particularly for low frequencies. Here we tested the performance of Normalized Canonical Correlation Analysis (NCCA), a frequency-normalized version of CCA, to quantify SSVEP from wearable EEG data with stimulation frequencies ranging from 1 to 10 Hz. We validated NCCA on data collected with an 8-channel wearable wireless EEG system based on BioWolf, a compact, ultra-light, ultra-low-power recording platform. The results show that NCCA correctly and rapidly detects SSVEP at the stimulation frequency within a few cycles of stimulation, even at the lowest frequency (4 s recordings are sufficient for a stimulation frequency of 1 Hz), outperforming a state-of-the-art normalized power spectral measure. Importantly, no preliminary artifact correction or channel selection was required. Potential applications of these results to research and clinical studies are discussed
A Wearable Ultra-Low-Power sEMG-Triggered Ultrasound System for Long-Term Muscle Activity Monitoring
Surface electromyography (sEMG) is a well-established approach to monitor
muscular activity on wearable and resource-constrained devices. However, when
measuring deeper muscles, its low signal-to-noise ratio (SNR), high signal
attenuation, and crosstalk degrade sensing performance. Ultrasound (US)
complements sEMG effectively with its higher SNR at high penetration depths. In
fact, combining US and sEMG improves the accuracy of muscle dynamic assessment,
compared to using only one modality. However, the power envelope of US hardware
is considerably higher than that of sEMG, thus inflating energy consumption and
reducing the battery life. This work proposes a wearable solution that
integrates both modalities and utilizes an EMG-driven wake-up approach to
achieve ultra-low power consumption as needed for wearable long-term
monitoring. We integrate two wearable state-of-the-art (SoA) US and ExG
biosignal acquisition devices to acquire time-synchronized measurements of the
short head of the biceps. To minimize power consumption, the US probe is kept
in a sleep state when there is no muscle activity. sEMG data are processed on
the probe (filtering, envelope extraction and thresholding) to identify muscle
activity and generate a trigger to wake-up the US counterpart. The US
acquisition starts before muscle fascicles displacement thanks to a triggering
time faster than the electromechanical delay (30-100 ms) between the
neuromuscular junction stimulation and the muscle contraction. 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.Comment: 4 pages, 5 figures, 1 table, 2023 IEEE International Ultrasonics
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