25 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
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
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
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
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
Symposiu
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 Sim-to-Real Deep Learning-based Framework for Autonomous Nano-drone Racing
Autonomous drone racing competitions are a proxy to improve unmanned aerial
vehicles' perception, planning, and control skills. The recent emergence of
autonomous nano-sized drone racing imposes new challenges, as their ~10cm form
factor heavily restricts the resources available onboard, including memory,
computation, and sensors. This paper describes the methodology and technical
implementation of the system winning the first autonomous nano-drone racing
international competition: the IMAV 2022 Nanocopter AI Challenge. We developed
a fully onboard deep learning approach for visual navigation trained only on
simulation images to achieve this goal. Our approach includes a convolutional
neural network for obstacle avoidance, a sim-to-real dataset collection
procedure, and a navigation policy that we selected, characterized, and adapted
through simulation and actual in-field experiments. Our system ranked 1st among
seven competing teams at the competition. In our best attempt, we scored 115m
of traveled distance in the allotted 5-minute flight, never crashing while
dodging static and dynamic obstacles. Sharing our knowledge with the research
community, we aim to provide a solid groundwork to foster future development in
this field.Comment: 8 pages, 10 Figures, 3 Tables, This paper has been accepted for
publication in the IEEE Robotics and Automation Letters (RAL). Copyright 2023
IEE
BioWolf16: a 16-channel, 24-bit, 4kSPS Ultra-Low Power Platform for Wearable Clinical-grade Bio-potential Parallel Processing and Streaming
Low-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