Unsupervised Feature Extraction from Raw Data for Gesture Recognition with Wearable Ultra Low-Power Ultrasound.

Wearable ultrasound is a novel sensing approach that shows promise in multiple application domains, and specifically in hand gesture recognition. In fact, ultrasound enables to collect information from deep musculoskeletal structures at high spatiotemporal resolution and high signal-to-noise ratio, making it a perfect candidate to complement surface electromyography for improved accuracy performance and on-the-edge classification. However, existing wearable solutions for ultrasound-based gesture recognition are not sufficiently low-power for continuous, long-term operation. On top of that, practical hardware limitations of wearable ultrasound devices (limited power budget, reduced wireless throughput, restricted computational power) set the need for the compressed size of models for feature extraction and classification. To overcome these limitations, this paper presents a novel end-to-end approach for feature extraction from raw musculoskeletal ultrasound data suited for edge-computing, coupled with an armband for hand gesture recognition based on a truly wearable (12 cm2, 9 g), ultra-low power (16 mW) ultrasound probe. The proposed approach uses a 1D convolutional autoencoder to compress raw ultrasound data by 20× while preserving the main amplitude features of the envelope signal. The latent features of the autoencoder are used to train an XGBoost classifier for hand gesture recognition on datasets collected with a custom US armband, considering armband removal/repositioning in between sessions. Our approach achieves a classification accuracy of 96%. Furthermore, the proposed unsupervised feature extraction approach offers generalization capabilities for inter-subject use, as demonstrated by testing the pre-trained Encoder on a different subject and conducting post-training analysis, revealing that the operations performed by the Encoder are subject-independent. The autoencoder is also quantized to 8-bit integers and deployed on an ultra-low-power wearable ultrasound probe along with the XGBoost classifier, allowing for a gesture recognition rate ≥ 25 Hz and leading to 21% lower power consumption (at 30 FPS) compared to the conventional approach (raw data transmission and remote processing).

Minimizing artifact-induced false-alarms for seizure detection in wearable EEG devices with gradient-boosted tree classifiers.

Electroencephalography (EEG) is widely used to monitor epileptic seizures, and standard clinical practice consists of monitoring patients in dedicated epilepsy monitoring units via video surveillance and cumbersome EEG caps. Such a setting is not compatible with long-term tracking under typical living conditions, thereby motivating the development of unobtrusive wearable solutions. However, wearable EEG devices present the challenges of fewer channels, restricted computational capabilities, and lower signal-to-noise ratio. Moreover, artifacts presenting morphological similarities to seizures act as major noise sources and can be misinterpreted as seizures. This paper presents a combined seizure and artifacts detection framework targeting wearable EEG devices based on Gradient Boosted Trees. The seizure detector achieves nearly zero false alarms with average sensitivity values of [Formula: see text] for 182 seizures from the CHB-MIT dataset and [Formula: see text] for 25 seizures from the private dataset with no preliminary artifact detection or removal. The artifact detector achieves a state-of-the-art accuracy of [Formula: see text] (on the TUH-EEG Artifact Corpus dataset). Integrating artifact and seizure detection significantly reduces false alarms-up to [Formula: see text] compared to standalone seizure detection. Optimized for a Parallel Ultra-Low Power platform, these algorithms enable extended monitoring with a battery lifespan reaching 300 h. These findings highlight the benefits of integrating artifact detection in wearable epilepsy monitoring devices to limit the number of false positives.

Reducing False Alarms in Wearable Seizure Detection With EEGformer: A Compact Transformer Model for MCUs.

The long-term, continuous analysis of electroencephalography (EEG) signals on wearable devices to automatically detect seizures in epileptic patients is a high-potential application field for deep neural networks, and specifically for transformers, which are highly suited for end-to-end time series processing without handcrafted feature extraction. In this work, we propose a small-scale transformer detector, the EEGformer, compatible with unobtrusive acquisition setups that use only the temporal channels. EEGformer is the result of a hardware-oriented design exploration, aiming for efficient execution on tiny low-power micro-controller units (MCUs) and low latency and false alarm rate to increase patient and caregiver acceptance.Tests conducted on the CHB-MIT dataset show a 20% reduction of the onset detection latency with respect to the state-of-the-art model for temporal acquisition, with a competitive 73% seizure detection probability and 0.15 false-positive-per-hour (FP/h). Further investigations on a novel and challenging scalp EEG dataset result in the successful detection of 88% of the annotated seizure events, with 0.45 FP/h.We evaluate the deployment of the EEGformer on three commercial low-power computing platforms: the single-core Apollo4 MCU and the GAP8 and GAP9 parallel MCUs. The most efficient implementation (on GAP9) results in as low as 13.7 ms and 0.31 mJ per inference, demonstrating the feasibility of deploying the EEGformer on wearable seizure detection systems with reduced channel count and multi-day battery duration.

Energy-Efficient Tree-Based EEG Artifact Detection.

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 EEG Artifact Corpus dataset and focus on the temporal electrodes. First, we extract optimal feature models in the frequency domain using an automated machine learning framework, achieving a 93.95% accuracy, with a 0.838 F1 score for a 4 temporal EEG channel setup. The achieved accuracy levels surpass state-of-the-art by nearly 20%. Then, these algorithms are parallelized and optimized for a PULP platform, achieving a 5.21x improvement of energy-efficient compared to state-of-the-art low-power implementations of artifact detection frameworks. Combining this model with a low-power seizure detection algorithm would allow for 300h of continuous monitoring on a 300 mAh battery in a wearable form factor and power budget. These results pave the way for implementing affordable, wearable, long-term epilepsy monitoring solutions with low false-positive rates and high sensitivity, meeting both patients' and caregivers' requirements. Clinical relevance- The proposed EEG artifact detection framework can be employed on wearable EEG recording devices, in combination with EEG-based epilepsy detection algorithms, for improved robustness in epileptic seizure detection scenarios.

A Wireless System for EEG Acquisition and Processing in an Earbud Form Factor with 600 Hours Battery Lifetime.

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 on-board processing capabilities. Here we overcome all these limitations, presenting an ear-EEG system based on dry electrodes that includes all the acquisition, processing, and connectivity electronics directly in the ear bud. The earpiece is equipped with an ultra-low power analog front-end for analog-to-digital conversion, a low-power MEMS microphone, a low-power inertial measurement unit, and an ARM Cortex-M4 based microcontroller enabling on-board processing and Bluetooth Low Energy connectivity. The system can stream raw EEG data or perform data processing directly in-ear. We test the device by analysing its capability to detect brain response to external auditory stimuli, achieving 4 and 1.3 mW power consumption for data streaming or on board processing, respectively. The latter allows for 600 hours operation on a PR44 zinc-air battery. To the best of our knowledge, this is the first wireless and fully self-contained ear-EEG system performing on-board processing, all embedded in a single earbud. Clinical relevance- The proposed ear-EEG system can be employed for diagnostic tasks such as objective hearing threshold estimations, outside of clinical settings, thereby enabling it as a point-of-care solution. The long battery lifetime is also suitable for a continuous monitoring scenario.

Electrochemical characterization of individual oil micro-droplets by high-frequency nanocapacitor array imaging.

CMOS-based nanocapacitor arrays allow local probing of the impedance of an electrolyte in real time and with sub-micron spatial resolution. Here we report on the physico-chemical characterization of individual microdroplets of oil in a continuous water phase using this new tool. We monitor the sedimentation and wetting dynamics of individual droplets, estimate their volume and infer their composition based on their dielectric constant. From measurements before and after wetting of the surface, we also attempt to estimate the contact angle of individual micron-sized droplets. These measurements illustrate the capabilities and versatility of nanocapacitor array technology.

UStEMG: an Ultrasound Transparent Tattoo-based sEMG System for Unobtrusive Parallel Acquisitions of Muscle Electro-mechanics.

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 inspected by means of ultrasound (US) imaging systems. This work shows for the first time an ultra-lightweight (7 g) electromyography (sEMG) system transparent to ultrasound, which enables the simultaneous acquisition of sEMG and US signals from the same location. The system is based on ultrathin and skin-conformable temporary tattoo electrodes (TTE) made of printed conducting polymer, connected to a tiny, parallel-ultra-low power acquisition platform (BioWolf). US phantom images recorded with the TTE had mean axial and lateral resolutions of 0.90±0.02 mm and 1.058±0.005 mm, respectively. The root mean squares for sEMG signals recorded with the US during biceps contractions were at 57±10 µV and mean frequencies were at 92±1 Hz. We show that neither ultrasound images nor electromyographic signals are significantly altered during parallel and synchronized operation.Clinical relevance- Modern prosthetic engineering concepts use interfaces connected to muscles or nerves and employ machine learning models to infer on natural movement behavior of amputated limbs. However, relying only on a single data source (e.g., electromyography) reduces the quality of a fine-grained motor control. To address this limitation, we propose a new and unobtrusive device capable of capturing the electrical and mechanical behavior of muscles in a parallel and synchronized fashion. This device can support the development of new prosthetic control and design concepts, further supporting clinical movement science in the configuration of better simulation models.

LightSpeed: A Compact, High-Speed Optical-Link-Based 3D Optoacoustic Imager.

Wide-scale adoption of optoacoustic imaging in biology and medicine critically depends on availability of affordable scanners combining ease of operation with optimal imaging performance. Here we introduce LightSpeed: a low-cost real-time volumetric handheld optoacoustic imager based on a new compact software-defined ultrasound digital acquisition platform and a pulsed laser diode. It supports the simultaneous signal acquisition from up to 192 ultrasound channels and provides a hig-bandwidth direct optical link (2x 100G Ethernet) to the host-PC for ultra-high frame rate image acquisitions. We demonstrate use of the system for ultrafast (500Hz) 3D human angiography with a rapidly moving handheld probe. LightSpeed attained image quality comparable with a conventional optoacoustic imaging systems employing bulky acquisition electronics and a Q-switched pulsed laser. Our results thus pave the way towards a new generation of compact, affordable and high-performance optoacoustic scanners.

A CMOS Pixelated Nanocapacitor Biosensor Platform for High-Frequency Impedance Spectroscopy and Imaging.

We describe the realization of a fully electronic label-free temperature-controlled biosensing platform aimed to overcome the Debye screening limit over a wide range of electrolyte salt concentrations. It is based on an improved version of a 90-nm CMOS-integrated circuit featuring a nanocapacitor array, readout and A/D conversion circuitry, and a field programmable gate array (FPGA)-based interface board with NIOS II soft processor. We describe chip's processing, mounting, microfluidics, temperature control system, as well as the calibration and compensation procedures to reduce systematic errors, which altogether make up a complete quantitative sensor platform. Capacitance spectra recorded up to 70 MHz are shown and successfully compared to predictions by finite element method (FEM) numerical simulations in the Poisson-Drift-Diffusion formalism. They demonstrate the ability of the chip to reach high upper frequency of operation, thus overcoming the low-frequency Debye screening limit at nearly physiological salt concentrations in the electrolyte, and allowing for detection of events occurring beyond the extent of the electrical double layer. Furthermore, calibrated multifrequency measurements enable quantitative recording of capacitance spectra, whose features can reveal new properties of the analytes. The scalability of the electrode dimensions, interelectrode pitch, and size of the array make this sensing approach of quite general applicability, even in a non-bio context (e.g., gas sensing).

On the Response of Nanoelectrode Impedance Spectroscopy Measures to Plant, Animal, and Human Viruses.

A simplified lumped geometrical and electrical model for the high-frequency impedance spectroscopy (HFIS) response of nanoelectrodes to capsids and full viruses is developed starting from atomistic descriptions, in order to test the theoretical response of a realistic HFIS CMOS biosensor platform to different viruses. Capacitance spectra are computed for plant (cowpea chlorotic mottle virus), animal (rabbit haemorrhagic disease virus), and human (hepatitis A virus) viruses. A few common features of the spectra are highlighted, and the role of virus charge, pH, and ionic strength on the expected signal is discussed. They suggest that the frequency of highest sensitivity at nearly physiological concentrations (100 mM) is within reach of existing HFIS platform designs.

High-Frequency Nanocapacitor Arrays: Concept, Recent Developments, and Outlook.

We have developed a measurement platform for performing high-frequency AC detection at nanoelectrodes. The system consists of 65 536 electrodes (diameter 180 nm) arranged in a sub-micrometer rectangular array. The electrodes are actuated at frequencies up to 50 MHz, and the resulting AC current response at each separately addressable electrode is measured in real time. These capabilities are made possible by fabricating the electrodes on a complementary metal-oxide-semiconductor (CMOS) chip together with the associated control and readout electronics, thus minimizing parasitic capacitance and maximizing the signal-to-noise ratio. This combination of features offers several advantages for a broad range of experiments. First, in contrast to alternative CMOS-based electrical systems based on field-effect detection, high-frequency operation is sensitive beyond the electrical double layer and can probe entities at a range of micrometers in electrolytes with high ionic strength such as water at physiological salt concentrations. Far from being limited to single- or few-channel recordings like conventional electrochemical impedance spectroscopy, the massively parallel design of the array permits electrically imaging micrometer-scale entities with each electrode serving as a separate pixel. This allows observation of complex kinetics in heterogeneous environments, for example, the motion of living cells on the surface of the array. This imaging aspect is further strengthened by the ability to distinguish between analyte species based on the sign and magnitude of their AC response. Finally, we show here that sensitivity down to the attofarad level combined with the small electrode size permits detection of individual 28 nm diameter particles as they land on the sensor surface. Interestingly, using finite-element methods, it is also possible to calculate accurately the full three-dimensional electric field and current distributions during operation at the level of the Poisson-Nernst-Planck formalism. This makes it possible to validate the interpretation of measurements and to optimize the design of future experiments. Indeed, the complex frequency and spatial dependence of the data suggests that experiments to date have only scratched the surface of the method's capabilities. Future iterations of the hardware will take advantage of the higher frequencies, higher electrode packing densities and smaller electrode sizes made available by continuing advances in CMOS manufacturing. Combined with targeted immobilization of targets at the electrodes, we anticipate that it will soon be possible to realize complex biosensors based on spatial- and time-resolved nanoscale impedance detection.