Magnetic resonance imaging (MRI) relies on physiological triggering for cardiac gating, typically using the R-peak of an electrocardiogram (ECG). However, ECG-based triggering faces limitations in MRI environments due to challenges in electrode placement, magnetic field interference, and RF-induced heating. Alternatively, contact photoplethysmography (PPG) offers a feasible solution; however, it suffers from reduced accuracy and requires the fixation of a probe on the finger tip. To overcome these limitations, this paper proposes an MRI-compatible system for contactless forehead PPG using bundled fiber-optic guides. The proposed approach eliminates electrical interference and ensures safety. A low-power sensor node is proposed to investigate the trade-offs among signal fidelity, energy efficiency, and system latency in on-device PPG. By combining programmable optical sources, an analog front-end, and BLE connectivity, the platform enables reproducible MRI experimentation. It fully processes the PPG data in just 2.8 ms onboard, utilizing a low-power ARM Cortex-M33 core running at 128 MHz. A feasibility study involving 8 subjects was conducted to evaluate the proposed system and demonstrate the effectiveness of the MRI-compatible contactless PPG using green/red light. Several fiducial points of the PPG waveform - foot, onset, and peak - were evaluated for trigger generation. Despite a physiological delay of ∼100-150 ms relative to the R-peak of a reference ECG, the PPG-based R-peak point is reliably estimated with a jitter of 5.14 ms. The sensor node demonstrated the efficiency of the proposed solution operating with a 520 mAh battery for over 23 hours and integrates custom adapters for 2 m optical guides, ensuring safe electronic placement outside the MRI bore. These results confirm that our system can enable prospective gating of MRI measurements without the practical challenges of securing ECG leads or a fingertip PPG. The proposed system paves the way for a safe, contactless, low-power, self-contained sensor node with onboard processing and an interference-free gating method, with the potential to redefine physiological monitoring workflows in MRI environments, enabling precise synchronization without electromagnetic interference.

CUPIDO (Circuit for Unobtrusive Palpebral Interpretation and Detection Optimization) is an ultra-low-power electrostatic sensor able to convert eye blinks into digital events with a detection sensitivity up to 90.5 %. It can be easily integrated into the rims of smart glasses allowing for contactless interaction without compromising comfort and privacy (since no camera is used). Thanks to its extremely low power consumption (385.1 μW at peak during the blink), CUPIDO can extend battery life in smart glasses, allowing for continuous and real-time (detection latency of approximately 1 ms) monitoring applications like hands-free glasses control, assistive technologies, augmented reality, and drowsiness monitoring while driving.

Achieving Simultaneous Localization and Mapping (SLAM) in an unfamiliar environment is a crucial challenge, especially for robots that rely on efficient on-device processing. While accurate mapping is achievable on high-end robotic systems, it still faces substantial challenges due to hardware and latency constraints, especially on smaller robots with limited power budget. Although machine learning is proving highly effective for robot perception, there is a growing need for lightweight solutions in terms of computation and sensing. This paper presents Nano VS, a lightweight monocular perception layer supporting semantic mapping with less than 1 M parameters. We propose a family of quantized and efficient models integrating emerging attention layers and weight-sharing in a multi-task neural network. Experimental results demonstrate multiple tasks within a single model, including Semantic Segmentation (SS), Feature Detection and Description (FDD), and Visual Place Recognition (VPR). Our findings indicate that multi-tasking effectively reduces computational overhead by eliminating the need for multiple networks. Nano VS achieves 70% classwise mIoU with the cityscapes benchmark and 66% Recall@1 in the Pitts30k challenge on tiny images (120x160 pixels). Finally, this paper implements and evaluates Nano VS on a novel milli-watt multi-core RISC-V Microcontroller (MCU), running the full semantic front-end in as little as 52 ms, consuming only 9 mJ per inference. This work represents a significant step towards making advanced SLAM capabilities accessible to tiny robots, or even faster and energy-efficient SLAM on high-end processors.

Smart glasses with integrated eye tracking technology are revolutionizing diverse fields, from immersive augmented reality experiences to cutting-edge health monitoring solutions. However, traditional eye tracking systems rely heavily on cameras and significant computational power, leading to high-energy demand and privacy issues. Alternatively, systems based on electrooculography (EOG) provide superior battery life but are less accurate and primarily effective for detecting blinks, while being highly invasive. To bridge this gap, the paper introduces ElectraSight, a system built upon a new concept we define as hybrid Electrooculography (hEOG). This approach combines contact and contactless electrodes to create a robust, low-power, and truly non-invasive eye tracking system. To validate our approach, we collected a comprehensive dataset from 20 participants, using a commercial eye-tracker for ground-truth labeling. A lightweight 1D Convolutional Neural Network (CNN), quantized to 4-bit and occupying just 79 kB of memory, performs real-time eye movement classification. Without requiring user-specific calibration, the model achieves 81% accuracy for 10 classes and 92% for 6 classes. Experimental results demonstrate that ElectraSight delivers high accuracy in eye movement and blink classification, with minimal overall movement detection latency (90% within 60ms) and an ultra-low inference time (301 ms). The power consumption settles down to 7.75mW for continuous data acquisition and 46μJ for the tinyML inference. This efficiency enables continuous operation for over 3 days on a compact 175 m A h battery. This work opens new possibilities for eye tracking in commercial applications, offering an unobtrusive solution that enables advancements in user interfaces, health diagnostics, and hands-free control systems.

The use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in applications ranging from surveillance and first-aid missions to industrial automation involving cooperation with other machines or humans. To maximize area coverage and reduce mission latency, swarms of collaborating drones have become a significant research direction. However, this approach requires open challenges in positioning, mapping, and communications to be addressed. This work describes a distributed mapping system based on a swarm of nano-UAVs, characterized by a limited payload of 35 g and tightly constrained onboard sensing and computing capabilities. Each nano-UAV is equipped with four 64-pixel depth sensors that measure the relative distance to obstacles in four directions. The proposed system merges the information from the swarm and generates a coherent grid map without relying on any external infrastructure. The data fusion is performed using the iterative closest point algorithm and a graph-based simultaneous localization and mapping algorithm, running entirely onboard the UAV’s low-power ARM Cortex-M microcontroller with just 192 kB of memory. Field results gathered in three different mazes with a swarm of up to 4 nano-UAVs prove a mapping accuracy of 12 cm and demonstrate that the mapping time is inversely proportional to the number of agents. The proposed framework scales linearly in terms of communication bandwidth and onboard computational complexity, supporting communication between up to 20 nano-UAVs and mapping of areas up to 180 m2 with the chosen configuration requiring only 50 kB of memory.

Autonomously navigating robots need to perceive and interpret their surroundings. Currently, cameras are among the most used sensors due to their high resolution and frame rates at relatively low energy consumption and cost. In recent years, cutting-edge sensors, such as miniaturized depth cameras, have demonstrated strong potential, specifically for nano-size unmanned aerial vehicles (UAVs), where low power consumption, lightweight hardware, and low computational demand are essential. However, cameras are limited to working under good lighting conditions, while depth cameras have a limited range. To maximize robustness, we propose to fuse a millimeter form factor 64 pixel depth sensor and a low-resolution grayscale camera. In this work, a nano-UAV learns to detect and fly through a gate with a lightweight autonomous navigation system based on two tinyML convolutional neural network models trained in simulation, running entirely onboard in 7.6 ms and with an accuracy above 91%. Field tests are based on the Crazyflie 2.1, featuring a total mass of 39 g. We demonstrate the robustness and potential of our navigation policy in multiple application scenarios, with a failure probability down to 1.2 ˙ 10-3 crash/meter, experiencing only two crashes on a cumulative flight distance of 1.7 km.

Perceiving and mapping the surroundings are essential for autonomous navigation in any robotic platform. The algorithm class that enables accurate mapping while correcting the odometry errors present in most robotics systems is Simultaneous Localization and Mapping (SLAM). Today, fully onboard mapping is only achievable on robotic platforms that can host high-wattage processors, mainly due to the significant computational load and memory demands required for executing SLAM algorithms. For this reason, pocket-size hardware-constrained robots offload the execution of SLAM to external infrastructures. To address the challenge of enabling SLAM algorithms on resource-constrained processors, this paper proposes NanoSLAM, a lightweight and optimized end-to-end SLAM approach specifically designed to operate on centimeter-size robots at a power budget of only 87.9mW. We demonstrate the mapping capabilities in real-world scenarios and deploy NanoSLAM on a nano-drone weighing 44g and equipped with a novel commercial RISC-V low-power parallel processor called GAP9. The algorithm, designed to leverage the parallel capabilities of the RISC-V processing cores, enables mapping of a general environment with an accuracy of 4.5cm and an end-to-end execution time of less than 250ms.

Nano-size unmanned aerial vehicles (UAVs) hold enormous potential to perform autonomous operations in complex environments, such as inspection, monitoring or data collection. Moreover, their small size allows safe operation close to humans and agile flight. An important part of autonomous flight is localization, a computationally intensive task, especially on a nano-UAV that usually has strong constraints in sensing, processing and memory. This work presents a real-time localization approach with low-element-count multizone range sensors for resource-constrained nano-UAVs. The proposed approach is based on a novel miniature 64-zone time-of-flight sensor from STMicroelectronics and a RISC-V-based parallel ultra-low-power processor to enable accurate and low latency Monte Carlo Localization on-board. Experimental evaluation using a nano-UAV open platform demonstrated that the proposed solution is capable of localizing on a 31.2 m2map with 0.15 m accuracy and an above 95% success rate. The achieved accuracy is sufficient for localization in common indoor environments. We analyze trade-offs in using full and half-precision floating point numbers as well as a quantized map and evaluate the accuracy and memory footprint across the design space. Experimental evaluation shows that parallelizing the execution for 8 RISC-V cores brings a 7x speedup and allows us to execute the algorithm onboard in real-time with a latency of 0.2-30 ms (depending on the number of particles), while only increasing the overall drone power consumption by 3–7%. Finally, we provide an open-source implementation of our approach.

Decentralized and autonomous control of Unmanned Aerial Vehicle (UAV) swarms is a key enabler for cooperative systems and infrastructure-less formation flights. However, UAVs often lack reliable heading angle measurements, especially in indoor scenarios, space, and GNSS-denied environments, posing an additional observability challenge on range-based relative localization. We tackle this problem by proposing a novel solution enhancing the classical tag-and-anchor trilateration. The proposed solution relies on Ultra-wideband range measurements and addresses the relative pose estimation between pairs of UAVs under relative motion. Furthermore, it does not require any explicit motion pattern or initialization procedure and leverages an approximate maximum-likelihood algorithm to recursively solve the relative localization problem with constant computational complexity. The method has been implemented and demonstrated through field experiments, where a swarm of nano-UAVs positioned themselves with respect to a leader in a nearly-static formation with an average error of 38.5 cm and a convergence time of 25 s. The achieved formation accuracy is similar to the one achieved by the state-of-the-art EKF-based leader-follower methods.

Nanosize drones hold enormous potential to explore unknown and complex environments. Their small size makes them agile and safe for operation close to humans and allows them to navigate through narrow spaces. However, their tiny size and payload restrict the possibilities for onboard computation and sensing, making fully autonomous flight extremely challenging. The first step toward full autonomy is reliable obstacle avoidance, which has proven to be challenging by itself in a generic indoor environment. Current approaches utilize vision-based or 1-D sensors to support nanodrone perception algorithms. This article presents a lightweight obstacle avoidance system based on a novel millimeter form factor 64 pixels multizone time-of-flight (ToF) sensor and a generalized model-free control policy. In-field tests are based on the Crazyflie 2.1, extended by a custom multizone ToF deck, featuring a total flight mass of 35 g. The algorithm only uses 0.3% of the onboard processing power (${210}\,{\mu }\mathrm{{s}}$execution time) with a frame rate of 15 f/s. The presented autonomous nanosize drone reaches 100% reliability at 0.5 m/s in a generic and previously unexplored indoor environment.

Unmanned aerial vehicles (UAVs) are a very active research topic, and especially the nano and micro subclass, characterized by cen-timeter size and minimal on-board computational capabilities, have gained popularity in recent years. These lightweight platforms provide good agility and movement freedom in indoor environments, but it is still a significant challenge to enable autonomous navigation or basic obstacle avoidance capabilities using standard image sensors, due to the limited computational capabilities that can be hosted on-board. This work demonstrates the possibility of using a new multi-zone Time of Flight (ToF) sensor to enhance autonomous navigation with a significantly lower computational load than most common visual-based solutions. Our system proved reliable (>95%) in-field obstacle avoidance capabilities when flying in indoor environments with dynamic obstacles.

Photoplethysmography (PPG) sensors allow for non-invasive and comfortable heart rate (HR) monitoring, suitable for compact wrist-worn devices. Unfortunately, motion artifacts (MAs) severely impact the monitoring accuracy, causing high variability in the skin-to-sensor interface. Several data fusion techniques have been introduced to cope with this problem, based on combining PPG signals with inertial sensor data. Until now, both commercial and reasearch solutions are computationally efficient but not very robust, or strongly dependent on hand-tuned parameters, which leads to poor generalization performance. In this work, we tackle these limitations by proposing a computationally lightweight yet robust deep learning-based approach for PPG-based HR estimation. Specifically, we derive a diverse set of Temporal Convolutional Networks for HR estimation, leveraging Neural Architecture Search. Moreover, we also introduce ActPPG, an adaptive algorithm that selects among multiple HR estimators depending on the amount of MAs, to improve energy efficiency. We validate our approaches on two benchmark datasets, achieving as low as 3.84 beats per minute of Mean Absolute Error on PPG-Dalia, which outperforms the previous state of the art. Moreover, we deploy our models on a low-power commercial microcontroller (STM32L4), obtaining a rich set of Pareto optimal solutions in the complexity vs. accuracy space.

The novel COVID-19 disease has been declared a pandemic event. Early detection of infection symptoms and contact tracing are playing a vital role in containing COVID-19 spread. As demonstrated by recent literature, multi-sensor and connected wearable devices might enable symptom detection and help tracing contacts, while also acquiring useful epidemiological information. This paper presents the design and implementation of a fully open-source wearable platform called H-Watch. It has been designed to include several sensors for COVID-19 early detection, multi-radio for wireless transmission and tracking, a microcontroller for processing data on-board, and finally, an energy harvester to extend the battery lifetime. Experimental results demonstrated only 5.9 mW of average power consumption, leading to a lifetime of 9 days on a small watch battery. Finally, all the hardware and the software, including a machine learning on MCU toolkit, are provided open-source, allowing the research community to build and use the H-Watch.

Ultra-wide band is one of the most promising localization technologies already and increasingly used in industrial environments. It is also starting to be included in many Internet of Things and mobile devices. The most attractive feature of ultra-wide band for localization and positioning is the achievable centimeter-accuracy that can enable a highly secure spoofing-protected application. On the other hand, the main drawback that still limits its use in battery-operated devices is the high power consumption, especially in idle listening. Today most common approaches use duty cycling, which reduces the overall power consumption at the cost of increased latency. The paper presents a hardware wake-up radio with addressing capability for ultra-wide band communication and ranging. Moreover, the paper introduces a novel protocol that enables the generation of wake-up signals directly with any existing ultra-wide band transceiver, without the requirement of a different radio. The proposed solution achieves −48 dBm of sensitivity with only 100 µW of power consumption. The proposed system enables full asynchronous communication, achieving millisecond latency and always-on capability. Experimental evaluation with a popular commercial transceiver produced by Decawave demonstrates the high energy saving and the effectiveness of the proposed addressing protocol by reducing energy consumption in the order of 100x compared to conventional receivers.

Low power and long-range communications are crucial features of the Internet of Things (IoT) paradigm that is becoming essential even for industrial applications. Today, the most promising long-range communication technologies are LoRaWAN and Narrow Band IoT (NB-IoT), which are driving a large IoT ecosystem. In this article, we evaluate the performance of LoRaWAN and NB-IoT with accurate in-field measurements using the same application context for a fair comparison in terms of energy efficiency, lifetime, quality of service, and coverage. The NB-IoT energy transmission is scarcely dependent on the payload length. Thus applications that can tolerate buffering and caching techniques on the node are favored. On the other hand, LoRaWAN consumes 10 × lower energy compared to NB-IoT for occasional and latency-sensitive communications, for which it enables much end-device lifetime. Finally, this paper provides design guidelines for future industrial applications with stringent requirements of long-range and low power wireless connectivity.

Low power and long-range communications are essential features of the Internet of Things (IoT) paradigm that is becoming widespread across a spectrum of industrial applications. In this paper, we present performance evaluation of the most promising long-range communication technologies, namely LoRaWAN and NB-IoT. We present accurate in-field measurements using a monitoring application as a testbench for a fair comparison in terms of energy efficiency and lifetime. Experimental results highlight that NB-IoT payload length does not impact on transmission energy. Thus, applications that implement buffering and caching techniques are favored. On the other hand, LoRaWAN consumes 10× less energy to transmit a payload equivalent to that of NB-IoT, thereby allowing longer end-device lifetime.

Structural health monitoring (SHM) systems are becoming increasingly widespread and are in some cases mandated by law. A major factor limiting the diffusion of such systems is the lack of low-cost low-power sensor nodes, which can be deployed in large numbers in hard-to-reach areas, while providing high-quality precise measurements over their entire lifespan with minimum maintenance and withstanding climatic stress. In this paper, we present a cost-effective wireless component for Structural Health Monitoring (SHM) that measure and track cracks in concrete and other construction materials. The sensor combines a microprocessor with LoRaWAN wireless communication, an analog transducer, and a solar energy harvester, allowing long-term remote monitoring with easy plug and play installation. Experimental results demonstrate that we achieved about 1μm accuracy and an expected lifetime of more than 10 years, with stable measurements across a-IS - 65°C temperature range.

LoRaWAN is one of the most promising standards for IoT applications. Nevertheless, the high density of end-devices expected for each gateway, the absence of an effective synchronization scheme between gateway and end-devices, challenge the scalability of these networks. In this article, we propose to regulate the communication of LoRaWAN networks using a Slotted-ALOHA instead of the classic ALOHA approach used by LoRa. The implementation is an overlay on top of the standard LoRaWAN; thus no modification in pre-existing LoRaWAN firmware and libraries is necessary. Our method is based on a novel distributed synchronization service that is suitable for low-cost IoT end-nodes. S-ALOHA supported by our synchronization service significantly improves the performance of traditional LoRaWAN networks regarding packet loss rate and network throughput.

Wireless sensor networks (WSNs) are today widely employed in real world applications. However, their lifetime is still challenging and the most critical limitation for the success of this technology. In fact, wireless sensors nodes, which are the backbone of the network, are typically powered by limited energy storage devices (i.e. small batteries or supercaps) and their short lifetime is a critical issue. To overcome this limitation a major research effort focuses on reducing power consumption, especially of communication, as the radio transceiver is one of the highest power consumers. A critical energy-efficiency issue in WSN transceivers is idle listening. Wake-up radio receivers are very effective in minimizing idle listening. This fact has resulted in a significant number of wake-up radio receiver architectures proposed in last decade. In this work we present an advanced design and implementation of an advanced wake-up radio that is capable of both processing the received data (i.e. for addressing) and retransmitting data or wake up messages to the neighbours when necessary. With these features it can be possible to further enhance the energy efficiency of the communication and allowing ultra-low power multi-hop communication. Experimental results demonstrate the functionality as well as the power and range of the proposed design which is ready for future energy efficient and pure-asynchronous MAC protocols.