Ensuring robust and real-time obstacle avoidance is critical for the safe operation of autonomous robots in dynamic, real-world environments. This paper proposes a neural network framework for predicting the time and collision position of an unmanned aerial vehicle with a dynamic object, using RGB and event-based vision sensors. The proposed architecture consists of two separate encoder branches, one for each modality, followed by fusion by self-attention to improve prediction accuracy.To facilitate benchmarking, we leverage the ABCD [8] dataset collected that enables detailed comparisons of single-modality and fusion-based approaches.At the same prediction throughput of 50Hz, the experimental results show that the fusion-based model offers an improvement in prediction accuracy over single-modality approaches of 1% on average and 10% for distances beyond 0.5m, but comes at the cost of +71% in memory and + 105% in FLOPs. Notably, the event-based model outperforms the RGB model by 4% for position and 26% for time error at a similar computational cost, making it a competitive alternative.Additionally, we evaluate quantized versions of the event-based models, applying 1- to 8-bit quantization to assess the trade-offs between predictive performance and computational efficiency.These findings highlight the trade-offs of multi-modal perception using RGB and event-based cameras in robotic applications.

Asset tracking solutions have proven their significance in industrial contexts, as evidenced by their successful commercialization (e.g., Hilti On!Track). However, a seamless solution for matching assets with their users, such as operators of construction power tools, is still missing. By enabling asset-user matching, organizations gain valuable insights that can be used to optimize user health and safety, asset utilization, and maintenance. This article introduces a novel approach to address this gap by leveraging existing Bluetooth low energy (BLE)-enabled low-power Internet of Things (IoT) devices. The proposed framework comprises the following components: 1) a wearable device; 2) an IoT device attached to or embedded in the assets; 3) an algorithm to estimate the distance between assets and operators by exploiting simple received signal strength indicator (RSSI) measurements via an extended Kalman filter (EKF); and 4) a cloud-based algorithm that collects all estimated distances to derive the correct asset-operator matching. The effectiveness of the proposed system has been validated through indoor and outdoor experiments in a construction setting for identifying the operator of a power tool. A physical prototype was developed to evaluate the algorithms in a realistic setup. The results demonstrated a median accuracy of 0.49m in estimating the distance between assets and users, and up to 98.6% in correctly matching users with their assets.

This work explores the feasibility of employing ultrasound (US) technology in a wrist-worn Internet-of-Things (IoT) device for low-power, high-fidelity heart rate (HR) extraction. US offers deep tissue penetration and can monitor pulsatile arterial blood flow in large vessels and the surrounding tissue, potentially improving robustness and accuracy compared to photoplethysmogram (PPG). We present an IoT wearable system prototype utilizing a commercial microcontroller (MCU) employing the onboard analogdigital converters (ADC) to capture high-frequency US signals and an innovative low-power US pulser. An envelope filter lowers the bandwidth of the US signal by a factor of >5 x, reducing the systems acquisition requirements without compromising accuracy (correlation coefficient between HR extracted from enveloped and raw signals, r(92)=0.996, p<0.001). The full signal processing pipeline is ported to fixed-point arithmetic for increased energy efficiency and runs entirely onboard. The extracted HR can be transmitted to the cloud via a Bluetooth low energy (BLE) module. The system has an average power consumption of 5.8mW, competitive with commercial PPG based systems, and the HR extraction algorithm requires only 69 kB of RAM and 71 ms of processing time on an ARM Cortex-M4 based MCU. The system is estimated to run continuously on a smartwatch battery for more than 7 days. To accurately evaluate the proposed circuit and algorithm and identify the anatomical location on the wrist with the highest accuracy for HR extraction, we collected a dataset from 10 healthy adults at three different wrist positions. The dataset comprises roughly 5 hours of HR data with an average of 80.6116.3 bpm. During recording, we synchronized the established electrocardiography (ECG) gold standard with our US-based method. The comparisons yield a Pearson correlation coefficient of r(92)=0.99, p<0.001 and a mean error of 0.6811.88 bpm in the lateral wrist position near the radial artery. Moreover, we tested our method while walking and running to assess its robustness to motion artifacts, achieving a heart rate extraction accuracy of 1.9912.80 bpm. The collected dataset and code used in this work have been open-sourced and are available at https://github.com/mgiordy/Ultrasound-Heart-Rate.

Visual Inertial Odometry (VIO) is a widely used computer vision method that determines an agent’s movement through a camera and an IMU sensor. This paper presents an efficient and accurate VIO pipeline optimized for applications on micro- and nano-UAVs. The proposed design incorporates state-of-the-art feature detection and tracking methods (SuperPoint, PX4FLOW, ORB), all optimized and quantized for emerging RISC-V-based ultra-low-power parallel systems on chips (SoCs). Furthermore, by employing a rigid body motion model, the pipeline reduces estimation errors and achieves improved accuracy in planar motion scenarios. The pipeline’s suitability for real-time VIO is assessed on an ultra-low-power SoC in terms of compute requirements and tracking accuracy after quantization. The pipeline, including the three feature tracking methods, was implemented on the SoC for real-world validation. This design bridges the gap between high-accuracy VIO pipelines that are traditionally run on computationally powerful systems and lightweight implementations suitable for microcontrollers. The optimized pipeline on the GAP9 low-power SoC demonstrates an average reduction in RMSE of up to a factor of 3.65x over the baseline pipeline when using the ORB feature tracker. The analysis of the computational complexity of the feature trackers further shows that PX4FLOW achieves on-par tracking accuracy with ORB at a lower runtime for movement speeds below 24 pixels/frame.

Low-latency sensing and decision-making processing are critical requirements for the highly dynamic control and perception applications often found in Unmanned Areal Systems (UASs). Novel sensors such as Dynamic Vision Sensors (DVSs) are enhancing the pure performance of the perception component with orders of magnitude lower latency. However, they are typically not optimally integrated with the computing hardware, which effectively reduces the potential in both latency and power consumption. In addition to the non-optimal integration, low latency processing of such high data rate generating sensors is often challenging on resource-constrained platforms. Here, Field Programmable Gate Arrays (FPGAs) platforms offer a promising set of features, including low-level access to hardware and memories, as well as high-speed interfaces. On the other hand, FPGA platforms are not as popular on UASs due to their complex programming and development environments, steep initial learning curve, and challenges in achieving optimal performance. To accelerate future development, this paper presents SwiftEagle, an open-source, cutting-edge 720g FPGA based UAS based on a custom hardware design including a dual camera interface RGB/DVSs on a multi-sensors subsystem and an initial software and firmware stack designed for high precision recording of machine learning datasets in-flight with sub-micro-second time resolution and on-FPGA rendering of DVS event frames. Utilizing this developed platform, as proof-of-concept the end-to-end latency of a novel, just released DVS is shown to be below 210µs as a worst-case scenario, enabling future cutting-edge autonomous algorithms.

The rapid growth of the Internet of Things paradigm is pushing the need to connect billions of battery-operated devices to the internet and among them. To address this need, the introduction of energy-efficient wireless mesh networks based on Bluetooth provides an effective solution. This paper proposes a testbed setup to accurately evaluate and compare the standard Bluetooth Mesh 5.0 and the emerging energy-efficient Wirepas protocol that promises better performance. The paper presents the evaluation in terms of power consumption, energy efficiency, and transmission latency which are the most crucial features, in a controlled and reproducible test setup consisting of 10 nodes. Experimental results demonstrated that Wirepas has a median latency of 2.83ms in Low-Latency mode respectively around 2s in the Low-Energy mode. The corresponding power consumption is 6.2mA in Low-Latency mode and 38.9µA in Low-Energy mode. For Bluetooth Mesh the median latency is 4.54ms with a power consumption of 6.2mA at 3.3V. Based on this comparison, conclusions about the advantages and disadvantages of both technologies can be drawn.

Digital ultrasound (US) probes integrate the analog-to-digital conversion directly on the probe and can be conveniently connected to commodity devices. Existing digital probes are however limited to a relatively small number of channels, do not guarantee access to the raw US data, or cannot operate at very high frame rates (e.g., due to exhaustion of computing and storage units on the receiving device). In this work, we present an open, compact, power-efficient, 192-channels digital US data acquisition system capable of streaming US data at transfer rates greater than 80 Gbps towards a host PC for ultra-high frame rate imaging (in the multi-kHz range). Our US probe is equipped with two power-efficient Field Programmable Gate Arrays (FPGAs) and is interfaced to the host PC with two optical-link 100G Ethernet connections. The high-speed performance is enabled by implementing a Remote Direct Memory Access (RDMA) communication protocol between the probe and the controlling PC, that utilizes a high-performance Non-Volatile Memory Express (NVMe) interface to store the streamed data. To the best of our knowledge, thanks to the achieved datarates, this is the first high-channel-count compact digital US platform capable of raw data streaming at frame rates of 20 kHz (for imaging at 3.5 cm depths), without the need for sparse sampling, consuming less than 40 W.

Stretchable magnetic resonance (MR) receive coils show shifts in their resonance frequency when stretched. An in-field receiver measures the frequency response of a stretchable coil. The receiver and coil are designed to operate at 128 MHz for a 3T MR scanner. Based on the measured frequency response, we are able to detect the changes of the resonance frequency of the coil. We show a proportional-integral-derivative controller that tracks the changes in resonance frequency and retunes the stretchable coil. The settling time of the control loop is less than 3.8ms. The retuning system reduces the loss in signal-to-noise ratio of phantom images from 1.6 dB to 0.3 dB, when the coil is stretched by 40% and the coil is retuned to 128 MHz.

Sensor technology that is unobtrusively integrated into the clothing and equipment of an athlete can support the training of sport activities and monitor the athlete's progress. In this paper, we propose two wearable systems that support ice hockey players in the training of skating and shooting. These assistants measure the motions of players and compare them with reference executions of the same activities by professional players. A third system that we introduce monitors the player;s activities during a hockey game and creates a match report for objective performance measurement. For each of the three proposed applications, we present a prototype setup that we evaluate with amateur and professional players. The main findings are i) that with a skate-worn motion sensor and user-dependent training, eight skating motions can be spotted with an accuracy above 90%, ii) that stick-integrated sensors enable the measurement of relevant shot features, which differentiate professional from amateur athletes, and iii) that it is possible to spot important ice hockey activities in the signals of body-worn motion sensors worn during a game.