Steven Marty

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Marty

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Steven

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0009-0004-8461-5041

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Publications 1 - 7 of 7

Frequency Matters: Comparative Analysis of Low-Power FMCW Radars for Vital Sign Monitoring
Marty, Steven; Ronco, Andrea; Pantanella, Federico; et al. (2024)
IEEE Transactions on Instrumentation and Measurement
Vital sign monitoring is a critical step in health assessment in clinical settings. A rising interest in contactless options is pushing for innovative solutions for heart rate (HR) and respiration rate (RR) estimation. Low-power millimeter-wave radars are emerging as a solution because of their invariance to lightning conditions, subject phenotype, and privacy guarantees. On the side, the robustness of radar-based systems is still a concern due to incomplete characterization of the systems, evaluation of different frequencies, and limited evaluation on human subjects. Moreover, low-power options have not been rigorously explored, despite their potential in ubiquitous deployment. This article evaluates three low-power frequency-modulated continuous wave (FMCW) radars with the frequencies of 24, 60, and 120 GHz, investigating their performance and the influence of the carrier frequency in vital sign estimation. An initial characterization of the displacement noise is conducted using a phantom device. Various techniques to enhance the signal to noise ratio (SNR) of the radar signal and the extraction of the chest displacement signal are combined. Finally, a lightweight and accurate algorithm based on the second-order derivative of the displacement is proposed, developed, and evaluated to assess the HR and RR. The evaluation is conducted for all three radar systems on a dataset with 24 subjects. We demonstrate with the experimental evaluation that the three systems accurately estimate the RR with a mean absolute error (MAE) of less than 2 brpm (±3.05). The 60- and 120-GHz system estimates the HR accurately with an MAE of 1.8 ± 3.1 bpm and 3.2 ± 5.3 bpm, respectively, while the 24-GHz system is less effective with an MAE of 9.0 bpm, mainly due to its high noise profile. This evaluation demonstrates the feasibility of HR and RR with low-power FMCW radars, underlying the importance of the operating frequency, and the necessity of an appropriate characterization when designing the algorithms.
TinyBird-ML: An ultra-low Power Smart Sensor Node for Bird Vocalization Analysis and Syllable Classification
Schulthess, Lukas; Marty, Steven; Dirodi, Matilde; et al. (2023)
IEEE ISCAS 2023 Symposium Proceedings
Animal vocalisations serve a wide range of vital functions. Although it is possible to record animal vocalisations with external microphones, more insights are gained from miniature sensors mounted directly on animals' backs. We present TinyBird-ML; a wearable sensor node weighing only 1.4 g for acquiring, processing, and wirelessly transmitting acoustic signals to a host system using Bluetooth Low Energy. TinyBird-ML embeds low-latency tiny machine learning algorithms for song syllable classification. To optimize battery lifetime of TinyBird-ML during fault-tolerant continuous recordings, we present an efficient firmware and hardware design. We make use of standard lossy compression schemes to reduce the amount of data sent over the Bluetooth antenna, which increases battery lifetime by 70% without negative impact on offline sound analysis. Furthermore, by not transmitting signals during silent periods, we further increase battery lifetime. One advantage of our sensor is that it allows for closed-loop experiments in the microsecond range by processing sounds directly on the device instead of streaming them to a computer. We demonstrate this capability by detecting and classifying song syllables with minimal latency and a syllable error rate of 7%, using a light-weight neural network that runs directly on the sensor node itself. Thanks to our power-saving hardware and software design, during continuous operation at a sampling rate of 16 kHz, the sensor node achieves a lifetime of 25 hours on a single size 13 zinc-air battery.
Noise Analysis and Modeling of the PMD Flexx2 Depth Camera for Robotic Applications
Cai, Yuke; Plozza, Davide; Marty, Steven; et al. (2024)
2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS)
Time of Flight (ToF) cameras, renowned for their ability to capture real-time 3D information, have become indispensable for agile mobile robotics. These cameras utilize light signals to accurately measure distances, enabling robots to navigate complex environments with precision. Innovative depth cameras, characterized by their compact size and lightweight design, such as the recently released PMD Flexx2, are particularly suited for mobile robots. Capable of achieving high frame rates while capturing depth information, this innovative sensor is suitable for tasks such as robot navigation and terrain mapping. Operating on the ToF measurement principle, the sensor offers multiple benefits over classic stereo-based depth cameras. However, the depth images produced by the camera are subject to noise from multiple sources, complicating their simulation. This paper proposes an accurate quantification and modeling of the non-systematic noise of the PMD Flexx2. We propose models for both axial and lateral noise across various camera modes, assuming Gaussian distributions. Axial noise, modeled as a function of distance and incidence angle, demonstrated a low average Kullback-Leibler (KL) divergence of 0.015 nats, reflecting precise noise characterization. Lateral noise, deviating from a Gaussian distribution, was modeled conservatively, yielding a satisfactory KL divergence of 0.868 nats. These results validate our noise models, crucial for accurately simulating sensor behavior in virtual environments and reducing the sim-to-real gap in learning-based control approaches.
Embedded 2D LiDAR-Based Person Tracking for Safe Navigation in Assistive Autonomous Robots
Plozza, Davide; Marty, Steven; Scherrer, Cyril; et al. (2025)
2025 10th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI)
This paper presents a fully embedded real-time person tracking pipeline for assistive quadrupedal robots supporting safe navigation for visually impaired users. Our approach combines a deep learning-based 2D LiDAR person detector with a lightweight multi-object tracker and integrates it into a Guide Dog Robot (GDR) navigation framework. A novel detection post-processing scheme is proposed, reducing detector latency by 52.38% compared to state-of-the-art voting-based methods while preserving accuracy. The improved latency enables the entire pipeline to operate reliably at 20 Hz on a resource-constrained mobile robotic embedded platform based on the NVIDIA Jetson Xavier NX. The experimental setup shows that our system tracks dynamic obstacles and continuously localizes the user holding the robot’s handle, enabling a dynamic safety footprint for proactive collision avoidance. Under the tested setup, the optimal configuration achieves a MOTA of 83.27% and a user tracking RMSE below 0.2 m on two custom datasets recorded with motion-capture ground truth. Real-world navigation experiments in indoor environments demonstrate effective collision prevention and smooth corrective maneuvers when the user drifts from the default following position. The modular design of the detection, tracking, and planning components ensures flexibility and ease of integration into other robotic platforms. This work contributes a scalable and efficient tracking and navigation solution for human-aware mobile robots operating in dynamic environments, supporting safer human-robot interaction in assistive contexts.
DTR: Delaunay Triangulation-based Racing for Scaled Autonomous Racing
Tognoni, Luca; Reichlin, Neil; Ghignone, Edoardo; et al. (2025)
2025 10th International Workshop on Advances in Sensors and Interfaces (IWASI)
Reactive controllers for autonomous racing avoid the computational overhead of full See-Think-Act autonomy stacks by directly mapping sensor input to control actions, eliminating the need for localization and planning. A widely used reactive strategy is Follow-The-Gap (FTG), which identifies gaps in Light Detection and Ranging (LiDAR) range measurements and steers toward a chosen one. While effective on fully bounded circuits, FTG fails in scenarios with incomplete boundaries and is prone to driving into dead-ends, known as FTG-traps. This work presents Delaunay Triangulation-based Racing (DTR), a reactive controller that combines Delaunay triangulation, from raw LiDAR readings, with track boundary segmentation to extract a centerline while systematically avoiding FTG-traps. Compared to FTG, the proposed method achieves lap times that are 70% faster and approaches the performance of map-dependent methods. With a latency of 8.95 ms and Central Processing Unit (CPU) usage of only 38.85% on the robot’s OnBoard Computer (OBC), DTR is real-time capable and has been successfully deployed and evaluated in field experiments.
Autonomous Navigation in Dynamic Human Environments with an Embedded 2D LiDAR-based Person Tracker
Plozza, Davide; Marty, Steven; Scherrer, Cyril; et al. (2024)
2024 IEEE Sensors Applications Symposium (SAS)
In the rapidly evolving landscape of autonomous mobile robots, the emphasis on seamless human-robot interactions has shifted towards autonomous decision-making. This paper delves into the intricate challenges associated with robotic autonomy, focusing on navigation in dynamic environments shared with humans. It introduces an embedded real-time tracking pipeline, integrated into a navigation planning framework for effective person tracking and avoidance, adapting a state-of-the-art 2D LiDAR-based human detection network and an efficient multi-object tracker. By addressing the key components of detection, tracking, and planning separately, the proposed approach highlights the modularity and transferability of each component to other applications. Our tracking approach is validated on a quadruped robot equipped with 270 degrees 2D-LiDAR against motion capture system data, with the preferred configuration achieving an average MOTA of 85.45% in three newly recorded datasets, while reliably running in real-time at 20 Hz on the NVIDIA Jetson Xavier NX embedded GPU-accelerated platform. Furthermore, the integrated tracking and avoidance system is evaluated in real-world navigation experiments, demonstrating how accurate person tracking benefits the planner in optimizing the generated trajectories, enhancing its collision avoidance capabilities. This paper contributes to safer human-robot cohabitation, blending recent advances in human detection with responsive planning to navigate shared spaces effectively and securely.
Investigation of mmWave Radar Technology For Non-contact Vital Sign Monitoring
Marty, Steven; Pantanella, Federico; Ronco, Andrea; et al. (2023)
2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings
Non-contact vital sign monitoring has many advan tages over conventional methods in being comfortable, unobtru sive and without any risk of spreading infection. The use of millimeter-wave (mmWave) radars is one of the most promising approaches that enable contact-less monitoring of vital signs. Novel low-power implementations of this technology promise to enable vital sign sensing in embedded, battery-operated devices. The nature of these new low-power sensors exacerbates the challenges of accurate and robust vital sign monitoring and especially the problem of heart-rate tracking. This work focuses on the investigation and characterization of three Frequency Modulated Continuous Wave (FMCW) low-power radars with different carrier frequencies of 24 GHz, 60 GHz and 120 GHz. The evaluation platforms were first tested on phantom models that emulated human bodies to accurately evaluate the baseline noise, error in range estimation, and error in displacement estimation. Additionally, the systems were also used to collect data from three human subjects to gauge the feasibility of identifying heartbeat peaks and breathing peaks with simple and lightweight algorithms that could potentially run in low-power embedded processors. The investigation revealed that the 24 GHz radar has the highest baseline noise level, 0.04 mm at 0° angle of incidence, and an error in range estimation of 3.45 ± 1.88 cm at a distance of 60 cm. At the same distance, the 60 GHz and the 120 GHz radar system shows the least noise level, 0.01 mm at 0° angle of incidence, and error in range estimation 0.64 ± 0.01 cm and 0.04 ± 0.0 cm respectively. Additionally, tests on humans showed that all three radar systems were able to identify heart and breathing activity but the 120 GHz radar system outperformed the other two.

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