arXiv subjects

Lorenzo Mucchi

Publications and source records attributed to Lorenzo Mucchi.

6 recordsLinked to original sources

arXiv2026

RF Intelligence for Health: Classification of SmartBAN Signals in overcrowded ISM band

Accurate classification of Radio-Frequency (RF) signals is essential for reliable wearable health-monitoring systems, providing awareness of the interference conditions in which medical protocols operate. In the overcrowded 2.4 GHz ISM band, however, identifying low-power transmissions from medical sensors is challenging due to strong co-channel interference and substantial power asymmetry with coexisting technologies. This work introduces the first open source framework for automatic recognition of SmartBAN signals in Body Area Networks (BANs). The framework combines a synthetic dataset of simulated signals with real RF acquisitions obtained through Software-Defined Radios (SDRs), enabling both controlled and realistic evaluation. Deep convolutional neural networks based on ResNet encoders and U-Net decoders with attention mechanisms are trained and assessed across diverse propagation conditions. The proposed approach achieves over 90% accuracy on synthetic datasets and demonstrates consistent performance on real over-the-air spectrograms. By enabling reliable SmartBAN signal recognition in dense spectral environments, this framework supports interferenceaware coexistence strategies and improves the dependability of wearable healthcare systems.

cs.NI↗

arXiv2024

Deep-Learning-Based Markerless Pose Estimation Systems in Gait Analysis: DeepLabCut Custom Training and the Refinement Function

The current gold standard for the study of human movement is the marker-based motion capture system that offers high precision but constrained by costs and controlled environments. Markerless pose estimation systems emerge as ecological alternatives, allowing unobtrusive data acquisition in natural settings. This study compares the performance of two popular markerless systems, OpenPose (OP) and DeepLabCut (DLC), in assessing locomotion. Forty healthy subjects walked along a 5 meters walkway equipped with four force platforms and a camera. Gait parameters were obtained using OP BODY 25 Pre-Trained model (OPPT), DLC Model Zoo full human Pre-Trained model (DLCPT) and DLC Custom-Trained model (DLCCT), then compared with those acquired from the force platforms as reference system. Our results demonstrated that DLCCT outperformed DLCPT and OPPT, highlighting the importance of leveraging DeepLabCut transfer learning to enhance the pose estimation performance with a custom-trained neural networks. Moreover, DLCCT, with the implementation of the DLC refinement function, offers the most promising markerless pose estimation solution for evaluating locomotion. Therefore, our data provide insights into the DLC training and refinement processes required to achieve optimal performance. This study offers perspectives for clinicians and practitioners seeking accurate low-cost methods for movement assessment beyond laboratory settings.

cs.CV↗

arXiv2024

Climbing Routes Clustering Using Energy-Efficient Accelerometers Attached to the Quickdraws

One of the challenges for climbing gyms is to find out popular routes for the climbers to improve their services and optimally use their infrastructure. This problem must be addressed preserving both the privacy and convenience of the climbers and the costs of the gyms. To this aim, a hardware prototype is developed to collect data using accelerometer sensors attached to a piece of climbing equipment mounted on the wall, called quickdraw, that connects the climbing rope to the bolt anchors. The corresponding sensors are configured to be energy-efficient, hence becoming practical in terms of expenses and time consumption for replacement when used in large quantities in a climbing gym. This paper describes hardware specifications, studies data measured by the sensors in ultra-low power mode, detect patterns in data during climbing different routes, and develops an unsupervised approach for route clustering.

eess.SP↗

arXiv2023

IEEE 802.15.7-Compliant Ultra-low Latency Relaying VLC System for Safety-Critical ITS

The integration of Visible-Light Communications technology (VLC) in Intelligent Transportation Systems (ITS) is a very promising platform for a cost-effective implementation of revolutionary ITS and cooperative ITS protocols. In this paper, we propose an infrastructure-to-vehicle-to-vehicle (I2V2V) VLC system for ITS, implementing it through a regular LED traffic light serving as a transmitter and a digital Active Decode-and-Relay (ADR) stage for decoding and relaying the received information towards further incoming units. The proposed VLC system targets the challenging and important case of ultra-low latency ADR transmission of short packets, as this is needed for emerging applications of automatic braking, car platooning and other critical automatic and/or assisted driving applications. The experimental validation of the ADR VLC chain, as well as a thorough statistical analysis of errors distribution in the transmission, has been performed for short to medium distances, up to 50 meters. The performances of the designed system are evaluated by measuring the packet error rate (PER) and latency in the whole ADR transmission chain. Our analysis shows that our system attains ultra-low, sub-ms latencies at 99.9\% confidence level for PER as high as $5\times10^{-3}$, yet granting a latency below 10 ms even for distances of 50 m. The demonstrated system prototype is compatible with IEEE 802.15.7 standard.

eess.SP↗

arXiv2020

Experimental measurements of a joint 5G-VLC communication for future vehicular networks

One of the main revolutionary features of 5G networks is the ultra-low latency that will enable new services such as those for the future smart vehicles. The 5G technology will be able to support extreme-low latency. Thanks to new technologies and the wide flexible architecture that integrates new spectra and access technologies. In particular, Visible Light Communication (VLC) is envisaged as a very promising technology for vehicular communications, since the information can flow by using the lights (as traffic-lights and car lights). This paper describes one of the first experiments on the joint use of 5G and VLC networks to provide real-time information to cars. The applications span from road safety to emergency alarm.

eess.SP↗

arXiv2020

Measurement-based VLC channel characterization for I2V communications in a real urban scenario

Visible light communication (VLC) is nowadays envisaged as a promising technology to enable new classes of services in intelligent transportation systems ranging, e.g., from assisted driving to autonomous vehicles. The assessment of the performance of VLC for automotive applications requires as a basic step a model of the transmission pattern and propagation of the VLC signal when real traffic-lights and road scenarios are involved. In this paper an experimental measurement campaign has been carried out by using a regular traffic-light as source (red light) and a photoreceiver positioned, statically, at different distances and heights along the road. A linear regression technique is used to come up with different propagation models. The proposed models have been compared, in terms of accuracy and complexity, to the conventional Lambertian model to describe the VLC channel in a real urban scenario. The proposed models provides a significant higher accuracy with comparable complexity.

eess.SP↗