The rapid expansion of Wireless Body Sensor Networks (WBSNs) in healthcare, rehabilitation, and movement analysis demands precise time synchronization across sensor nodes to ensure reliable multi-modal data fusion. Existing synchronization solutions for Bluetooth Low Energy (BLE)-based WBSNs often rely on hardware-level timestamping or operate in advertising mode, limiting their scalability, interoperability, or usability in real-time interactive applications. In this paper, we present SharkTooth, a novel, scalable, and real-time synchronization algorithm that operates entirely at the application layer of BLE. Unlike prior approaches, SharkTooth does not require any firmware modifications or specialized hardware, and it is compatible with commercial off-the-shelf BLE devices. The algorithm employs an adaptive timestamp correction technique that mitigates both deterministic drift and unpredictable delays caused by packet retransmissions and protocol variability. Extensive experimental validation using up to 12 sensor nodes and 18 BLE network configurations demonstrates submillisecond synchronization accuracy, with a median absolute average synchronization error between 0.21 ms and 0.62 ms, even in congested network scenarios. Compared to state-ofthe-art solutions, SharkTooth is uniquely capable of delivering robust, long-term synchronization in high-throughput, multinode systems using only connection-based BLE communication. By prioritizing compatibility, reproducibility, and real-world performance, SharkTooth advances the design of scalable and interoperable WBSNs for next-generation biomedical and wearable IoT applications.

Over the years, several clock and data recovery architectures have been proposed for wireless Amplitude Shift Keying (ASK) transmitted signals. State-of-the-art architectures mainly rely on synchronous phase-locked loop circuits or self- sampling systems, both resulting in large area consumption. This work presents a novel CMOS architecture for Clock and Data Recovery (CDR) in miniaturised and wirelessly powered implants. The proposed CDR architecture works at 433.92 MHz and includes: an ASK-demodulator, an on-chip oscillator, a power-on-reset, a control and a recovering block operating in feedback-loop. The ASK-demodulator works for a data rate as high as 6 Mbps and a modulation index in the range of 9-30%. A novel communication protocol is presented for a separated clock and data transmission. The entire CDR architecture occupies$17 \times 89 \mu m^{2}$and consumes$15.01 \mu W$while operating with a clock rate of 6 Mbps.

Remote monitoring of vital body signals has drawn the attention of late, particularly with the burst of COVID-19. Wearable devices are started to be widely used for non-invasive health monitoring tasks. This work presents a wearable system for multiple body signal monitoring using only one biocompatible aluminum nitride piezoelectric sensor and a custom wireless electronic device for data acquisition and transmission. The proposed sensor has been customized for the suprasternal notch, where we can simultaneously extract heart rate, respiration rate, and deglutition events. These parameters are helpful for the remote diagnosis of multiple diseases like cardiac arrhythmia, asthma, and dysphagia. Moreover, heart sound components have been derived from the same signal, providing critical information about heart health and insight into possible heart diseases. The preliminary experimental results show that the proposed wearable system can be used for personalized healthcare applications and offers a promising solution for unobtrusive remote health monitoring.

This demonstration presents a smart wearable armband for hand gesture recognition interfaced with a 6-DOF robotic arm which actuates the user's movements. The armband is composed of seven modules which detect the muscular activity beneath them, fuse the data together, predict the performed gesture, and transmit the high-level information to an external computer via a Bluetooth Low Energy (BLE) communication. There, a software module transforms the sequence of gestures into consistent commands for the robotic arm. The observed responsiveness and accuracy make this armband suitable for the real-time control of robotic limbs in mixed reality scenarios.

In modern society, the ever-increasing demand for Earth Observation products in a large variety of sectors is exposing the limitations of traditional satellite data chain architectures. The European Union Horizon 2020 EO-ALERT project aims at overcoming the existing bottlenecks by leveraging the performance of state-of-the-art commercial off-the-shelf devices to move the critical elements of data processing on the flight segment without sacrificing processing performance. This paper introduces the architecture of the EO-ALERT CPU Scheduling, Compression, Encryption and Data Handling Subsystem, responsible for coordinating the onboard optical and Synthetic Aperture Radar data chains, as well as providing data compression, encryption, and storage services. The performance obtained by a reference implementation of the proposed architecture is also presented, showing an extremely low contribution to the overall system latency that allows real-time Earth Observation product delivery to the end user in less than 5 min.

Transmitting images from earth observation satellites to ground is a major challenge, and a compression/encryption stage is actually mandatory. Development of hardware accelerators is highly recommended, both to relieve the software from such demanding task, and to improve performance, aiming at quasi-real-time data processing. To this end, we discuss the design, development, deployment and test of a FPGA-based accelerator, featuring a lossless and lossy (near-lossless) compression, including the data encryption too. Its architecture is well suited for different image types, including single- and multi-band optical and SAR images and can be fully run-time configurable. Measured performance showed a throughput of 10 Msamples/s, in agreement with related state-of-the-art works, focused on lossless compression only.

World population growth and desertification are the most severe issue to agricultural food production. Smart agriculture is a promising solution to ensure food security. The use of sensors to monitor crop production can help farmers improve the yield and reduce water consumption. Here we propose a study where the electrical impedance of green plants' stem is analyzed in vivo, along with environmental conditions. In particular, the variations associated with the daily cycle are highlighted. These analyses lead to the possibility of understanding plant status directly from stem impedance.

With the growth of world population and food demand, it is crucial to optimize water consumption for agriculture cultivation. Here we propose a method to monitor plant status, relating the measured parameters to the watering or drying situation of a single plant. Plant trunk electrical impedance measurements and environmental parameters were analyzed with a statistical approach. Correlation and causality among the data are showed and analyzed. In this way, it was possible to easily obtain the needed information about plant status.

This demonstration presents the first implementation of “event-driven” serial asynchronous communication on optical fiber. “event-driven” communication is used by neuromorphic sensors, that sample the sensory signal when the signal itself changes of a given amount. This type of sensing adapts to the dynamics of the input itself, achieving at the same time extremely high temporal resolution (when needed), low latency and signal compression. To apply this technology in robotics, optical communication will greatly improve the resilience to electric disturbances.

This paper reports the achievements reached in the setup of a simple electrical communication inside a plant body. The approach comprises an electrical signal coupled into the plant body through a low-frequency communication signal. Different setups with the stem of a Prunus Bianco have been evaluated. From analog measurements, the optimum transmission frequency range has deemed to be 60-140 kHz. A setup for the digital communication, with an impulse-based approach (Synchronized On-Off Keying, S-OOK, modulation) and a bit rate of 50kb/s, has been tested; bit error rate has been estimated to be negligible.

The demonstration presents a robotic event-based tactile infrastructure for a humanoid robot. It leverages on currently deployed sample-based capacitive sensors to generate tactile events, enabling the investigation and development of event-driven tactile applications, and minimizing communication bandwidth and latency. The modular FPGA-based system samples data from tactile sensors and generates address-events, transmitted through an asynchronous serial address-event representation protocol. To enable performance comparisons of the event-driven approach with respect to standard sample-based solutions, the acquisition modules can directly forward the input samples through the same event-based communication channel. We will show in real time a comparison between the tactile events and the original sampled data generated when the skin patch is touched.

This study presents preliminary results of a broader research on Home Robot monitoring for elder people. The final goal of the project is the development of a robot that works in synergy with an automatic fall detection device, reaching the patient and checking his condition in case of triggered alarm. This paper covers the initial steps necessary for the design of the tracking network which provides the machine with the subject's position, in particular the single node performance. The network is based on Ultra-Wide Band (UWB) wireless transceivers that in this study are the Decawave EVB1000 evaluation boards. Two types of analysis have been performed on the anchor: a Line of Sight (LOS) baseline accuracy and interference robustness. The results demonstrate that, for LOS distance estimation, to achieve a margin of error below 15 cm, the node has to be closer than 12 m to the target. If we remove the line of sight condition, introducing a subject walking straight between the two anchors, the error is spread in the order of 10 cm from the original baseline for a 10 m nodes distance recording. If the path is obstructed instead by a subject walking perpendicularly to the nodes instead leads to a different types of perturbations, with an absolute error below 13 cm.

The demonstration presents a surface ElectroMyoGraphy (sEMG) low power wireless system used to control Functional Electrical Stimulation for rehabilitative neuro-muscular applications. An event-driven technique is applied to the bio-signal, minimising power consumption, complexity, and transmitted data. The acquisition board transmits four channels sEMG event data, through a Bluetooth Low Energy wireless module, to a workstation where they are processed in order to control the stimulation pattern of a commercially available functional stimulator. We will show system functionality and efficiency during the execution of some basic functional movements.

This paper presents a minimum complexity hand movement recognition algorithm based on Average Threshold Crossing (ATC) technique. It exploits the number of threshold-crossing events, generated by a full-custom acquisition board, from the surface ElectroMyoGraphic (sEMG) signals of three forearm muscles to detect four different movements of the wrist: flexion, extension, abduction and grasp. A Support Vector Machine (SVM) model has been trained with the signals acquired from ten subjects, who repeated ten times each gesture. To avoid correlation between training and testing dataset, the Leave One Subject Out (LOSO) cross-validation technique has been chosen. The average ATC classifier's accuracy is 92.87 %, only 5.34 % below the results obtained feeding the same model with the sEMG features extracted from the raw sampled signals. The total latency of the algorithm, from the acquisition to the prediction, is 160 ms. Power consumption was considered too: with less than the power budget for one sampled sEMG channel, it is possible to acquire and transmit (through a Bluetooth low energy module) the event-driven data of four sEMG channels, with an effective data rate of only 28B/s. Obtained performance makes this technique suited for wearable systems or Internet-of-Things (IoT) applications.

We propose a method to compress the enormous amount of data originating from tactile sensors is presented that explicitly exploits the inherent sparseness over space and time, sending tactile “events” only when a contact is detected. The resulting modular architecture is based on FPGA modules that acquire data samples from off-the-shelf tactile sensors based on capacitive transducers and generate and transmit an event-driven readout. This architecture has been specifically implemented for integration on robots with a large number of tactile sensors, to reduce communication bandwidth, power and processing requirements. An asynchronous serial address-event representation protocol further optimises effective data transmission rate (efficiency of 94.1%) and latency (340 ns) with respect to more common transmission protocols (e.g., Ethernet, CAN). We propose two complementary algorithms for the translation of raw-data into events, optimising data rate and bandwidth, or exploiting the asynchronous nature of the event-driven encoding and the temporal information within the sensory signal. Data reduction capability can reach up to 20 % of the correspondent clock-based encoding, with limited information loss due to the compression.

This paper presents a sensorized belt with four fully integrated pressure-touch sensors. We propose a very-low complexity sensor able to measure a pressure variation (up to 4 MPa) and to identify with accuracy a contact event at around 10 kPa. The overall pressure/touch sensor integrates a transducer, based on piezo capacitive material, coupled with a read-out circuit designed around a ring-oscillator. This converts the capacitance variation of the transducer into a quasi-digital signal characterized by a frequency range of 36.3–270 kHz with a very low standard deviation (2.3 kHz) and a sensitivity of 2.2 Hz/Pa. The tight integration of the electronics with the transducer results in a very compact all-in-one sensor system (overall size is 20 mm × 20 mm × 10 mm). Further, a major benefit of a low complexity design is the low power consumption, measured to be ∼370 μW. Based on a quasi-digital approach (event-driven), the system is well suited for impulse-based wireless communication.

This paper presents an 130 nm event-driven, all-digital, modular and scalable read-out circuit for capacitive sensors, whose operation is not influenced by either variation of the supply voltage or temperature. The sensing element of the ReadOut Circuit (ROC) is a voltage-controlled ring oscillator, designed to make the system robust to voltage and temperature variations with a dedicated calibration implemented to eliminate offset when no pressure is applied. The ring oscillator is used at the same time as a sensor and a clock signal for the entire event-driven unit. This, in turn, enables to further reduce the thermal drift of the measured capacitance. The sensitivity is 10 fF per LSB, considering a nominal sensing capacitance of 1 pF. The 8 bit output is asynchronously made available with a Parallel-In-Serial-Out register (PISO) after the ROC completes a measurement. The simulated average power consumption is 5.94 μ Wat 1.2V VDD on 1ms operation. The small active area (221×79 μm2) and power consumption make the circuit ideal to be replicated in an array in a cyber physical system, as capacitive pressure sensor in Humanoid Robots.

We present a fully asynchronous threshold-based IR-UWB receiver which enables a high sensitive distance estimation. It includes an ultra-low power baseband unit which achieves 533 fJ/pulse with an asynchronous, multipath robust and time expiring energy detector, which embeds signal strength in the baseband processing latency to increase sensitivity to TX-RX separation. Asynchronous line-of-sight over-the-air measurements obtained with an integrated all-digital transmitter show a maximum sensitivity of 1mm TX-RX separation per nanosecond system latency. The RX also permits data communication based on the use of self-synchronized modulations.

This paper presents abstraction techniques and modeling approaches to include physical-level antenna and receiver performance effects in a system-level network simulation for Wireless Body Area Networks (WBAN). The simulation platform is based on SystemC which can be used to model digital HW and SW aspects of an embedded application. By using the SystemC Network Simulation Library also a distributed network scenario can be simulated. Here, this platform has been extended to take into account the bit error rate and the path loss associated with antenna positioning in close proximity to the human body and with the design parameters of the wireless receiver. Antenna effects are modeled through a database of performance values based on physical measurements on a human phantom. Path loss information is fed in the SystemC simulator to model the received signal strength as a function of the position of nodes. The same information is used in the physical-level simulation of the receiver to extract bit error rate curves to be used by the SystemC simulator to build a statistical model of packet corruption. Instead of physical-level details, their effects are modeled in a parametric way into the system-level simulation thus combining speed and fidelity and allowing cross-domain design space exploration.

The design of a wireless data-glove is introduced in this paper, the aim is to provide an effective and complete solution wherever a tactile sensing system has to be integrated to correctly interact with the surrounding environment (e.g., astronaut's extravehicular activity glove). The focus is on the design of the whole system, starting with the readout circuit, the digital signal encoding and ending with the data transmission. The system is made of eight quasi-digital readout circuits to convert the analog information into a pulse-density modulation. The pulse streams are then temporally ordered to form a single streams of events. By integrating a Impulse Radio Ultra-Wide Band (IR-UWB) transmitter and choosing the proper protocol and modulation, we can aim to minimize the power consumption and provide error detection, the design has also taken into account the minimization of the complexity of the receiver. The whole system, fully asynchronous, has been designed as a single full-custom chip, besides having multiple independent inputs, it can be configured both to deploy a multi-chip system (with a single receiver) and to optimize wireless transmission parameters.

We present a modified type-I level-up shifter with improved Process-Voltage-Temperature (PVT) robustness, propagation delay and energy consumption. Compared to a standard cross-coupled level-shifter, the circuit comprises a couple of long channel parallel P and N transistors to implement larger PMOS on-resistance maintaining unvaried upstream logic fan-out. Simulation results show significant robustness increase with respect to a standard topology maintaining low NMOS-to-PMOS sizing. Switching energy consumption is reduced from ~ 10pJ to 200fJ and propagation delay from ~ 240ns to 1ns. With Monte Carlo process variation simulations we have verified a reduction in output delay sensitivity from 209ns to 333ps while with transient noise simulation jitter is reduced from 3.5ns to 36ps. Operating ranges are wider in the proposed circuit, while sensitivity to temperature is comparable for high values. A prototype of this drain-degenerated logic-translator has been fabricated in a 130nm CMOS technology and evaluated with measurements.

The aim of our project is to develop a mobile real-time reader device for blind people. It uses Artificial Neural Networks (ANNs) for the core character recognition engine. The hardware constraints led us to develop a cross-platform framework to design and evaluate such subsystem in order to find a good trade off between run-time performances and accuracy of results.

STIPER is an Italian national project whose aim is the study of devices to help blind people in daily activities. The core of the portable reader device is based on Artificial Neural Networks, used to recognize characters in real time beyond the fingers of blind people flowing on labels, restaurant menus and other printed objects. Neural Nets outputs are used to drive a Braille matrix, stimulating the fingertips of a blind person, and to speak by means of a common PDA device.