Design of Wireless Power Smart Personal Protective Equipment for Industrial Internet of Things

Personal Protective Equipment (PPE) is crucial in safeguarding against workplace hazards. However, incidents still occur due to improper PPE usage. With the rise of Industry 4.0 and increasing industrial automation, efforts aim to develop systems for real-time monitoring of PPE. We have developed a proof-of-concept of wireless-powered smart PPE that integrates commercial off-the-shelf electronics with PPE. The smart PPE consists of a power harvesting system, an Ultra-High Frequency Radio-Frequency Identification (UHF RFID) tag for tracking and data communication, and a microcontroller directly supporting capacitive sensing used to recognize the correct wearing of the PPE. A common UHF RFID reader interrogates and powers the smart PPE at the same time using the EPCglobal Class-1 Generation-2 communication protocol. The power harvester is more than 30 % efficient at -10 dBm, and the capacitive measurement shows a peak consumption of less than 100 μA at 1.8 V. Finally, the smart PPE was tested in a realistic scenario. The test was conducted by distancing the smart PPE from the reader from 1 m to 4 m in 1 m steps. The results showed that the wireless power supply and the communication data are feasible up to 4 m. The proposed smart PPE is an ultra-low-power wearable solution that easily integrates into industrial infrastructure and is easily miniaturized, ensuring a significant improvement in workplace safety by enabling real-time monitoring of correct PPE usage

A Low-Power Low-Complexity Circuit for Event-Based Feature Extraction from sEMG

This paper presents an analog circuit for calibration-free event-driven myoelectric control of sEMG-based applications. The proposed solution is to be installed downstream of the conditioning chain of an sEMG sensor and consists of a Sallen-Key filter, acting as a differentiator in the main sEMG frequency band, and a voltage comparator. The output of the circuit is a quasi-digital signal, in which the muscle activity is mapped onto the time distribution of digital events. The design phase focused on noise robustness, and a prototype was tested during in-vivo experiments on both upper and lower limbs. Among the obtained results, besides a current consumption of only 12.92 μA, a median increase in the number of events of more than 25% was achieved by varying the exerted muscle force in steps of 20% MVC

A Fast and Low-Impact Embedded Orientation Correction Algorithm for Hand Gesture Recognition Armbands

Hand gesture recognition is a prominent topic in the recent literature, with surface ElectroMyoGraphy (sEMG) recognized as a key method for wearable Human-Machine Interfaces (HMIs). However, sensor placement still significantly impacts systems performance. This study addresses sensor displacement by introducing a fast and low-impact orientation correction algorithm for sEMG-based HMI armbands. The algorithm includes a calibration phase to estimate armband orientation and real-time data correction, requiring only two distinct hand gestures in terms of sEMG activation. This ensures hardware and database independence and eliminates the need for model retraining, as data correction occurs prior to classification or prediction. The algorithm was implemented in a hand gesture HMI system featuring a custom seven-channel sEMG armband with an Artificial Neural Network (ANN) capable of recognizing nine gestures. Validation demonstrated its effectiveness, achieving 93.36 % average prediction accuracy with arbitrary armband wearing orientation. The algorithm also has minimal impact on power consumption and latency, requiring just an additional 500 μW and introducing a latency increase of 408 μs. These results highlight the algorithm’s efficacy, general applicability, and efficiency, presenting it as a promising solution to the electrode-shift issue in sEMG-based HMI applications

Raspberry Pi based Modular System for Multichannel Event-Driven Functional Electrical Stimulation Control

This paper describes the implementation and testing of a modular software for multichannel control of Functional Electrical Stimulation (FES). Moving towards an embedded scenario, the core of the system is a Raspberry Pi, whose different models (with different computing powers) best suit two different system use-cases: user-supervised and stand-alone. Given the need for real-time and reliable FES applications, software processing timings were analyzed for multiple configurations, along with hardware resources utilization. Among the results, the simultaneous use of eight channels has been functionally achieved (0% lost packets) while minimizing system timing failures (excessive processing latency). Further investigations included stressing the system using more constraining acquisition parameters, eventually limiting the usable channels (only for the stand-alone use-case)

Live Demonstration: A Wearable Armband for Real-Time Control of Multi-DOF Robotic Actuators

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

Formal Verification of Neural Networks: a Case Study about Adaptive Cruise Control

Formal verification of neural networks is a promising technique to improve their dependability for safety critical applications. Autonomous driving is one such application where the controllers supervising different functions in a car should undergo a rigorous certification process. In this pa- per we present an example about learning and verification of an adaptive cruise control function on an autonomous car. We detail the learning process as well as the attempts to ver- ify various safety properties using the tool NEVER2, a new framework that integrates learning and verification in a sin- gle easy-to-use package intended for practictioners rather than experts in formal methods and/or machine learning

Combining Action Observation Treatment with a Brain–Computer Interface System: Perspectives on Neurorehabilitation

Action observation treatment (AOT) exploits a neurophysiological mechanism, matching an observed action on the neural substrates where that action is motorically represented. This mechanism is also known as mirror mechanism. In a typical AOT session, one can distinguish an observation phase and an execution phase. During the observation phase, the patient observes a daily action and soon after, during the execution phase, he/she is asked to perform the observed action at the best of his/her ability. Indeed, the execution phase may sometimes be difficult for those patients where motor impairment is severe. Although, in the current practice, the physiotherapist does not intervene on the quality of the execution phase, here, we propose a stimulation system based on neurophysiological parameters. This perspective article focuses on the possibility to combine AOT with a brain–computer interface system (BCI) that stimulates upper limb muscles, thus facilitating the execution of actions during a rehabilitation session. Combining a rehabilitation tool that is well-grounded in neurophysiology with a stimulation system, such as the one proposed, may improve the efficacy of AOT in the treatment of severe neurological patients, including stroke patients, Parkinson’s disease patients, and children with cerebral palsy

A Biomimetic Multichannel Synergistic Calibration for Event-Driven Functional Electrical Stimulation

In this paper, we present the Profile Extraction (PE) algorithm, which allows the computation of a multi-channel profile highly correlated with voluntary muscle activity. This event-based profile can be used as biomimetic control during the calibration phase of a Functional Electrical Stimulation (FES) system. The adoption of the PE technique represents the preliminary step to extend the applicability of our event-driven paradigm to control the coordinated multi-joint movements. Through an experimental campaign, we tested the improvements made by the use of PE in the FES calibration, assessing the reproducibility between the voluntary and stimulated movements. Results show a 2 % increase of the median correlation value for a single-channel exercise and a 3.6 % increase for a dual-channel one. A statistical decrease of normalized Root Mean Square Error has been obtained for the dual-channel exercise (p < 0.05)

Live Demonstration: A Real-Time Bio-Mimetic System for Multichannel FES Control

This demonstration presents a bio-mimetic system for the real-time multichannel control of Functional Electrical Stimulation (FES). The intensities of the FES profiles are directly mapped by processing surface ElectroMyoGraphic (sEMG) signals detected from synergistic muscles, thus achieving a user-comfortable stimulation that follows the monitored physiological patterns. Furthermore, a user-dedicated calibration routine and multiple versatile operating configurations allow the system to be integrated into standard rehabilitation protocols to enhance the restoration of motor functionalities