Wearable Platform for Automatic Recognition of Parkinson Disease by Muscular Implication Monitoring

The need for diagnostic tools for the characterization of progressive movement disorders - as the Parkinson Disease (PD) - aiming to early detect and monitor the pathology is getting more and more impelling. The parallel request of wearable and wireless solutions, for the real-time monitoring in a non-controlled environment, has led to the implementation of a Quantitative Gait Analysis platform for the extraction of muscular implications features in ordinary motor action, such as gait. The here proposed platform is used for the quantification of PD symptoms. Addressing the wearable trend, the proposed architecture is able to define the real-time modulation of the muscular indexes by using 8 EMG wireless nodes positioned on lower limbs. The implemented system “translates” the acquisition in a 1-bit signal, exploiting a dynamic thresholding algorithm. The resulting 1-bit signals are used both to define muscular indexes both to drastically reduce the amount of data to be analyzed, preserving at the same time the muscular information. The overall architecture has been fully implemented on Altera Cyclone V FPGA. The system has been tested on 4 subjects: 2 affected by PD and 2 healthy subjects (control group). The experimental results highlight the validity of the proposed solution in Disease recognition and the outcomes match the clinical literature results

WSN-Based Near Real-Time Environmental Monitoring for Shelf Life Prediction Through Data Processing to Improve Food Safety and Certification

This position paper aims to support a control technique in the perishables goods supply-chain through a combination of near real-time wireless sensor network (WSN) for environmental monitoring and further data processing to predict the shelf life of the product. This approach returns a low cost, versatile and efficient tool that can significantly improve the safety and food certification through the organoleptic qualities control using three different sensors, i.e. temperature, light and humidity. In this article, therefore, the advantages of the proposed technique are explained and a case study is presented to support this approach, as well as an example of processing algorithm for shelf life evaluation

Gait analysis and quantitative drug effect evaluation in Parkinson disease by jointly EEG-EMG monitoring

This work addresses the rising need for a diagnostic tool for the evaluation of the effectiveness of a drug treatment in Parkinson disease, allowing the physician to monitor of the patient gait at home and to shape the treatment on the individual peculiarity. In aim, we present a cyber-physical system for real-time processing EEG and EMG signals. The wearable and wireless system extracts the following indexes: (i) typical activation and deactivation timing of single muscles and the duty cycle in a single step (ii) typical and maximum co-contractions, as well as number of co-contraction/s. The indexes are validated by using Movement Related Potentials (MRPs). The signal processing stage is implemented on Altera Cyclone V FPGA. In the paper, we show in vivo measurements by comparing responses before and after the drug (Levodopa) treatment. The system quantifies the effect of the Levodopa treatment detecting: (i) a 17% reduction in typical agonist-antagonist co-contractions time (ii) 23.6% decrease in the maximum co-contraction time (iii) 33% decrease in number of critical co-contraction. Brain implications shows a mean reduction of 5% on the evaluated potentials

Wireless Shelf Life Monitoring and Real Time Prediction in a Supply-Chain of Perishables Goods

This paper discusses the huge potential of a Wireless Sensor Network (WSN) as a tool for real-time monitoring in a perishable goods supply chain according to the pressing need of security and food certification. The combination of an appropriate monitoring system and further data processing create a tool that can provide the most useful information for each application. In this paper we propose a case study

Fall-Risk Assessment by Combined Movement Related Potentials and Co-Contraction Index Monitoring

In this paper we propose a novel approach for online fall-risk assessment based on concurrent EEG and EMG monitoring. The fall-risk evaluation is based on: i) clinical condition of the individual, ii) environment, iii) EMG agonist-antagonist co-contraction analysis and iv) Movement Related Potentials and event related desynchronizations occurrence/absence. The fall-risk assessment evaluation algorithm has been implemented on a FPGA (Altera Cyclone V SE 5CSEMA5F31C6N) in order to realize an autonomous and stand-alone fall prevention tool. The experimental results (based on a dataset of 10 individuals) are described and demonstrate the validity of the algorithm and its FPGA implementation, which responds in 41ms, well within the 300ms time limit according to a study on 45 fallers and 80 non-fallers (with 74 years average age)

FPGA Based Architecture for Fall-risk Assessment During Gait Monitoring by Synchronous EEG/EMG

One out of three subjects older than 65 years falls. Despite extensive research, existing assessment tools for fall risk have been insufficient for predicting falls since the phenomenology is complex and there is no equipment on the market that allows everyday life monitoring. In this paper we present a novel approach for fall-risk on-line assessment based on: i) clinical condition of the subject, ii) environmental conditions, iii) electromyographic (EMG) co-contraction analysis and iv) electroencephalographic (EEG) analysis based on Movement Related Potentials (MRPs) and μ-rhythm event related desynchronizations (μ-ERDs) occurrence. This fall-risk assessment approach is implemented by a complete cyber-physical system made up by EEG and EMG wearable recording systems interfaced to an FPGA on-line performing the needed real-time processing for indexes extraction. The results present a fall-risk assessment case study on healthy subjects walking showing detectable fall-risk increasing (+1.5%) when obstacles are overcome

An Embedded System Remotely Driving Mechanical Devices by P300 Brain Activity

In this paper we present a P300-hased Brain Computer Interface (BCI) for the remote control of a mechatronic actuator, such as wheelchair, or even a car, driven by EEG signals to be used hy tetraplegic and paralytic users or just for safe drive in case of car. The P300 signal, an Evoked Related Potential (ERP) devoted to the cognitive brain activity, is induced for purpose by visual stimulation. The EEG data are collected by 6 smart wireless electrodes from the parietal-cortex area and online classified by a linear threshold classifier, basing on a suitable stage of Machine Learning (ML). The ML is implemented on a μPC dedicated to the system and where the data acquisition and processing is performed. The main improvement in remote driving car by EEG, regards the approach used for the intentions recognition. In this work, the classification is based on the P300 and not just on the average of more not well identify potentials. This approach reduces the number of electrodes on the EEG helmet. The ML stage is based on a custom algorithm (t-RIDE) which tunes the following classification stage on the user's “cognitive chronometry”. The ML algorithm starts with a fast calibration phase (just ~190s for the first learning). Furthermore, the BCI presents a functional approach for time-domain features extraction, which reduces the amount of data to be analyzed, and then the system response times. In this paper, a proof of concept of the proposed BCI is shown using a prototype car, tested on 5 subjects (aged 26 ± 3). The experimental results show that the novel ML approach allows a complete P300 spatio-temporal characterization in 1.95s using 38 target brain visual stimuli (for each direction of the car path). In free-drive mode, the BCI classification reaches 80.5 ± 4.1% on single-trial detection accuracy while the worst-case computational time is 19.65ms ± 10.1. The BCI system here described can be also used on different mechatronic actuators, such as robots

Towards Mobile Health Care: Neurocognitive Impairment Monitoring by BCI-based Game

A mobile-health solution for neuro-cognitive impairment monitoring based on P300 spatio-temporal characterization achieved by tuned Residue Iteration Decomposition (t-RIDE) is here presented. It allows remote monitoring of neuro-cognitive impairment through a domestic game-test by physician which can interact with it. Data collection is allowed by cloud bridging. It has been validated on 10 subjects: P300 amplitude and latency ranges are 2.8pV-8pV and 300ms-410ms (on Pz, Fz, Cz, EEG electrodes) in total agreement with the medical references. The methodology shows fast diagnosis of cognitive deficit, including mild and heavy cognitive impairment: t-RIDE convergence is reached in 79 iteration (i.e. 1.95s) giving 80% accuracy in P300 amplitude evaluation with only 13 trials on a single EEG channel

The Ultimate IoT Application: a Cyber-Physical System for Ambient Assisted Living

We propose a novel approach that integrates wireless, non-invasive devices with fast, real-time algorithms for large data analysis and biofeedback reaction, to discern the voluntariness of human movement through direct sensing of brain potentials combined with muscular action signal monitoring. The system has been tested in real situations

Biodegradable pressure sensor for health-care

The subject of the present work is the design, the testing and the implementation of a biodegradable and biocompatible pressure sensor that can be swallowed or implanted in the human body. It has to be biodegradable, at least in part, biocompatible and small in the size. The biodegradable polymer used (Polycaprolactone, PCL) and the technique of printing gold (200-400 nm thick) on it have played a key role throughout the project. PCL was used both as substrate, on which all connections for discrete surface mount devices were printed, and for fabricating the pressure sensitive devices. A possible implementation for gastroenterology is presented. The final implementation fully satisfies the design specifications of biodegradability and biocompatibility, high operating frequency, high frequency sensitivity to changes in capacitance and size minimization