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

An embedded system for evoked biopotential acquisition and processing

This work presents an autonomous embedded system for evoked biopotential acquisition and processing. The system is versatile and can be used on different evoked potential scenarios like medical equipments or brain computer interfaces, fulfilling the strict real-time constraints that they impose. The embedded system is based on an ARM9 processor with capabilities to port a real-time operating system. Initially, a benchmark of the Windows CE operative system running on the embedded system is presented in order to find out its real-time capability as a set. Finally, a brain computer interface based on visual evoked potentials is implemented. Results of this application recovering visual evoked potential using two techniques: the fast Fourier transform and stimulus locked inter trace correlation, are also presented.Fil: Garcia, Pablo Andres. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Spinelli, Enrique Mario. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Toccaceli, Graciela Mabel. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a System-on-Chip based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with secured remote recognition in 5.74pJ/op; and seizure detection with encrypted data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE Transactions on Circuits and Systems - I: Regular Paper

Mobihealth: mobile health services based on body area networks

In this chapter we describe the concept of MobiHealth and the approach developed during the MobiHealth project (MobiHealth, 2002). The concept was to bring together the technologies of Body Area Networks (BANs), wireless broadband communications and wearable medical devices to provide mobile healthcare services for patients and health professionals. These technologies enable remote patient care services such as management of chronic conditions and detection of health emergencies. Because the patient is free to move anywhere whilst wearing the MobiHealth BAN, patient mobility is maximised. The vision is that patients can enjoy enhanced freedom and quality of life through avoidance or reduction of hospital stays. For the health services it means that pressure on overstretched hospital services can be alleviated

Using Low-Power, Low-Cost IoT Processors in Clinical Biosignal Research: An In-depth Feasibility Check

Research on biosignal (ExG) analysis is usually performed with expensive systems requiring connection with external computers for data processing. Consumer-grade low-cost wearable systems for bio-potential monitoring and embedded processing have been presented recently, but are not considered suitable for medical-grade analyses. This work presents a detailed quantitative comparative analysis of a recently presented fully-wearable low-power and low-cost platform (BioWolf) for ExG acquisition and embedded processing with two researchgrade acquisition systems, namely, ANTNeuro (EEG) and the Noraxon DTS (EMG). Our preliminary results demonstrate that BioWolf offers competitive performance in terms of electrical properties and classification accuracy. This paper also highlights distinctive features of BioWolf, such as real-time embedded processing, improved wearability, and energy-efficiency, which allows devising new types of experiments and usage scenarios for medical-grade biosignal processing in research and future clinical studies

Low-Cost, Wireless Bioelectric Signal Acquisition and Classification Platform

Bioelectric signal classification is a flourishing area of biomedical research, however conducting this research in a clinical setting can be difficult to achieve. The lack of inexpensive acquisition hardware can limit researchers from collecting and working with real-time data. Furthermore, hardware requiring direct connection to a computer can impose restrictions on typically mobile clinical settings for data collection. Here, we present an open-source ADS1299-based bioelectric signal acquisition system with wireless capability suitable for mobile data collection in clinical settings. This system is based on the ADS_BP and BioPatRec, both open-source bioelectric signal acquisition hardware and MATLAB-based pattern recognition software, respectively. We provide 3D-printable housing enabling the hardware to be worn by users during experiments and demonstrate the suitability of this platform for real-time signal acquisition and classification. In conjunction, these developments provide a unified hardware-software platform for a cost of around 150 USD. This device can enable researchers and clinicians to record bioelectric signals from non-disabled or motor-impaired individuals in laboratory or clinical settings, and to perform offline or real-time intent classification for the control of robotic and virtual devices

The Smartphone Brain Scanner: A Portable Real-Time Neuroimaging System

Combining low cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. We present a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system - Smartphone Brain Scanner - combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully mobile system for real-time 3D EEG imaging. We discuss the benefits and challenges of a fully portable system, including technical limitations as well as real-time reconstruction of 3D images of brain activity. We present examples of the brain activity captured in a simple experiment involving imagined finger tapping, showing that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using a off-the-shelf consumer neuroheadset is lower compared to that obtained using high density standard EEG equipment, we propose that mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings