The use of electroencephalography (EEG) has recently grown as a means to diagnose neurodegenerative pathologies such Alzheimer's disease (AD). AD recognition can benefit from machine learning methods that, compared with traditional manual diagnosis methods, have higher reliability and improved accuracy, being able manage large amounts data. Nevertheless, may exhibit lower accuracies when faced incomplete, corrupted, or otherwise missing data, so it is important do develop robust...

Identifying diseases from images of plant leaves is one the most important research areas in precision agriculture. The aim this paper to propose an image detector embedding a resource constrained convolutional neural network (CNN) implemented low cost, power platform, named OpenMV Cam H7 Plus, perform real-time classification disease. CNN so obtained has been trained on two specific datasets for detection, ESCA-dataset and PlantVillage-augmented dataset, low-power, low-cost Python...

The human activity monitoring technology is one of the most important technologies for ambient assisted living, surveillance-based security, sport and fitness activities, healthcare elderly people. performed in two steps: acquisition body signals classification activities being performed. This paper presents a low-cost wearable wireless system specifically designed to acquire surface electromyography (sEMG) accelerometer when performing as well applications.The proposed consists several...

Photoplethysmography (PPG) is a common and practical technique to detect human activity other physiological parameters commonly implemented in wearable devices. However, the PPG signal often severely corrupted by motion artifacts. The aim of this paper address recognition (HAR) task directly on device, implementing recurrent neural network (RNN) low cost, power microcontroller, ensuring required performance terms accuracy complexity. To reach goal, (i) we first develop an RNN, which...

Esca is one of the most common disease that can severely damage grapevine. This disease, if not properly treated in time, cause vegetative stress or death attacked plant, with consequence losses production as well a rising risk propagation to closer grapevines. Nowadays, detection carried out manually through visual surveys usually done by agronomists, requiring enormous amount time. Recently, image processing, computer vision and machine learning methods have been widely adopted for plant...

The encephalographic (EEG) signal is an electrical that measures the brain activity. Due to its noninvasive acquisition process, it often used investigate presence of Alzheimer's disease (AD) or other common forms neurodegerative disorders due changes, occur most frequently in older adults. Early detection prodromal stages AD, which individual has mild but measurable cognitive deficiencies with no significant effect on functional activity daily living, may help reduce mortality and...

We introduce a dataset to provide insights about the photoplethysmography (PPG) signal captured from wrist in presence of motion artifacts and accelerometer signal, simultaneously acquired same wrist. The data presented were collected by electronics research team Department Information Engineering, Polytechnic University Marche, Ancona, Italy. This article describes recorded 7 subjects includes 105 PPG signals (15 for each subject) corresponding tri-axial measured with sampling frequency 400...

Esca is one of the most common grape leaf diseases that seriously affect yield, causing a loss global production in range 20%–40%. Therefore, timely and effective identification disease could help to develop an early treatment approach control its spread while reducing economic losses. For this purpose use computer vision machine learning techniques for recognizing plant have been extensively studied recent years. The aim paper propose image detector based on high-performance convolutional...

Seismic wave picking is an essential task the implementation of Earthquake Early Warning (EEW) systems. While artificial intelligence methods show excellent accuracy, most were designed for devices with high computational resources. At same time, distributed approaches systems promise viable, widespread alert This paper introduces a complete AIoT system earthquake on resource-constrained devices. An algorithm has been developed to enable switch between detection mode, in which inferences are...

Wireless surface electromyography (sEMG) sensors are very practical in that they can be worn freely, but the radio link between them and receiver might cause unpredictable latencies hinder accurate synchronization of time multiple sensors, which is an important aspect to study, e.g., correlation signals sampled at different sites. Moreover, minimize power consumption, it useful design a sensor with clock domains so each subsystem only runs minimum frequency for correct operation, thus saving...

This paper presents a flexible low-cost wireless system specifically designed to acquire fitness metrics both from surface electromyographic (sEMG) and electrocardiographic (ECG) signals. The system, that can be easily extended capture process many other biological signals as well the motion-related body signals, consists of several ultralight sensing nodes acquire, amplify, digitize, transmit or mechanical one more base stations through 2.4 GHz radio link using custom-made communication...

Falls and their aftermath pose significant healthcare challenges, impacting individuals across various age groups occupational backgrounds. These incidents detrimentally affect functional mobility overall quality of life, necessitating a comprehensive approach to fall detection systems in diverse populations. Therefore, wearable devices are necessary continuously monitor activities. This work introduces novel deep-learning model specifically optimized for edge capable detecting falls. The...

Speaker identification plays a crucial role in biometric person as systems based on human speech are increasingly used for the recognition of people. Mel frequency cepstral coefficients (MFCCs) have been widely adopted decades processing to capture speech-specific characteristics with reduced dimensionality. However, although their ability decorrelate vocal source and tract filter make them suitable recognition, they greatly mitigate speaker variability, specific characteristic that...

This paper proposes a wireless sensor device for the real-time acquisition of bioelectrical signals such as electromyography (EMG) and electrocardiography (ECG), coupled with an inertial sensor, to provide comprehensive stream data suitable human activity detection, motion analysis, technology-assisted nursing persons physical or cognitive impairments. The is able acquire up three independent channels (six electrodes), each 24 bits resolution sampling rate 3.2 kHz, has 6-DoF platform...

Cardiovascular diseases are one of the main causes death around world.Automatic classification electrocardiogram (ECG) signals is paramount importance in unmanned detection a wide range heartbeat abnormalities.In this paper an effective multi-class beat classifier, based on statistical identification minimum-complexity model, presented.This methodology extracts from ECG signal multivariate relationships its natural modes, by means separation property Karhunen-Loève transform (KLT).Then, it...

The present work aims at the evaluation of effectiveness different machine learning algorithms on a variety clinical data, derived from small, medium, and large publicly available databases. To this end, several were tested, their performance, both in terms accuracy time required for training testing phases, are here reported. Sometimes data preprocessing phase was also deemed necessary to improve performance procedures, order reduce problem size. In such cases detailed analysis compression...

Neurodegenerative diseases severely impact the life of millions patients worldwide, and their occurrence is more increasing proportionally to longer expectancy. Electroencephalography has become an important diagnostic tool for these diseases, due its relatively simple procedure, but it requires analyzing a large number data, often carrying small fraction informative content. For this reason, machine learning tools have gained considerable relevance as aid classify potential signs specific...

The human activity diarization using wearable technologies is one of the most important supporting techniques for ambient assisted living, sport and fitness activities, healthcare elderly people. performed in two steps: acquisition body signals classification activities being performed. This paper presents a technique data fusion at classifier level accelerometer sEMG acquired by low-cost wireless system monitoring when performing as well applications. To demonstrate capability diarizing...

Photoplethysmography (PPG) is a non invasive measurement of the blood flow, that can be used instead electrocardiography to estimate heart rate (HR). Most existing techniques for HR monitoring in fitness with PPG focus on slowly running alone, while those suitable intensive physical exercise need an initialization stage which wearers are required stand still several seconds. This paper present novel algorithm estimation from signal based motion artifact removal (MAR) and adaptive tracking...

Wearable devices offer a convenient means to monitor biosignals in real time at relatively low cost, and provide continuous monitoring without causing any discomfort. Among signals that contain critical information about human body status, electromyography (EMG) signal is particular useful muscle functionality activity during sport, fitness, or daily life. In surface (sEMG) has proven be suitable technique several health applications, thanks its non-invasiveness ease use. However, recording...