A5064947412- Muscle activation and electromyography studies
- Balance, Gait, and Falls Prevention
- Advanced Sensor and Energy Harvesting Materials
- ECG Monitoring and Analysis
- Cerebral Palsy and Movement Disorders
- EEG and Brain-Computer Interfaces
- Non-Invasive Vital Sign Monitoring
- Lower Extremity Biomechanics and Pathologies
- Cardiac electrophysiology and arrhythmias
- Diabetic Foot Ulcer Assessment and Management
- Phonocardiography and Auscultation Techniques
- Motor Control and Adaptation
- Heart Rate Variability and Autonomic Control
- Stroke Rehabilitation and Recovery
- Cardiac Arrhythmias and Treatments
- Prosthetics and Rehabilitation Robotics
- Sports injuries and prevention
- Neonatal and fetal brain pathology
- Gait Recognition and Analysis
- Sports Performance and Training
- Effects of Vibration on Health
- Musculoskeletal pain and rehabilitation
- Hand Gesture Recognition Systems
- Parkinson's Disease Mechanisms and Treatments
- Neuroscience and Neural Engineering
Marche Polytechnic University
2016-2025
Polytechnic University of Turin
2015
Engineering (Italy)
2014
Argerich Hospital
2012
Ospedali Riuniti di Ancona
1989-2005
In this study, a minimal setup for the ankle joint kinematics estimation is proposed relying only on proximal information of lower-limb, i.e. thigh muscles activity and kinematics. To purpose, myoelectric Rectus Femoris (RF), Biceps (BF), Vastus Medialis (VM) were recorded by surface electromyography (sEMG) from six healthy subjects during unconstrained walking task. For each subject, angular hip joints synchronously with sEMG signal total 288 gait cycles. Two feature sets extracted signals,...
Correctly identifying gait phases is a prerequisite to achieve spatial/temporal characterization of muscular recruitment during walking. Recent approaches have addressed this issue by applying machine learning techniques treadmill-walking data. We propose deep approach for surface electromyographic (sEMG)-based classification stance/swing and prediction the foot–floor-contact signal in more natural walking conditions (similar everyday ones), overcoming constraints controlled environment,...
Recent studies have highlighted the possibility of using surface electromyographic (EMG) signals to develop human–computer interfaces that are also able recognize complex motor tasks involving hand as handwriting digits. However, automatic recognition words from EMG information has not yet been studied. The aim this study is investigate feasibility combined forearm and wrist probes for solving problem 30 with consolidated machine-learning techniques aggregating state-of-the-art features...
Machine-learning techniques are suitably employed for gait-event prediction from only surface electromyographic (sEMG) signals in control subjects during walking. Nevertheless, a reference approach is not available cerebral-palsy hemiplegic children, likely due to the large variability of foot-floor contacts. This study designed investigate machine-learning-based approach, specifically developed binary classify gait events and predict heel-strike (HS) toe-off (TO) timing sEMG...
The use of surface electromyographic (sEMG) signals, alongside pattern recognition (PR) systems, is fundamental in the design and control assistive technologies. Transient sEMG signal epochs at early beginning movement provide important information for upper-limb intent motion recognition. However, only few studies investigated role transient myoelectric architectures. Therefore, this work, focus was given to signals intact-limb (IL) subjects transhumeral amputees (AMP), who performed a...
Despite hand gesture recognition is a widely investigated field, the design of myoelectric architectures for detecting finer motor task, like handwriting, less studied.However, writing tasks involving cognitive loads represent an important aspect toward generalization myoelectric-based human-machine interfaces (HMI), and also many rehabilitative tasks.In this study, handwriting ten digits was faced under control perspective, considering probes setup feature extraction step.Time frequency...
Motion intent detection for shoulder actions may allow the early decoding of upper limb motions, thus enhancing real-time usability rehabilitative devices and prosthetics. In this study we faced a motion problem involving four movements by using transient epochs surface electromyographic (EMG) signals. Reliability time frequency domain features was investigated through clusters separability properties classification performances. Those able to provide accuracy greater than 90% were selected...
Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) Support Vector Machine (SVM), typically experience performance degradation when modeling gait cycle with more than just stance swing phases. This study introduces a...
Contactless detection is one of the new frontiers technological innovation in field healthcare, enabling unobtrusive measurements biomedical parameters. Compared to conventional methods for Heart Rate (HR) that employ expensive and/or uncomfortable devices, such as Electrocardiograph (ECG) or pulse oximeter, contactless HR offers fast and continuous monitoring heart activities provides support clinical analysis without need user wear a device. This paper presents validation study estimation...
Fetal well-being evaluation may be accomplished by monitoring cardiac activity through fetal electrocardiography. Direct electrocardiography (acquired scalp electrodes) is the gold standard but its invasiveness limits clinical applicability. Instead, use of indirect abdominal limited poor signal quality.Aim this study was to evaluate suitability Segmented-Beat Modulation Method denoise electrocardiograms in order achieve a signal-quality at least comparable direct ones.Direct and recordings,...
The analysis of gait rhythm by pattern recognition can support the state-of-the-art clinical methods for identification neurodegenerative diseases (NDD). In this study, we investigated use time domain (TD) and time-dependent spectral features (PSDTD) detecting NDD sub-types. Also, proposed two classification pathways supporting diagnosis, first one made a two-step learning phase, whereas second encompasses single model. We considered stride-to-stride fluctuation data healthy controls (CN),...
Variability of myoelectric activity during walking is the result human capability to adapt both intrinsic and extrinsic perturbations. The availability sEMG signals lasting at least some minutes (instead seconds) needed comprehensively analyze variability surface electromyographic (sEMG) signals. current study introduces a dataset long-lasting recorded sessions 31 healthy subjects, aged between 20 30 years, conducted Movement Analysis Lab Università Politecnica delle Marche, Ancona, Italy....
Indirect fetal electrocardiography is preferable to direct because of being noninvasive and applicable also during the end pregnancy, besides labor. Still, former strongly affected by noise so that even R-peak detection (which essential for heart-rate evaluations subsequent processing procedures) challenging. Some studies have applied Pan-Tompkins' algorithm that, however, was originally designed adult applications. Thus, this work evaluated suitability applications, proposed adjustments...