The relationship between flexibility and the pattern formed by the surface electromyography activity of the back muscles while performing a dynamic trunk flexion-extension task is not yet thoroughly understood, although many previous studies have adopted it as their focus in the literature. Additionally, several studies have proposed technologies and algorithms to analyse the flexion-relaxation phenomenon, which is defined by myoelectric silence that occurs when the subject's torso exceeds a certain flexion angle. Before participating in the flexion-relaxation test, subjects involved in the data collection underwent medical examinations, in which their physical condition, perceived pain, and level of disability were reported in their anamnesis. During the flexion-relaxation test, which was conducted with 25 subjects with and without low back pain, subjects wore four surface electromyography electrodes positioned over the back muscles, as well as an inertial sensor to estimate trunk inclination.

An accurate clinical assessment of the flexion-relaxation phenomenon on back muscles requires objective tools for the analysis of surface electromyography signals correlated with the real movement performed by the subject during the flexion-relaxation test. This paper deepens the evaluation of the flexion-relaxation phenomenon using a wireless body sensor network consisting of sEMG sensors in association with a wearable device that integrates accelerometer, gyroscope, and magnetometer. The raw data collected from the sensors during the flexion relaxation test are processed by an algorithm able to identify the phases of which the test is composed, provide an evaluation of the myoelectric activity and automatically detect the phenomenon presence/absence. The developed algorithm was used to process the data collected in an acquisition campaign conducted to evaluate the flexion-relaxation phenomenon on back muscles of subjects with and without Low Back Pain. The results have shown that the proposed method is significant for myoelectric silence detection and for clinical assessment of electromyography activity patterns.

An aspect correlated with climate change is certainly represented by the alternation of severe floods and relevant drought periods. Moreover, there is evidence that changes in climate and land cover are inducing changes in stream channel cross-sections, altering local channel capacity. A direct consequence of a significant change in the local channel capacity is that the relationship between the amount of water flowing at a given point in a river or stream (usually at gauging stations) and the corresponding stage in that section, known as a stage–discharge relationship or rating curve, is changed. The key messages deriving from the present work are: (a) the more frequent and extreme the floods become, the more rapid the changes in the stream channel cross-section become, (b) from an operational point of view, the collection and processing of field measurements of the stage and corresponding discharge at a given section in order to quickly and frequently update the rating curve becomes a priority. It is, therefore, necessary to define a control system for acquiring hydrological data capable of keeping river levels and discharges under control to support flood early warnings and water management. The proposed stage–discharge management system is used by the Civil Protection Service of the Marche Region (east-central Italy) for the monitoring of river runoff in the regional watersheds. The Civil Protection Service staff performs stage–discharge field measurements using water level sensors and recorders (e.g., staff gauges, submersible pressure transducers, ultrasound and radar sensors) and a current meter, acoustic doppler velocimeter, acoustic doppler current profilers, portable mobile radar profiler and salt dilution method equipment, respectively. Power functions are fitted to the stage–discharge field data. Furthermore, extrapolation is performed to cover the full range of flow measurements; in general, extrapolation is not an easy task because of sharp changes in the stream cross-section geometry for very high or very low stages. In the present work, we also focused attention on the application problems that occur in practice and the need for frequent updating.

This paper presents a wearable wireless sensor as a support tool used to assess the postural stability of a subject. Changes in postural steadiness are age-related. Their characterization and identification, through the development of a non-invasive device, will allow to identify persons at risk. In this work we develop an accurate device for real-time estimation of balance parameters with the aim to reduce falls in the elderly population. The proposed device embeds a 3-axis accelerometer, a 3-axis gyroscope and a 3-axis magnetometer. In order to get an accurate estimation of the displacement of the Center of Gravity (CoG) and of the Centre of Pressure (CoP), a data fusion algorithm is used. In particular, an Attitude and Heading Reference System (AHRS) has been implemented and optimized to provide an estimate of the orientation of the human body wearing the sensors. Moreover, we proposed an algorithm to evaluate the postural stability. Therefore, our algorithm has the aim of obtaining the CoG and CoP displacements in the anterior-posterior (AP) and medial-lateral (ML) directions, from the information provided by the AHRS, based on the inverse pendulum mathematical model. Results from these experiments on a population of healthy subjects suggest that the proposed device and algorithm can effectively be used for measuring CoG and CoP displacements.

The myoelectric activity of the back muscles can be studied to evaluate the flexion-relaxation phenomenon and find differences between electromyography patterns on different subjects. In this paper, we propose an algorithm able to provide a myoelectric silence evaluation based on the data acquired from a wireless body sensor network consisting of surface electromyography sensors in association with a wearable inertial measurement unit. From the study group was chosen a gold standard subject, a healthy control with the best regular patterns, as a reference to find a first validity range. Through the subsequent iterations, the range was modified to include the other healthy subjects who showed muscle relaxation according to the previous ranges. Through this likelihood analysis, we want to compare patterns on different channels, identified by the electromyography root mean squared values, to study and find with iterations a validity range for the myoelectric activity silence identification and classification. The proposed algorithm was tested by processing the data collected in an acquisition campaign conducted to evaluate the flexion-relaxation phenomenon on the back muscles of subjects with and without lower back pain. The results show that the submitted method is significant for the clinical assessment of electromyography activity patterns to evaluate which are the subjects that have patterns near or far from the gold standard. This analysis is useful both for prevention and for assessing the progress of subjects with low back pain undergoing physiotherapy.