The accurate and reliable assessment of gait parameters is assuming an important role, especially in the perspective of designing new therapeutic and rehabilitation strategies for the remote follow-up of people affected by disabling neurological diseases, including Parkinson’s disease and post-stroke injuries, in particular considering how gait represents a fundamental motor activity for the autonomy, domestic or otherwise, and the health of neurological patients. To this end, the study presents an easy-to-use and non-invasive solution, based on a single RGB-D sensor, to estimate specific features of gait patterns on a reduced walking path compatible with the available spaces in domestic settings. Traditional spatio-temporal parameters and features linked to dynamic instability during walking are estimated on a cohort of ten parkinsonian and eleven post-stroke subjects using a custom-written software that works on the result of a body-tracking algorithm. Then, they are compared with the “gold standard” 3D instrumented gait analysis system. The statistical analysis confirms no statistical difference between the two systems. Data also indicate that the RGB-D system is able to estimate features of gait patterns in pathological individuals and differences between them in line with other studies. Although they are preliminary, the results suggest that this solution could be clinically helpful in evolutionary disease monitoring, especially in domestic and unsupervised environments where traditional gait analysis is not usable.

Continuous monitoring of ventilatory parameters such as tidal volume (TV) and minute ventilation (MV) has shown to be effective in the prevention of respiratory compromise events in hospitalized patients. However, the non-invasive estimation of respiratory volume in non-intubated patients remains an outstanding challenge. In this work, we present a novel approach to respiratory volume monitoring (RVM) that continuously predicts TV and MV in normal subjects. Respiratory flow in 19 volunteers under spontaneous breathing was recorded using respiratory inductance plethysmography and a temperature-based wearable sensor. Temperature signals were processed to identify features such as temperature amplitude and mean value, among others. The feature datasets were then used to train and validate three machine-learning (ML) algorithms for the prediction of respiratory volume based on temperature-related features. A model based on Random-Forest regression resulted in the lowest root mean-square error and was subsequently chosen to predict ventilatory parameters on subject test data not used in the construction of the model. Our predictions achieve a bias (mean error) in TV and MV of 16.04 mL and 0.19 L/min, respectively, which compare well with performance metrics reported in commercially-available RVM systems based on electrical impedance. Our results show that the combination of novel respiratory temperature sensors and machine-learning algorithms can deliver accurate and continuous estimates of TV and MV in healthy subjects.

Proper control of temperature and electrolyte circulation flow is mandatory in electrolytic cells to produce dense and high-purity cathodes. The electrochemical kinetics of cells is inherently dependent on these electrolyte variables. Continuous monitoring of electrolyte condition allows enhancing cathode quality, electrodeposition time, better utilization of electrolyte additives, early identification of temperature excursions and electrolyte flow blockages. Abnormal cell conditions can produce excessive evaporation, higher energy consumption, anode passivation that impair cathode production in copper electrorefining, and safety issues because the production of flammable hydrogen in copper electrowinning. Therefore, the monitoring of changes in temperature and electrolyte flow can give critical indicators of process deviations. With real-time information about electrolyte variables is possible to provide early warnings to face the wide variability of cell performance and safety conditions caused by electrolyte condition mismanagement. This paper proposes a non-invasive wireless sensor to monitoring the electrolyte temperature inside cells and estimates the electrolyte circulation flow through each cell simultaneously. The sensor design is suitable to highly corrosive sulfuric acid environments. The condition-monitoring sensor proposed is small size, lightweight, meets battery-free operation and non-sparking safety requirements. It uses an inductive link-based system for powering and a RF link for communicating. The result is a sensor that surpasses the features of standard instrumentation not suitable for electrolytic process monitoring.

Bruxism is an excessive clenching and involuntary parafunctional grinding of the teeth during sleep. It can cause damage to dental structures and temporomandibular joint dysfunction. An evaluation was performed on the effects over the masseter and temporalis anterior muscles in subjects with and without bruxism when electrical stimulation is applied to the right mental nerve. The results showed that, on average, the percentage decrease in the bruxist group for the right masseter was 25.02% and for the left masseter 25.87%. These results indicate that the inhibitory system produces an important decrease in the electrical activity of the two muscles, so it is a good starting point for a possible treatment in patients with bruxism and to develop new electronic stimulators.