Condition Monitoring of Pneumatic Drive Systems Based on the AI Method Feed-Forward Backpropagation Neural Network.

Machine condition monitoring is used in a variety of industries as a very efficient strategy for equipment maintenance. This paper presents a study on monitoring a pneumatic system using a feed-forward backpropagation neural network as a classifier and compares the results obtained with different sensor signals and associated extracted features as input for classification. The vibrations of the body of a pneumatic cylinder are acquired using both common industrial sensors and low-cost sensors integrated into an Arduino board. Pressure sensors for both chambers and a position sensor are also used. Power spectral density (PSD) is used to extract features from the acceleration signals, as well as statistical indices. Statistical indices are considered for pressure and position sensors. The results, which are based on experimental data obtained on a test bench, show that a feed-forward neural network makes it possible to identify the operating states with a good degree of reliability. Even with low-cost instrumentation, it is possible to realize reliable condition monitoring based on vibrations. This last result is particularly important as it can help to further increase the uptake of this maintenance approach in the industrial environment.

Sensors and Actuation Technologies in Exoskeletons: A Review.

Exoskeletons are robots that closely interact with humans and that are increasingly used for different purposes, such as rehabilitation, assistance in the activities of daily living (ADLs), performance augmentation or as haptic devices. In the last few decades, the research activity on these robots has grown exponentially, and sensors and actuation technologies are two fundamental research themes for their development. In this review, an in-depth study of the works related to exoskeletons and specifically to these two main aspects is carried out. A preliminary phase investigates the temporal distribution of scientific publications to capture the interest in studying and developing novel ideas, methods or solutions for exoskeleton design, actuation and sensors. The distribution of the works is also analyzed with respect to the device purpose, body part to which the device is dedicated, operation mode and design methods. Subsequently, actuation and sensing solutions for the exoskeletons described by the studies in literature are analyzed in detail, highlighting the main trends in their development and spread. The results are presented with a schematic approach, and cross analyses among taxonomies are also proposed to emphasize emerging peculiarities.

The Robot Selection Problem for Mini-Parallel Kinematic Machines: A Task-Driven Approach to the Selection Attributes Identification.

In the last decades, the Robot Selection Problem (RSP) has been widely investigated, and the importance of properly structuring the decision problem has been stated. Crucial aspect in this process is the correct identification of the robot attributes, which should be limited in number as much as possible, but should be also able to detect at best the peculiar requirements of specific applications. Literature describes several attributes examples, but mainly dedicated to traditional industrial tasks, and applied to the selection of conventional industrial robots. After a synthetic review of the robot attributes depicted in the RSP literature, presented with a custom taxonomy, this paper proposes a set of possible requirements for the selection problem of small scale parallel kinematic machines (PKMs). The RSP is based on a task-driven approach: two mini-manipulators are compared as equivalent linear actuators to be integrated within a more complex system, for the application in both an industrial and a biomedical environment. The set of identified criteria for the two environments is proposed in the results and investigated with respect to working conditions and context in the discussion, emphasizing limits and strength points of this approach; finally, the conclusions synthesizes the main results.

Lower Tinetti scores can support an early diagnosis of spatial neglect in post-stroke patients.

BACKGROUND: Neglect represents a severe complication of stroke, which impairs patients' daily activities. An early diagnosis of neglect is fundamental for management decisions. AIM: The aim of this study is to evaluate the usefulness of the Tinetti Test as an outcome of spatial neglect in post-stroke patients. DESIGN: Observational retrospective data analysis. SETTING: Rehabilitation Hospital. POPULATION: Cohort of post-stroke adults admitted in our Rehabilitation Unit. METHODS: One hundred and sixty stroke patients were evaluated between the 1st of January 2015 and the 31st of December 2016 at our Department. Eighty-nine inpatients matched the inclusion criteria. Their scores of the Tinetti Test for balance condition and gait function were compared with Bells Test and line bisection task for spatial neglect. Global independence activity was also assessed using Barthel Index and global cognitive functioning by means of the Mini-Mental State Examination. RESULTS: Twenty-two patients between the 89 patients included in this study were affected by spatial neglect at admission. A high statistical significant correlation was observed between lower Tinetti scores and neglect presence (mean Tinetti Score: 2.36 neglect; 7.82 non-neglect; P<0.001). CONCLUSIONS: The Tinetti Test is a well-established assessment scale to measure balance ability and gait function in post-stroke patients. Results from this study suggest that Tinetti Test may be considered as an early ecological screening tool for the diagnosis of neglect in post-stroke patients. CLINICAL REHABILITATION IMPACT: The alternative use of the Tinetti Test for the diagnosis of spatial neglect.