Hand gesture recognition is a crucial task for the automated translation of sign language, which enables communication for the deaf. This work proposes the usage of a magnetic positioning system for recognizing the static gestures associated with the sign language alphabet. In particular, a magnetic positioning system, which is comprised of several wearable transmitting nodes, measures the 3D position and orientation of the fingers within an operating volume of about 30 × 30 × 30 cm, where receiving nodes are placed at known positions. Measured position data are then processed by a machine learning classification algorithm. The proposed system and classification method are validated by experimental tests. Results show that the proposed approach has good generalization properties and provides a classification accuracy of approximately 97% on 24 alphabet letters. Thus, the feasibility of the proposed gesture recognition system for the task of automated translation of the sign language alphabet for fingerspelling is proven.

Magnetic localization is used in many indoor positioning applications, such as industrial, medical and IoT, for its benefits related to the absence of line of sight needs, multipath and fading, the low cost of transmitters and receivers, the simple development of set-ups made of coils and magnetic sensors. In short ranges applications, this technology could bring some advantages respect to ultrasound, laser or RF ones. Nevertheless, fixed both the desired accuracy and the energy constraints, the optimal design of a localization system based on magnetic measurement depends on several factors: the dimension, the number and the optimal positions of the anchors, the uncertainties due to the sensing elements and the data acquisition systems. To preliminary fix all these parameters, suitable simulation environments allow developers saving time and money in developing localization applications. Many magnetic field simulators are available, but it is rare to find those which, considering the uncertainty due to the receiver and data acquisition systems, are able to provide optimal anchors scenario given a target accuracy. To address this problem, the paper presents a simulation tool providing the user with design requirements for a given target accuracy. The aim of the paper is to perform first steps in providing a read-to-use specification framework that given the localization domain, the mobile sensors and the data acquisition system characteristics and the target accuracy, helps the developer of indoor magnetic positioning systems. The actual validity of the simulation model has been tested on a real set-up.

The growing diffusion and usability of additive printing techniques have created new manufacturing scenarios for products. Printed electronics is playing a key role in the design of next-generation objects. These objects are no longer only able to fulfill their original function, but through specific sensors and circuits can measure physical quantities in the surrounding environment. In this way, a conventional object is made smart, being able to communicate with other objects or remote units. This kind of change fits perfectly into the context of Industry 4.0, where the ability to collect additional information can increase efficiency in the production, transportation, and storage of products. Smart objects can be manufactured with numerous different technologies and materials depending on the performance required and on the specific application. The purpose of this review is to provide an analysis of printed technologies that enable resistive sensors printing on complex surfaces for smart objects fabrication. First, an explanation of the technologies under consideration is provided. Then, an analysis of the aspects that affect the final print quality for each technique is provided, highlighting advantages and disadvantages. The paper also indicates the limitations and potential of these printed technologies for smart objects.

Every year in Europe more than 500 thousand injuries that involve the anterior cruciate ligament (ACL) are diagnosed. The ACL is one of the main restraints within the human knee, focused on stabilizing the joint and controlling the relative movement between the tibia and femur under mechanical stress (i.e., laxity). Ligament laxity measurement is clinically valuable for diagnosing ACL injury and comparing possible outcomes of surgical procedures. In general, knee laxity assessment is manually performed and provides information to clinicians which is mainly subjective. Only recently quantitative assessment of knee laxity through instrumental approaches has been introduced and become a fundamental asset in clinical practice. However, the current solutions provide only partial information about either static or dynamic laxity. To support a multiparametric approach using a single device, an innovative smart knee brace for knee laxity evaluation was developed. Equipped with stretchable strain sensors and inertial measurement units (IMUs), the wearable system was designed to provide quantitative information concerning the drawer, Lachman, and pivot shift tests. We specifically characterized IMUs by using a reference sensor. Applying the Bland–Altman method, the limit of agreement was found to be less than 0.06 m/s² for the accelerometer, 0.06 rad/s for the gyroscope and 0.08 μT for the magnetometer. By using an appropriate characterizing setup, the average gauge factor of the three strain sensors was 2.169. Finally, we realized a pilot study to compare the outcomes with a marker-based optoelectronic stereophotogrammetric system to verify the validity of the designed system. The preliminary findings for the capability of the system to discriminate possible ACL lesions are encouraging; in fact, the smart brace could be an effective support for an objective and quantitative diagnosis of ACL tear by supporting the simultaneous assessment of both rotational and translational laxity. To obtain reliable information about the real effectiveness of the system, further clinical validation is necessary.

Aim. To evaluate the conditioning capabilities of the DAR™ Hygrobac™ S, a Heat and Moisture Exchanger (HME), using a new device to measure the temperature (T) and the absolute humidity (AH) of the ventilated gasesin vivoduring mechanical ventilation in Intensive Care Unit (ICU) patients.Materials and Methods. In 49 mechanically ventilated ICU patients, we evaluated T and AH, indicating the HME efficacy, during the inspiratory phase upstream and downstream the HME and the ratio of inspired AH to expired AH and the difference between expired T and inspired T indicated the HME efficiency. Efficacy and efficiency were assessed at three time points: at baseline (t₀, HME positioning time), at 12 hours (t₁), and at 24 hours (t₂) using a dedicated,ad hocbuilt wireless device. Differences over time were evaluated using one-way ANOVA for repeated measures, whereas differences betweenin vivoand laboratory values (declared by the manufacturer according to UNI® EN ISO 9360 international standard) were evaluated using one-sample Student t-test.Results. 49 HMEs were analysedin vivoduring mechanical ventilation. T and AH means (SD) of the inspired gas (the efficacy) were 31.5°C (1.54) and 32.3 mg/l (2.60) at t₀, 31.1°C (1.34) and 31.7 mg/l (2.26) at t₁, and 31°C (1.29) and 31.4 mg/l (2.27) at t₂. Both efficiency parameters were constant over time (inspired AH/expired AH=89%, p=0.24; and expired T–inspired T = 2.2°C, p=0.81). Compared with laboratory values,in vivoT and AH indicating efficacy were significantly lower (p<0.01), whereas the efficiency was significantly higher (p<0.01).Conclusions. HME performances can be accurately assessed for prolonged periodsin vivoduring routine mechanical ventilation in ICU patients. Temperature and absolute humidity of ventilated gasesin vivowere maintained within the expected range and remained stable over time. HME efficacy and efficiencyin vivosignificantly differed from laboratory values.