The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings to support solutions in robotics, additive manufacturing, teleworking and human safety. This review synthesizes and evaluates studies investigating the use of MoCap technologies in industry-related research. A search was performed in the Embase, Scopus, Web of Science and Google Scholar. Only studies in English, from 2015 onwards, on primary and secondary industrial applications were considered. The quality of the articles was appraised with the AXIS tool. Studies were categorized based on type of used sensors, beneficiary industry sector, and type of application. Study characteristics, key methods and findings were also summarized. In total, 1682 records were identified, and 59 were included in this review. Twenty-one and 38 studies were assessed as being prone to medium and low risks of bias, respectively. Camera-based sensors and IMUs were used in 40% and 70% of the studies, respectively. Construction (30.5%), robotics (15.3%) and automotive (10.2%) were the most researched industry sectors, whilst health and safety (64.4%) and the improvement of industrial processes or products (17%) were the most targeted applications. Inertial sensors were the first choice for industrial MoCap applications. Camera-based MoCap systems performed better in robotic applications, but camera obstructions caused by workers and machinery was the most challenging issue. Advancements in machine learning algorithms have been shown to increase the capabilities of MoCap systems in applications such as activity and fatigue detection as well as tool condition monitoring and object recognition.

Retinal imaging is a fundamental tool in ophthalmic diagnostics. The potential use of retinal imaging within screening programs, with consequent need to analyze large numbers of images with high throughput, is pushing the digital image analysis field to find new solutions for the extraction of specific information from the retinal image. The aim of this review is to explore the latest progress in image processing techniques able to recognize specific retinal image features. and potential features of disease. In particular, this review aims to describe publically available retinal image databases, highlight different performance evaluators commonly used within the field, outline current approaches in feature-based retinal image analysis, and to map related trends. This review found two key areas to be addressed for the future development of automatic retinal image analysis: fundus image quality and the affect image processing may impose on relevant clinical information within the images. Performance evaluators of the algorithms reviewed are very promising, however absolute values are difficult to interpret when validating system suitability for use within clinical practice

Wearable systems for remote monitoring of physiological parameter are ready to evolve towards wearable imaging systems. The Electrical Impedance Tomography (EIT) allows the non-invasive investigation of the internal body structure. The characteristics of this low-resolution and low-cost technique match perfectly with the concept of a wearable imaging device. On the other hand low power consumption, which is a mandatory requirement for wearable systems, is not usually discussed for standard EIT applications. In this work a previously developed low power architecture for a wearable bioimpedance sensor is applied to EIT acquisition and reconstruction, to evaluate the impact on the image of the limited signal to noise ratio (SNR), caused by low power design. Some anatomical models of the chest, with increasing geometric complexity, were developed, in order to evaluate and calibrate, through simulations, the parameters of the reconstruction algorithms provided by Electrical Impedance Diffuse Optical Reconstruction Software (EIDORS) project. The simulation results were compared with experimental measurements taken with our bioimpedance device on a phantom reproducing chest tissues properties. The comparison was both qualitative and quantitative through the application of suitable figures of merit; in this way the impact of the noise of the low power front-end on the image quality was assessed. The comparison between simulation and measurement results demonstrated that, despite the limited SNR, the device is accurate enough to be used for the development of an EIT based imaging wearable system.

Autorefraction consists of the automatic sensing of three parameters -spherical error, cylindrical error, slope of the principal meridian -that describe the deviation of the focusing properties of an ametropic eye with respect to an emmetropic state. Low-cost autorefractors would be highly desirable in resource-poor settings, for the stratification of patients between those who can be treated in the community and those who need to be referred to specialist care. In the present paper, we describe the implementation of an autorefractor based on projecting patterns onto the retina of an eye and observing the projected pattern through an ophthalmoscopic camera configuration coaxial with the projection path. Tunable optics in the coaxial path, combined with appropriate image processing, allows determination of the three parameters. The simplicity and performance of the setup, measured on an eye simulator, shows promise towards clinical use in the community. Further work is needed to confirm the performance in vivo.

Repetitive movements that involve a significant shift of the body's center of mass can lead to shoulder and elbow fatigue, which are linked to injury and musculoskeletal disorders if not addressed in time. Research has been conducted on the joint torque individuals can produce, a quantity that indicates the ability of the person to carry out such repetitive movements. Most of the studies surround gait analysis, rehabilitation, the assessment of athletic performance, and robotics. The aim of this study is to develop a model that estimates the maximum shoulder and elbow joint torque an individual can produce based on anthropometrics and demographics without taking a manual measurement with a force gauge (dynamometer). Nineteen subjects took part in the study which recorded maximum shoulder and elbow joint torques using a dynamometer. Sex, age, body composition parameters, and anthropometric data were recorded, and relevant parameters which significantly contributed to joint torque were identified using regression techniques. Of the parameters measured, body mass index and upper forearm volume predominantly contribute to maximum torque for shoulder and elbow joints; coefficient of determination values were between 0.6 and 0.7 for the independent variables and were significant for maximum shoulder joint torque (P<0.001) and maximum elbow joint torque (P<0.005) models. Two expressions illustrated the impact of the relevant independent variables on maximum shoulder joint torque and maximum elbow joint torque, using multiple linear regression. Coefficient of determination values for the models were between 0.6 and 0.7. The models developed enable joint torque estimation for individuals using measurements that are quick and easy to acquire, without the use of a dynamometer. This information is useful for those employing joint torque data in biomechanics in the areas of health, rehabilitation, ergonomics, occupational safety, and robotics.Clinical Relevance-The rapid estimation of arm joint torque without the direct force measurement can help occupational safety with the prevention of injury and musculoskeletal disorders in several working scenarios.