Collaborative robots are expected to physically interact with humans in daily living and the workplace, including industrial and healthcare settings. A key related enabling technology is tactile sensing, which currently requires addressing the outstanding scientific challenge to simultaneously detect contact location and intensity by means of soft conformable artificial skins adapting over large areas to the complex curved geometries of robot embodiments. In this work, the development of a large-area sensitive soft skin with a curved geometry is presented, allowing for robot total-body coverage through modular patches. The biomimetic skin consists of a soft polymeric matrix, resembling a human forearm, embedded with photonic fibre Bragg grating transducers, which partially mimics Ruffini mechanoreceptor functionality with diffuse, overlapping receptive fields. A convolutional neural network deep learning algorithm and a multigrid neuron integration process were implemented to decode the fibre Bragg grating sensor outputs for inference of contact force magnitude and localization through the skin surface. Results of 35 mN (interquartile range 56 mN) and 3.2 mm (interquartile range 2.3 mm) median errors were achieved for force and localization predictions, respectively. Demonstrations with an anthropomorphic arm pave the way towards artificial intelligence based integrated skins enabling safe human–robot cooperation via machine intelligence.
Musculoskeletal disorders are the most common form of occupational ill-health. Neck pain is one of the most prevalent musculoskeletal disorders experienced by computer workers. Wrong postural habits and non-compliance of the workstation to ergonomics guidelines are the leading causes of neck pain. These factors may also alter respiratory functions. Health and safety interventions can reduce neck pain and, more generally, the symptoms of musculoskeletal disorders and reduce the consequent economic burden. In this work, a multi-parametric wearable system based on two fiber Bragg grating sensors is proposed for monitoring neck movements and breathing activity of computer workers. The sensing elements were positioned on the neck, in the frontal and sagittal planes, to monitor: (i) flexion-extension and axial rotation repetitions, and (ii) respiratory frequency. In this pilot study, five volunteers were enrolled and performed five repetitions of both flexion-extension and axial rotation, and ten breaths of both quite breathing and tachypnea. Results showed the good performances of the proposed system in monitoring the aforementioned parameters when compared to optical reference systems. The wearable system is able to well-match the trend in time of the neck movements (both flexion-extension and axial rotation) and to estimate mean and breath-by-breath respiratory frequency values with percentage errors ≤6.09% and ≤1.90%, during quiet breathing and tachypnea, respectively.
Cardiac radiofrequency ablation (RFA) has received substantial attention for the treatment of multiple arrhythmias. In this scenario, there is an ever-growing demand for monitoring the temperature trend inside the tissue as it may allow an accurate control of the treatment effects, with a consequent improvement of the clinical outcomes. There are many methods for monitoring temperature in tissues undergoing RFA, which can be divided into invasive and non-invasive. This paper aims to provide an overview of the currently available techniques for temperature detection in this clinical scenario. Firstly, we describe the heat generation during RFA, then we report the principle of work of the most popular thermometric techniques and their features. Finally, we introduce their main applications in the field of cardiac RFA to explore the applicability in clinical settings of each method.
Low back pain (LBP) is one of the musculoskeletal disorders that most affects workers. Among others, one of the working categories which mainly experiences such disease are video terminal workers. As it causes exploitation of the National Health Service and absenteeism in workplaces, LBP constitutes a relevant socio-economic burden. In such a scenario, a prompt detection of wrong seating postures can be useful to prevent the occurrence of this disorder. To date, many tools capable of monitoring the spinal range of motions (ROMs) are marketed, but most of them are unusable in working environments due to their bulkiness, discomfort and invasiveness. In the last decades, fiber optic sensors have made their mark allowing the creation of light and compact wearable systems. In this study, a novel wearable device embedding a Fiber Bragg Grating sensor for the detection of lumbar flexion-extensions (F/E) in seated subjects is proposed. At first, the manufacturing process of the sensing element was shown together with its mechanical characterization, that shows linear response to strain with a high correlation coefficient (R2 > 0.99) and a sensitivity value (Sε) of 0.20 nm∙mε−1. Then, the capability of the wearable device in measuring F/E in the sagittal body plane was experimentally assessed on a small population of volunteers, using a Motion Capture system (MoCap) as gold standard showing good ability of the system to match the lumbar F/E trend in time. Additionally, the lumbar ROMs were evaluated in terms of intervertebral lumbar distances (Δ d L 3 − L 1 ) and angles, exhibiting moderate to good agreement with the MoCap outputs (the maximum Mean Absolute Error obtained is ~16% in detecting Δ d L 3 − L 1 ). The proposed wearable device is the first attempt for the development of FBG-based wearable systems for workers’ safety monitoring.
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