Wearable technology has advanced significantly, offering real-time monitoring of athletes’ physiological parameters and optimizing training and recovery strategies. Recent developments focus on biosensor devices capable of monitoring biochemical parameters in addition to physiological ones. These devices employ noninvasive methods such as sweat analysis, which reveals critical biomarkers like glucose, lactate, electrolytes, pH, and cortisol. These biomarkers provide valuable insights into an athlete’s energy use, hydration status, muscle function, and stress levels. Current technologies utilize both electrochemical and colorimetric methods for sweat analysis, with electrochemical methods providing higher precision despite potential signal interference. Wearable devices such as epidermal patches, temporary tattoos, and fabric-based sensors are preferred for their flexibility and unobtrusive nature compared to more rigid conventional wearables. Such devices leverage advanced materials and transmit real-time data to computers, tablets, or smartphones. These data would aid coaches and sports medical personnel in monitoring athletes’ health, optimizing diets, and developing training plans to enhance performance and reduce injuries.

The use of embedded boards on robots, including unmanned aerial and ground vehicles, is increasing thanks to the availability of GPU equipped low-cost embedded boards in the market. Porting algorithms originally designed for desktop CPUs on those boards is not straightforward due to hardware limitations. In this paper, we present how we modified and customized the open source SLAM algorithm ORB-SLAM2 to run in real-time on the NVIDIA Jetson TX2. We adopted a data flow paradigm to process the images, obtaining an efficient CPU/GPU load distribution that results in a processing speed of about 30 frames per second. Quantitative experimental results on four different sequences of the KITTI datasets demonstrate the effectiveness of the proposed approach. The source code of our data flow ORB-SLAM2 algorithm is publicly available on GitHub.

Axial postural abnormalities (aPA) are common features of Parkinson’s disease (PD) and manifest in over 20% of patients during the course of the disease. aPA form a spectrum of functional trunk misalignment, ranging from a typical Parkinsonian stooped posture to progressively greater degrees of spine deviation. Current research has not yet led to a sufficient understanding of pathophysiology and management of aPA in PD, partially due to lack of agreement on validated, user-friendly, automatic tools for measuring and analysing the differences in the degree of aPA, according to patients’ therapeutic conditions and tasks. In this context, human pose estimation (HPE) software based on deep learning could be a valid support as it automatically extrapolates spatial coordinates of the human skeleton keypoints from images or videos. Nevertheless, standard HPE platforms have two limitations that prevent their adoption in such a clinical practice. First, standard HPE keypoints are inconsistent with the keypoints needed to assess aPA (degrees and fulcrum). Second, aPA assessment either requires advanced RGB-D sensors or, when based on the processing of RGB images, they are most likely sensitive to the adopted camera and to the scene (e.g., sensor–subject distance, lighting, background–subject clothing contrast). This article presents a software that augments the human skeleton extrapolated by state-of-the-art HPE software from RGB pictures with exact bone points for posture evaluation through computer vision post-processing primitives. This article shows the software robustness and accuracy on the processing of 76 RGB images with different resolutions and sensor–subject distances from 55 PD patients with different degrees of anterior and lateral trunk flexion.

The revolutionary technologies behind Industry 4.0 have opened a new era for manufacturing: connected and autonomous machines, collaborative robotics, and monitoring techniques are spreading to increase productivity and sustainability. From the workers’ perspective, they bring new safety threats but also opportunities to solve old ones, while concerns about workers’ privacy arise due to the increase of data sensed and transferred from the shop floor. This paper presents the results of a research project addressing the prediction of dangerous conditions through workplace monitoring with privacy guarantees. This work is driven by a realistic approach starting from the fact that it is entirely centered on a real 14-meter production line equipped with an extensive array of top-tier devices, including robotic arms, autonomous mobile robots, a reconfigurable moving belt, a multi-camera system, and a highly efficient data transport and computation infrastructure. This project shows safety and privacy achievements over six representative use cases such as man-on-the-ground, environmental events (e.g., fire incidents), workers’ errors that can lead to potential accidents, compliance of Personal Protective Equipment (PPE), and gatherings restrictions. The benefits of this study extend to stakeholders such as manufacturers and workers offering safety systems that can be deployed in industrial settings while addressing privacy concerns and providing compliance with regulations. The industrial laboratory at the heart of this study represents with realism a dynamic and interconnected Industry 4.0 and 5.0 environment.

Background: Software‐based measurements of axial postural abnormalities in Parkinson's disease (PD) are the gold standard but may be time‐consuming and not always feasible in clinical practice. An automatic and reliable software to accurately obtain real‐time spine flexion angles according to the recently proposed consensus‐based criteria would be a useful tool for both research and clinical practice.Objective: We aimed to develop and validate a new software based on Deep Neural Networks to perform automatic measures of PD axial postural abnormalities.Methods: A total of 76 pictures from 55 PD patients with different degrees of anterior and lateral trunk flexion were used for the development and pilot validation of a new software called AutoPosturePD (APP); postural abnormalities were measured in lateral and posterior view using the freeware NeuroPostureApp (gold standard) and compared with the automatic measurement provided by the APP. Sensitivity and specificity for the diagnosis of camptocormia and Pisa syndrome were assessed.Results: We found an excellent agreement between the new APP and the gold standard for lateral trunk flexion (intraclass correlation coefficient [ICC] 0.960, IC95% 0.913–0.982, P < 0.001), anterior trunk flexion with thoracic fulcrum (ICC 0.929, IC95% 0.846–0.968, P < 0.001) and anterior trunk flexion with lumbar fulcrum (ICC 0.991, IC95% 0.962–0.997, P < 0.001). Sensitivity and specificity were 100% and 100% for detecting Pisa syndrome, 100% and 95.5% for camptocormia with thoracic fulcrum, 100% and 80.9% for camptocormia with lumbar fulcrum.Conclusions: AutoPosturePD is a valid tool for spine flexion measurement in PD, accurately supporting the diagnosis of Pisa syndrome and camptocormia.