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.

No abstract

No abstract

No abstract

Absfrucf-Conventional trackers are point trackers. Tracking energy on a field of sensor cells requires windowing, thresholding, and interpolating to arrive at data points to feed the tracker. This scheme poses problems when tracking energy that is distributed across many cells. Such signals are sometimes termed "over-resolved.'' It has been suggested that tracking could be improved by decreasing the resolution of the signal processor, so that the cells are large enough to encompass the bulk of the energy, and better match the point tracker assumptions. Larger arrays provide greater resolution at lower frequencies, with the potential for improved detection and classification performance, but in direct conflict with tracking "over-resolved" signals. These issues are addressed by the histogram-based probabilistic multi-hypothesis tracking (PMHT) method discussed in this paper, which provides a means for modeling and tracking signals that may be spread across many sensor cells. This paper will focus on the initial development and testing of this algorithm for one-dimensional sensor data. Elements of the signal model, theory, and algorithm will be presented along with two frequency domain examples.