UNB StepUP: A footStep database for gait analysis and recognition using Underfoot Pressure

Gait refers to the patterns of limb movement generated during walking, which are unique to each individual due to both physical and behavioural traits. Walking patterns have been widely studied in biometrics, biomechanics, sports, and rehabilitation. While traditional methods rely on video and motion capture, advances in underfoot pressure sensing technology now offer deeper insights into gait. However, underfoot pressures during walking remain underexplored due to the lack of large, publicly accessible datasets. To address this, the UNB StepUP database was created, featuring gait pressure data collected with high-resolution pressure sensing tiles (4 sensors/cm$^2$, 1.2m by 3.6m). Its first release, UNB StepUP-P150, includes over 200,000 footsteps from 150 individuals across various walking speeds (preferred, slow-to-stop, fast, and slow) and footwear types (barefoot, standard shoes, and two personal shoes). As the largest and most comprehensive dataset of its kind, it supports biometric gait recognition while presenting new research opportunities in biomechanics and deep learning. The UNB StepUP-P150 dataset sets a new benchmark for pressure-based gait analysis and recognition. Please note that the hypertext links to the dataset on FigShare remain dormant while the document is under review.

Computational Pathology: A Survey Review and The Way Forward

Computational Pathology (CoPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CoPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology facilitating transformational changes in the diagnosis and treatment of cancer diseases. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CoPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CoPath. In this article we provide a comprehensive review of more than 700 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CoPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CoPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CoPath.