Person identification is a problem that has received substantial attention, particularly in security domains. Gait recognition is one of the most convenient approaches enabling person identification at a distance without the need for high-quality images. There are several review studies addressing person identification such as the utilization of facial images, silhouette images, and wearable sensor. Despite skeleton-based person identification gaining popularity while overcoming the challenges of traditional approaches, existing survey studies lack the comprehensive review of skeleton-based approaches to gait identification. We present a detailed review of the human pose estimation and gait analysis that makes the skeleton-based approaches possible. The study covers various types of related datasets, tools, methodologies, and evaluation metrics with associated challenges, limitations, and application domains. Detailed comparisons are presented for each of these aspects with recommendations for potential research and alternatives. A common trend throughout this paper is the positive impact that deep learning techniques are beginning to have on topics such as human pose estimation and gait identification. The survey outcomes might be useful for the related research community and other stakeholders in terms of performance analysis of existing methodologies, potential research gaps, application domains, and possible contributions in the future.

Person identification is a key problem in the security domain and may be used to automatically identify criminals or missing persons. The traditional face matching approaches adopted by the police and security services across the world have recently been shown to produce a high rate of false positive identification. Alternatively, gait-based person identification has shown to be a convenient method particularly as it can be performed at a distance, without the cooperation of the subject, and is a biometric trait which cannot be easily disguised. In this work, we propose a gait-based person identification approach which uses limb joint motion data and deep machine learning models to identify the individuals. Distinct statistical features are identified and extracted from limb movement using a fixed width sliding window to train a Long Short-Term Memory model. The proposed solution outperforms the existing methods producing 98.87% accuracy when evaluated over unseen samples. In addition, we propose a simple two-stage filtering approach to increase the prediction accuracy up to 100% when identifying individuals from larger sequences of samples. This finding may improve the current solutions in controlled environments such as airports. In the future, this approach may help to overcome the problem of occlusion in gait-based identification, as unlike the existing works, it does not require information regarding the entire body. The study also presents a primary dataset comprising limb joint movement acquired from a diverse range of participants during casual walking captured through two digital goniometers.

Gait datasets are often limited by a lack of diversity in terms of the participants, appearance, viewing angle, environments, annotations, and availability. We present a primary gait dataset comprising 1,560 annotated casual walks from 64 participants, in both indoor and outdoor real-world environments. We used two digital cameras and a wearable digital goniometer to capture visual as well as motion signal gait-data respectively. Traditional methods of gait identification are often affected by the viewing angle and appearance of the participant therefore, this dataset mainly considers the diversity in various aspects (e.g., participants’ attributes, background variations, and view angles). The dataset is captured from 8 viewing angles in 45° increments along-with alternative appearances for each participant, for example, via a change of clothing. The dataset provides 3,120 videos, containing approximately 748,800 image frames with detailed annotations including approximately 56,160,000 bodily keypoint annotations, identifying 75 keypoints per video frame, and approximately 1,026,480 motion data points captured from a digital goniometer for three limb segments (thigh, upper arm, and head).

Person de-identification has become a challenging problem that is receiving substantial attention because of the growing demand for privacy protection and related regulations. In this context, computer vision and Deep Learning (DL) algorithms offer automated solutions for Face de-identification (FDeID), commonly used to conceal personal identities in visual data. The existing survey studies addressing the FDeID topic lack comprehensive coverage of modern generative DL-based FDeID methods, limitations of data resources, proposing new applications, and potential technical and ethical research directions, which are covered for the first time in this survey. Throughout the manuscript, we offer critical analysis from various perspectives with a recurring theme of the growing impact that generative deep learning techniques are beginning to have on FDeID and related areas such as gait de-identification. In addition, we suggest 17 novel research dimensions and corresponding research questions in both technical and dataset perspectives, which will advance the research frontiers in this domain. The insights presented in this survey can benefit the research community and diverse stakeholders such as law enforcement, healthcare, industry, etc. It offers valuable insights into the performance analysis of existing methodologies, identifies research gaps, highlights application domains, and suggests precise possible avenues for future contributions.

Aims: This study aims to examine how digital technologies can be safely and effectively integrated into clinical practice to enhance patient safety, with a particular focus on emergency department triage. Background: Patient safety remains a persistent challenge in high-pressure environments such as emergency care. The complexity of clinical workflows, cognitive demands on healthcare professionals, and system-level constraints often contribute to patient safety risks. While digital tools such as Clinical Decision Support Systems (CDSS) offer promise, their impact depends on how well they align with real-world decision-making processes. Methods: A Cognitive Task Analysis (CTA) was conducted with triage nurses in the emergency department (ED) of Malta’s main acute hospital. The study involved semi-structured interviews and direct observations to elicit the cognitive challenges, decision strategies, and contextual constraints experienced during triage. Findings were synthesised into a Cognitive Demands Table to identify sources of risk and variation in decision-making. Results: The CTA revealed key challenges affecting patient safety at triage, including cognitive overload, incomplete information, reliance on intuition, protocol deviations, communication gaps, and fatigue. These findings informed the development of a conceptual framework comprising six pillars essential for safe digital integration: governance and policy alignment, human-centred design, clinical validation, digital literacy, interoperability, and continuous monitoring. Conclusion: Digital technologies have the potential to significantly improve patient safety, but their effectiveness depends on thoughtful integration into clinical environments. This study highlights the importance of designing digital systems that are context-aware, ethically governed, and co-developed with end users. The proposed framework offers practical guidance for healthcare leaders, developers, and policymakers seeking to embed safety into the digital transformation of care.