Advantage Forensics (Canada)

Body-worn cameras document crime scenes during initial law enforcement response, yet their potential for forensic reconstruction has not been empirically validated. Despite expanding global adoption, recorded video primarily serves qualitative documentation rather than quantitative measurement applications. This study empirically evaluated three-dimensional (3D) reconstruction accuracy from body-worn camera video to assess its feasibility for feature measurement. Three Axon camera models-Body 2 (AB2), Flex 2 (AF2), and Body 3 (AB3)-were tested in an outdoor parking lot, with each model recording five videos at both 720P and 1080P resolutions (n = 30). Videos were recorded under controlled experimental conditions to achieve optimized documentation scenarios. Videos were processed using 3DF Zephyr photogrammetry software to create 3D reconstructions, then compared against Faro Focus S350 laser scanner ground truth at three distances: long (12.48 m), medium (2.42 m), and short (0.24 m). One-sample t-tests revealed significant differences between AF2 measurements and ground truth (p < 0.05), with a maximum mean error of 14.42 cm at 720P for long distances. AB2 and AB3 showed no significant differences from the ground truth at both resolutions across all validation distances (p ≥ 0.05). Two-sample t-tests demonstrated no significant differences between resolutions (p ≥ 0.05). Single-factor ANOVAs indicated significant differences between camera models (p < 0.05). Resolution did not affect measurement accuracy under the conditions of the controlled methodology and internal software interpolation. These best-case results demonstrate that with deliberate documentation protocols, accurate 3D reconstruction from body-worn camera video is achievable for forensic applications.

BackgroundRetirement homes are an essential support for many older adults as they age. However, retirement homes may not be an option for all, particularly adults with low socioeconomic status or those from linguistic or visible minority groups, in jurisdictions where there are high out-of-pocket costs and limited availability of language- and culturally-concordant home options. Research examining health inequities in facility-based care for older adults has largely focused on the long-term care sector leaving a gap in knowledge and understanding about the care experiences and health outcomes of retirement home residents. The overall aim of this project is to build an evidence base and provide policy recommendations to improve equity in the retirement home sector in Ontario, Canada.MethodsWe will conduct a multi-phase sequential mixed methods study involving both quantitative and qualitative data. Phase 1 will involve consulting on social and systemic barriers to equity in RHs with our Community Advisory Committee members and advocacy group collaborators. In phase 2, we will lead retrospective cohort studies using Ontario health administrative data to describe and evaluate differences in rates of RH vs. LTC use and differences in healthcare outcomes. In Phase 3, we will conduct semi-structured interviews with older adults and their family partners preparing to enter or having already entered, facility-based care within the prior 6 months to investigate how individuals’ social identity, including language and income, and pragmatic considerations shape decision-making regarding the transition to facility-based care, including choices between retirement homes and long-term care. Findings from Phases 1,2 and 3 will be contextualized in Phase 4 with decision-maker knowledge users, such as from the Retirement Home Regulatory Authority and Ontario Ministries of Health, Long-term Care, and Seniors and Accessibility.DiscussionThis project will leverage the existing momentum for long-term care reform where evidence of health inequities is well documented, to engage in research in a neighbouring sector that serves older adults with increasingly similar health profiles and complexities. We will disseminate contextualized policy options to both provincial-level decision-makers and community knowledge user partners to help inform future work on equity in retirement homes at different levels of governance.

This chapter explores the complex intersection of AI and academic ethics through a multidisciplinary lens, incorporating perspectives from law, international relations, philosophy of science, computer science, and the social sciences. It outlines how AI is reshaping every stage of the research continuum—from hypothesis generation to scholarly dissemination—while considering the broader socio-political context of these changes. The discussion highlights central ethical issues, including authorship, algorithmic opacity, bias, privacy, and unequal distribution of resources, and reviews evolving governance frameworks implemented by leading academic institutions. Despite adopting a level of scholarly rigor suitable for an interdisciplinary audience, this text emphasizes clarity and engagement, weaving empirical examples and case studies—such as the rapid rollout of AI tools during the COVID-19 pandemic—into its analysis.

The opioid crisis is a significant public health issue in North America that began in the 1990s with opioid-related deaths increasing each year. Although studies have been published regarding the incidence of opioid-related deaths in the general population, there is limited information regarding deaths in the prison population. To investigate the impact of the opioid epidemic in this vulnerable population, a retrospective study of all drug-related deaths that occurred in correctional facilities and penitentiaries in Ontario, Canada, between January 2009 and December 2019 was conducted. A total of 90 deaths were attributed to acute drug toxicity and the decedents ranged in age from 18 to 63 years and comprised 81 men and 9 women. The results of this study indicate the number of drug-related fatalities have increased by 375% (from 4 to 19) over the last 11 years. The detection of opioids in drug-related deaths has increased with fentanyl being the most frequently detected drug. Data also indicates the recent emergence of fentanyl-related analogues in this population. The results from this study provide valuable information about drug-related deaths in the Ontario prison system and provide insight into how the opioid crisis and the increased use of fentanyl and its analogues have affected this vulnerable population.

Summary This journal (Drone Systems and Applications; DSA) conducted a targeted “horizon scan” during 2022 within our team of editors and associate editors. We asked— Which research areas currently under-represented in Drone Systems and Applications would you like to see more heavily represented in the future? The process highlighted five areas of interest and potential growth: Drones in the geosciences Aquatic drones Ground drones Drones within calibration/validation experiments Drones and computer vision Over the past two years (2020–22), the journal has published over 50 papers with a strong leaning towards aerial drones for ecology and also with an engineering focus. DSA is keen to receive new submissions addressing the five highlighted areas, which lie firmly within the aims and scope of the journal. Further to the horizon scan, we propose two special collections for the coming year—one addressing drone applications ( drones in geoscience applications) and a second addressing drone systems ( aquatic drone systems). We would like to hear from scientists and practitioners in these fields as both contributors and (or) collection editors.

Wearable devices are increasingly used to measure real-world head impacts and study brain injury mechanisms. These devices must undergo validation testing to ensure they provide reliable and accurate information for head impact sensing, and controlled laboratory testing should be the first step of validation. Past validation studies have applied varying methodologies, and some devices have been deployed for on-field use without validation. This paper presents best practices recommendations for validating wearable head kinematic devices in the laboratory, with the goal of standardizing validation test methods and data reporting. Key considerations, recommended approaches, and specific considerations were developed for four main aspects of laboratory validation, including surrogate selection, test conditions, data collection, and data analysis. Recommendations were generated by a group with expertise in head kinematic sensing and laboratory validation methods and reviewed by a larger group to achieve consensus on best practices. We recommend that these best practices are followed by manufacturers, users, and reviewers to conduct and/or review laboratory validation of wearable devices, which is a minimum initial step prior to on-field validation and deployment. We anticipate that the best practices recommendations will lead to more rigorous validation of wearable head kinematic devices and higher accuracy in head impact data, which can subsequently advance brain injury research and management.