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Recent Submissions

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    Analysis and Evaluation of Non-Blocking Interpolation Search Trees
    (ACM, 2020-01-02) Prokopec, Aleksandar; Brown, Trevor; Alistarh, Dan
    We start by summarizing the recently proposed implementation of the first non-blocking concurrent interpolation search tree (C-IST) data structure. We then analyze the individual operations of the C-IST, and show that they are correct and linearizable. We furthermore show that lookup (and several other non-destructive operations) are wait-free, and that the insert and delete operations are lock-free. We continue by showing that the C-IST has the following properties. For arbitrary key distributions, this data structure ensures worst-case O(log n+p) amortized time for search, insertion and deletion traversals. When the input key distributions are smooth, lookups run in expected O(log log n+p) time, and insertion and deletion run in expected amortized O(log log n+p) time, where p is a bound on the number of threads. Finally, we present an extended experimental evaluation of the non-blocking IST performance.
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    Estimating human poses, muscle activations, and ground reaction forces from monocular video
    (University of Waterloo, 2026-10-30) Zhu, Kevin
    Human pose estimation (HPE) generally refers to extracting human kinematics from various inputs, such as videos, inertial measurement units, and marker-based motion cap- ture. This thesis pertains to monocular HPE, where the input source is a single-view video. From an application standpoint, this overcomes many physical and financial limi- tations that other modalities involving wearable sensors or expensive hardware may pose. For example, in sports, monocular HPE allows the user to analyze athletes outside a lab, from opposing teams, or in the past, while being more accessible to amateur and para athletes, or anyone with a smartphone. In this thesis, we focus on physics-based HPE (PHPE), where we also estimate kinetics. Monocular PHPE is a challenging task, as predicting 3D dynamics from a single camera view is an under-constrained problem. To address this, we developed MusclePose as the first pose estimator to incorporate muscle dynamics modeling, via muscle torque generators, for regularization. We showed improvements in biofidelity over state-of-the-art (SOTA) regression-based PHPE methods, with more realistic force and torque predictions. However, regression-based methods, including MusclePose, can have performance drops when applied to new domains. To this end, we developed SPUD as a diffusion-based optimization framework for PHPE, with two novel diffusion priors for kinetics and bone estimation. We showed improved adaptability over SOTA methods via experiments on two disparate sports — fencing and golf. SPUD is also the first PHPE method to incorporate the more anatomically accurate SKEL human model over SMPL’s kinematic skeleton, allowing for better compatibility with common biomechanics standards. Lastly, to overcome the lack of force-annotated video datasets for athletic human move- ments, we collected a motion capture dataset for fencing. This dataset includes over 13 thousand video frames of beginner and national level fencers performing 13 different fenc- ing techniques, synchronized with Vicon markers and force plates. We hope this new evaluation dataset can advance the current state of PHPE research.
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    Decision-Support and Optimization Framework for Selection of Low-Cost Wearable Devices to Improve the Health and Safety of Mechanical-Trade Workers
    (University of Waterloo, 2026-10-30) Attalla, Omar
    The construction industry is a major contributor to the Canadian and U.S. economies. However, it continues to face persistent challenges related to worker attrition, productivity loss, and cost inefficiency, driven in large part by the physically demanding nature of the work. Among the injuries that burden this workforce, work-related musculoskeletal disorders (WMSDs) represent one of the largest single categories of nonfatal lost-time injury, consistently occurring at rates higher than the all-industry average. This burden is especially severe in the mechanical trades, including plumbers, pipefitters, welders, refrigeration technicians, and HVAC mechanics, whose work combines overhead installation, kneeling and crouching in confined spaces, manual material handling, and prolonged awkward postures within a single workday. U.S. data show that mechanical trades such as HVAC mechanics and sheet metal workers experience WMSD incidence rates up to 2.5 times the construction average. Canadian workers' compensation data further identify overexertion and repetitive motion as the leading contributors to lost workdays and claim costs in the mechanical trades, with the WMSD-related burden increasing over the past decade. In British Columbia alone, WMSD-related injuries in the mechanical trades accounted for approximately 69,800 lost workdays (39% of the trades’ total) and $39.5 million in claim costs (28%) from 2020 to 2024. WMSD risks vary by task, affected body region, and worker-specific anthropometric, demographic, and health- related characteristics, indicating a clear need for targeted, cost-effective ergonomic interventions matched to each worker and the body regions requiring support. While wearable support devices, including exoskeletons and exosuits, can mitigate physical loading, full-body systems may provide broader support than a given worker requires, introducing unnecessary costs and adoption barriers related to bulk, motion constraints, and donning burden. To address this gap, this research investigates the physical demands of mechanical-trade work, which is among the most physically taxing in construction, to understand mechanical-trade workers’ exposure to WMSD risk factors and the body regions most affected. Building on this understanding, it develops a multi-criteria decision-support framework that recommends worker-specific combinations of low-cost, lightweight wearable support devices to reduce exposure to WMSD risk factors and enhance worker health, safety, and productivity (HS&P) in the mechanical trades. The objectives of this research are fourfold: (1) to identify industry needs, at-risk body regions, and working postures in mechanical-trade work; (2) to evaluate and classify low-cost wearable support devices; (3) to develop and validate a system that recommends worker-specific device combinations; and (4) to establish the conditions under which the framework applies to other trades. First, it identifies industry needs through a field survey of 13 owner organizations, examines worker injury trends to establish the back, legs, and shoulders as the at-risk body regions, and derives the working postures that load them. Second, it experimentally evaluates commercially available wearable support devices in laboratory and field settings by measuring various performance metrics to classify each device by the level of support (LOS) it provides. Third, it develops a decision support system (DSS) that uses machine learning to predict each worker's required LOS across the three regions from anthropometric, demographic, and health-related data, and then selects, in a single optimization spanning all at-risk regions, the combination of support devices that best matches those predicted requirements under a single budget constraint. The DSS is subsequently validated through field implementation involving real-world mechanical-trades work. Experiments with 44 participants, including 13 practitioners, showed that all 12 devices received the same LOS classification in the laboratory and the field (mean absolute percentage difference of 4.83% in the LOS index). The machine learning models predicted required LOS with 10-fold cross-validated accuracies of 77.8%, 78.4%, and 79.0% for the back, legs, and shoulders, and the recommended devices were generally well accepted by three additional practitioners in the field validation. Fourth, the applicability of the framework beyond the mechanical trades is established. The worker injury and working-posture analyses are repeated for four further construction trades, demonstrating that the framework’s targeted body regions and the postures under which its devices were evaluated recur beyond the trade group for which it was developed. These findings define the conditions under which the framework applies, with application to a trade that meets those conditions requiring repetition of the industry data collection component alone. The proposed framework offers a structured, systematic approach for matching individual mechanical-trade workers to the most suitable combination of wearable support devices for their specific needs. Because all at-risk body regions are optimized together rather than independently, the recommended set is tailored to the individual worker as a whole. The framework’s significance is twofold: for research, it places device support and worker need on a common scale, the LOS index; for practice, it provides occupational health or procurement staff with a DSS that can be operated without programming expertise. By providing objective, data-driven, and worker-tailored recommendations, this research has the potential to reduce the substantial lost-time and compensation-cost burden of WMSDs in the mechanical trades while supporting the retention of an experienced and difficult-to-replace skilled workforce.
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    Exploring social and physical contextual factors on adolescent’s physical activity: An ecological momentary assessment study
    (American Psychological Association, 2026) Harris, Sheereen; Yang, Chih-Hsiang; Bourke, Matthew; Kwan, Matthew
    Physical activity (PA) peaks during early adolescence followed by sharp declines. Understanding factors related to PA during adolescence will help inform strategies to attenuate such declines. We explored effects of social and physical contexts on moderate-to-vigorous intensity PA (MVPA) among adolescents using ecological momentary assessment (EMA). In Fall 2019, participants (N=190 adolescents; Mage = 15.8±0.5 years; 83 female; 64% White) from Southern Ontario, Canada received four EMA surveys daily after-school while wearing an accelerometer for one week. Time spent in MVPA 60-mins following each EMA prompt was calculated. A mixed-effects location scale model estimated each person’s mean MVPA and MVPA variance at between-person and within-person levels. Results showed a statistically significant within-person interaction between social and physical context such that the fewest MVPA minutes occurred after participants reported being alone and at home, whereas the highest amount of MVPA occurred after reports of being away from home and alone. There was a statistically significant within-person effect of physical context on within-person variance suggesting more consistent patterns of activity when participants reported being at home relative to when they were away from home. Gender was a statistically significant covariate in the mean and within-person models. This study aligns with prior literature examining socio-environmental factors related to PA during adolescence and is among the first to examine associations between contextual factors and variability in MVPA.
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    Blind to bias? Young children do not anticipate that sunk costs lead to irrational choices
    (Wiley Online Library, 2021) Sehl, Claudia G.; Denison, Stephanie; Friedman, Ori
    Young children anticipate that others act rationally in light of their beliefs and desires, and environmental constraints. However, little is known about whether children anticipate others’ irrational choices. We investigated young children’s ability to predict that sunk costs can lead to irrational choices. Across four experiments, 5- to 6-year-olds (total N = 185) and adults (total N = 117) judged which of two identical objects an agent would keep, one obtained at a high cost or one obtained at a low cost. In Experiment 1, adults predicted that the agent would choose the high-cost object over the low-cost one, whereas children responded at chance. Experiment 2 replicated these findings in children, but also included another condition which showed they were sensitive to future costs. They predicted that an agent would be more likely to seek out a low-cost item than a high-cost item. Experiments 3 and 4then found that children do not anticipate the sunk cost bias in first person scenarios, or in interpersonal sunk cost scenarios, where costs are sunk by others. Taken together, our findings suggest that young children may struggle to understand and predict irrational behavior. The findings also reveal an asymmetry between how they consider sunk costs and future costs in understanding actions. We proposethat this asymmetry might arise because children do not consider sunk costs as wasted.