Matthias Jammot

Understanding affect is central to anticipating human behavior, yet current egocentric vision benchmarks largely ignore the person’s emotional states that shape their decisions and actions. Existing tasks in egocentric perception focus on physical activities, hand-object interactions, and attention modeling—assuming neutral affect and uniform personality. This limits the ability of vision systems to capture key internal drivers of behavior. In this paper, we present egoEMOTION, the first dataset that couples egocentric visual and physiological signals with dense self-reports of emotion and personality across controlled and real-world scenarios. Our dataset includes over 50 hours of recordings from 43 participants, captured using Meta’s Project Aria glasses. Each session provides synchronized eye-tracking video, head-mounted photoplethysmography, inertial motion data, and physiological baselines for reference. Participants completed emotion-elicitation tasks and naturalistic activities while self-reporting their affective state using the Circumplex Model and Mikels’ Wheel as well as their personality via the Big Five model. We define three benchmark tasks: (1) continuous affect classification (valence, arousal, dominance); (2) discrete emotion classification; and (3) trait-level personality inference. We show that a classical learning-based method, as a simple baseline in real-world affect prediction, produces better estimates from signals captured on egocentric vision systems than processing physiological signals. Our dataset establishes emotion and personality as core dimensions in egocentric perception and opens new directions in affect-driven modeling of behavior, intent, and interaction.

Cable-driven exosuits assist users in ambulatory activities by transmitting assistive torques from motors to the actuated joints. State-of-the-art exosuits typically use Bowden cable transmissions, albeit their limited efficiencies (40–60%) and non-linear response in curved paths. This paper evaluates the efficiency and responsiveness of a new cable-pulley transmission compared to a Bowden transmission, using both steel and Dyneema cables. The analysis includes three experiments: a test bench simulating a curved transmission path, followed by a static and dynamic experiment where six unimpaired participants donned an exosuit featuring both transmissions across the hips and knees. Our findings demonstrate that the pulley transmission consistently outperformed the Bowden’s efficiency by absolute margins of 18.77 ± 7.29% using a steel cable and by 40.60 ± 6.76% using a Dyneema cable across all experiments. Additionally, the steel cable was on average 19.19 ± 5.29% more efficient than the Dyneema cable in the pulley transmission and 41.02 ± 6.34% in the Bowden tube. These results led to the development of the Stillsuit, a novel lower-limb cable-driven exosuit that uses a pulley transmission and steel cable. The Stillsuit sets a new benchmark for exosuits with 87.56 ± 3.92% transmission efficiency, generating similar biological torques to those found in literature (16.4% and 19.0% of the biological knee and hip torques, respectively) while using smaller motors, resulting in a lighter actuation unit (1.92 kg).