Motivation

Emotion shapes the way we move — the way we walk, rise from a chair, and initiate movement all carry subtle signatures of our internal state. Yet most emotion recognition research focuses on faces and voice, modalities that are easy to fake, difficult to capture passively, and impossible to use for people with facial or speech impairments. Gait and functional movement transitions like sit-to-walk are different: they're automatic, difficult to consciously suppress, and directly tied to the motor and postural control systems that mood disorders are known to affect. If we could reliably decode emotional state from movement, we'd have a low-burden, manipulation-resistant signal with real clinical potential — for monitoring mood disorders like bipolar disorder and depression, tracking rehabilitation progress, or flagging emotional dysregulation before it becomes clinically apparent. Beyond emotion recognition, these same biomechanical shifts have direct implications for fall prevention: emotions like fear and sadness alter dynamic balance in ways that matter for vulnerable populations.

Our Solution

We captured full-body 3D biomechanics across five emotional states: anger, sadness, joy, fear, and neutral. Induced through an autobiographical memory induction paradigm to elicit genuine emotional recall. Participants, wearing a reflective marker suit, performed walking and sit-to-walk trials under each condition while instrumented with tracked by a Vicon motion capture system and processed through Visual3D. From these recordings I helped extract over 150 biomechanical variables spanning spatiotemporal parameters and full-body joint kinematics.

My Contributions

I ran participants through the full walking and sit-to-walk protocol, applied the 74 reflective markers, operated the Vicon motion capture system, cleaned data in Vicon Nexus. For my first poster, I computed COM-to-ankle angles at heel strike, toe-off, and seat-off events in Visual3D, applied eligibility-based trial exclusion, and performed all statistical analysis.

For the emotion classification study (Stout et al., Gait & Posture 2025), I contributed to data collection, data curation, and feature extraction from the 155 biomechanical variables computed in Visual3D, and served as a writing reviewer and editor on the manuscript. For the depression and anxiety classification study (Stout et al., Gait & Posture 2026), I contributed to data collection and curation, and held a writing role spanning both original drafting and review. I also contributed to data collection for the IMU validation study (Alvarez et al., Sensors 2025), which confirmed that ankle-mounted IMUs can replicate emotion-linked spatiotemporal changes previously documented only with laboratory motion capture.