OXIG.L
Buy. Our thesis: improvements in ML that predict dynamics from kinematics make clinical-grade gait analytics cheaper and more scalable, potentially benefiting companies closest to motion-capture and wearable ecosystems.
Recent proof-backed thesis calls
One active call: a buy thesis anchored to research (Gait2Hip-60) showing sequence models can predict hip muscle forces and joint moments from multi-cadence gait kinematics. The paper implies automation of biomechanics metrics from cheaper kinematics inputs (wearables, markerless systems) could broaden commercial adoption.
Scientific paper proposes a unified benchmark (60 healthy subjects, 3 cadences) to predict hip muscle forces and joint moments directly from gait kinematics using sequence models; Transformer performed best and showed only moderate zero-shot generalization to a small external pathological cohort (9 ONFH patients). Investable implication is not the specific model, but acceleration/automation of gait analytics and biomechanics-derived metrics from cheaper kinematics inputs (wearables/markerless ca
Current stance
Current recommendation: buy. Rationale: beneficiary exposure to kinematics-to-dynamics ML, which may expand the addressable market for gait analytics and shift value toward integrated motion-capture/wearable ecosystems.
- beneficiary via Kinematics-to-dynamics ML can expand the addressable market for gait analytics, shifting value to integrated motion-capture/wearable ecosystems. from https://rss.arxiv.org/rss/cs.LG (confidence 0.46)
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Active and historical ticker theses
Active play: 'Gait2Hip-60' — a unified deep-learning benchmark demonstrating that sequence models (Transformer performed best) can predict hip muscle forces and joint moments from gait kinematics across three cadences and 60 healthy subjects. The study showed only moderate zero-shot generalization to a small external pathological cohort (9 ONFH patients), highlighting opportunity in improving robustness for broader clinical deployment.
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