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    Sensori Model Presented for Health from Wrist Movement

    AI researcher shares new self-supervised model trained on wrist accelerometer data.

    TM
    2 Sources, 29d ago, first seen 29d ago

    TLDR

    Tanishq Mathew Abraham posted about an arXiv paper titled Learning Human Health and Diseases from 24-hour Wrist Movement. The post describes Sensori, a self-supervised foundation model that learns general-purpose health representations from 24 hours of raw tri-axial wrist movement. The work draws on a dataset of 122,640 participants contributing 683,617 person-days of free-living recordings. Abraham, listed as founder and PhD holder, is CEO of SophontAI and posted the update with an attached screenshot of the paper.

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    2 Sources, first seen 29d ago

    Combined views

    7.5K

    2 Sources, first seen 29d ago

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    6 comments
    15 saves
    5 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    2 Sources

    @iScienceLuvrLearning Human Health and Diseases from 24-hour Wrist Movement "Here, we present Sensori, a self-supervised foundation model that learns general-purpose health representations directly from 24 hours of raw tri-axial wrist movement." Dataset: 122,640 participants contributing 683,617 person-days of free-living recordings. Architecture: Sensori uses a multiscale architecture in which pooling operations progressively reduce the temporal resolution. Training: Sensori is pretrained using two complementary objectives designed to capture movement patterns at different temporal scales: masked reconstruction and day-level contrastive learning. Results: adding Sensori embeddings to the clinical covariate model significantly improved AUROC for 52 of 102 eligible conditions across the six disease categories, with the largest gains for neurological and psychiatric disorders. paper link: https://arxiv.org/abs/2608.29494

    2 Sources

    @iScienceLuvrLearning Human Health and Diseases from 24-hour Wrist Movement "Here, we present Sensori, a self-supervised foundation model that learns general-purpose health representations directly from 24 hours of raw tri-axial wrist movement." Dataset: 122,640 participants contributing 683,617 person-days of free-living recordings. Architecture: Sensori uses a multiscale architecture in which pooling operations progressively reduce the temporal resolution. Training: Sensori is pretrained using two complementary objectives designed to capture movement patterns at different temporal scales: masked reconstruction and day-level contrastive learning. Results: adding Sensori embeddings to the clinical covariate model significantly improved AUROC for 52 of 102 eligible conditions across the six disease categories, with the largest gains for neurological and psychiatric disorders. paper link: https://arxiv.org/abs/2608.29494