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pith:QVFEHGXI

pith:2026:QVFEHGXIQ4KISDNLF4JXSGQLEB

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Uncertainty-Driven Anomaly Detection for Psychotic Relapse Using Smartwatches: Forecasting and Multi-Task Learning Fusion

Niki Efthymiou, Nikolaos Tsalkitzis, Panagiotis P.Filntisis, Petros Maragos

Late fusion of cardiac forecasting and multi-task sleep-motion models on smartwatches detects psychotic relapse with an 8% improvement over the winning baseline.

arxiv:2605.13816 v1 · 2026-05-13 · cs.LG

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Claims

C1strongest claim

our fused model achieves a 8% relative improvement over the competition-winning baseline... the integration of diverse digital phenotypes, cardiac, motion, and sleep, is essential for the high-fidelity detection of psychotic relapse in real-world settings.

C2weakest assumption

That deviations flagged as anomalies by the uncertainty-driven scores correspond to actual clinical psychotic relapse events rather than other sources of wearable noise or behavioral change.

C3one line summary

Fusing cardiac forecasting with multi-task sleep-motion learning on smartwatch data produces an 8% better psychotic relapse detector than the prior competition winner.

References

19 extracted · 19 resolved · 0 Pith anchors

[1] Opportunities and challenges in the collection and anal- ysis of digital phenotyping data, 2021

[2] Digital phenotyping: a global tool for psychiatry, 2018

[3] Automatic recognition of schizophrenia from facial videos using 3D convolutional neural net- work,

[4] Predicting early warning signs of psychotic relapse from passive sensing data: an approach using encoder-decoder neural networks, 2020

[5] Smartphone Health Assessment for Relapse Prevention (SHARP): a digital solution toward global mental health, 2021

Receipt and verification

First computed	2026-05-18T02:44:15.321966Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

854a439ae88714890dab2f13791a0b207dfde08937dcce976ab55be8610b7505

Aliases

arxiv: 2605.13816 · arxiv_version: 2605.13816v1 · doi: 10.48550/arxiv.2605.13816 · pith_short_12: QVFEHGXIQ4KI · pith_short_16: QVFEHGXIQ4KISDNL · pith_short_8: QVFEHGXI

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/QVFEHGXIQ4KISDNLF4JXSGQLEB \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 854a439ae88714890dab2f13791a0b207dfde08937dcce976ab55be8610b7505

Canonical record JSON

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    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T17:43:07Z",
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