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Healthcare Robotics and Therapy

A woman on a dementia ward becomes restless in the late afternoon. A device on her bedside table plays something familiar: regional songs from her youth. She starts to sing along, fragmentary. The device follows: adjusting tempo, holding the harmonic thread when she loses it, softening when she goes quiet. It is not playing a recording. It is accompanying her, in real time, through a moment that will not repeat the same way tomorrow.

The clinical case is established. The German S3 Demenz guideline (DGPPN/DGN, 2023) names music therapy explicitly as a first-line non-pharmacological intervention for agitation in dementia; the UK's NICE NG97 recommends psychosocial and environmental interventions for distress before antipsychotic medication. The constraint is not whether the approach works; it is staffing. Germany has roughly 1.8 million people living with dementia, with a projected nursing shortfall of around 500,000 positions by 2030, and only 12.3% of music therapists globally work in geriatric settings. What helps through the difficult hours is often simple. Time is what a short-staffed ward does not have.

The broader argument for why this market (adaptive sound, not song generation) is the consequential one is in the journal piece The Wrong Debate. This page covers the clinical specifics.

Why cultural specificity is clinical, not aesthetic​

The right music for a patient is shaped by the era, region, language, and household repertoire they grew up in. Generic Western-commercial playlists cover a vanishing fraction of that surface for anyone outside a narrow demographic. A device that has to find familiar material for a 1950s Bavarian patient, a Tamil grandmother in Düsseldorf, or a retired Mexican farmworker on a Texas ward is querying a corpus along dimensions commercial catalogs were never indexed for.

CORPUS's diversity bonus rewards underrepresented contributions; the semantic pipeline makes the resulting library queryable by region, era, tradition, instrumentation, ceremonial context. The patient is already agitated. There is no re-roll. The model has to land on the first try, which a generalist song generator on a prompt-discard-reprompt loop cannot.

Alarm fatigue​

Uniform alarm tones across devices, units, and urgency levels train clinical staff to filter the entire band out. A CORPUS-trained model can produce alarm cues that differentiate urgency through timbral and harmonic shape, coherent across a hospital's device estate, distinct enough per tier to stay legible after a long shift.

Why hospital procurement requires CORPUS-grade licensing​

  • Provenance under audit, not vendor attestation. EU AI Act Article 53(1)(d) makes a training-content summary an obligation for GPAI providers; clinical integrators inherit the burden.
  • Personality rights on vocals. Any patient-facing voiced or sung material rests on a corpus where the named singers consented; see Personality rights and vocal performance.
  • EU data residency. CORPUS infrastructure runs on self-administered servers in Germany under EU data protection law; see Data residency.

A model that cannot be defended under audit cannot be deployed where audit is a precondition for procurement. See Why CORPUS.