Huma - Samsung Partnership for Clinical Biosensor Data | Huma
Clinical-grade wearable evidence
A partnership between Huma - Samsung gives sponsors protocol-level control over raw biosensor data from Galaxy Watch and Galaxy Ring. The same signal foundation runs from early-phase trials through to real-world evidence.
Huma Alcedis
Theblackboxproblem
In most trials, wearables return pre-processed scores with little visibility into the underlying signal. Protocol design ends up adapting to what the device exposes, rather than what the science requires.
AccesstoSamsung'sResearchPlatform
Huma Alcedis holds access to Samsung's Research Platform. Configure every sensor on Galaxy Watch around your research question, defining what is measured, when, and for which participants.
Beyonddeviceprovision
An end-to-end evidence-generation ecosystem, not just hardware. One coordinated team across design, data, operations, and analytics.
Study Design
Endpoint strategy, digital biomarker design, and protocol alignment built around your scientific objective from day one.
Data Infrastructure
Protocol-driven sensor configuration, raw and secure data acquisition, and harmonisation with your existing clinical systems.
Operations
Device logistics, site and participant training, compliance monitoring, and technical support across every study site.
Analytics
Digital biomarker analytics and algorithm co-development, turning raw biosensor signal into validated, decision-ready endpoints.
Sharper,earlierevidence
Higher sensitivity
Continuous biosensor data raises endpoint sensitivity, surfacing treatment effects that intermittent, in-clinic measurements often miss.
Faster read-outs
Objective, continuous signal shortens the time needed to assess treatment response, so trials read out sooner.
Subtle disease changes
Detect small, gradual shifts in disease state that scheduled, conventional assessments are too coarse and infrequent to capture.
Objective measures
Generate objective functional data alongside patient-reported outcomes, pairing how patients feel with how they actually move and function.
Hard-to-measure symptoms
Quantify symptoms that are hard to assess in a clinic visit, across sleep, activity, mobility, and heart rhythm.
Earlier safety signals
Continuous monitoring surfaces safety signals earlier in the study, supporting faster and better-informed decisions during the trial.
Onecontinuousdatastrategy
The same signal foundation grows with the asset, from first-in-human through commercial care.
Phase I–II
Early Phase
Exploratory efficacy and dose optimisation, functional outcome measurement, and digital biomarker development grounded in disease-biology insight.
Phase III
Late Phase
Registrational endpoint support, continuous participant monitoring, and enhanced treatment differentiation across confirmatory trials.
RWE
Post-Approval
Real-world evidence generation, long-term effectiveness monitoring, label-expansion support, and health economics and reimbursement evidence.
Beyond
Commercial Care
Therapy-support algorithms and validated biomarkers deployed globally via Huma Cloud Platform at SaMD grade.
Designedaroundthequestion
| Aspect | Traditional approach | Huma Alcedis × Samsung |
|---|---|---|
| Sensor configuration | Limited by manufacturer SDKs | Tailored to the scientific objective |
| Data access | Restricted to predefined scores | Rich raw sensor datasets |
| Analytics framework | Locked to manufacturer outputs | Flexible, study-specific analytics |
| Evidence quality | Fixed, predefined metrics | Greater depth and completeness |
| Accountability | Fragmented vendors | One integrated delivery team |
Faster,cleanerexecution
Audit-ready and deployable within weeks.
- Real-time compliance and data-completeness monitoring
- Reduced operational complexity
- Lower vendor-management burden
- Streamlined data integration
- Support for decentralised assessments
- Reduced participant burden
- Lower trial-execution costs
Oneecosystem,notthreevendors
Huma Alcedis and Samsung are not a wearable vendor, a CRO, or a marketplace for digital health tools. Together, they are one evidence-generation ecosystem, spanning early development through real-world implementation.