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.

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

The black box problem

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.

Access to Samsung's Research Platform

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.

Beyond device provision

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, earlier evidence

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.

One continuous data strategy

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 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.

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.

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.

Beyond

Commercial Care

Therapy-support algorithms and validated biomarkers deployed globally via Huma Cloud Platform at SaMD grade.

Designed around the question

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, cleaner execution

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

One ecosystem, not three vendors

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.

Take the next step

Tell us what you’re working on and what you need from Huma to move forward.

Take the next step

Tell us what you’re working on and what you need from Huma to move forward.