Physical activity and gait, measured with care.

We turn accelerometry and device summaries into activity, sedentary and gait measures, check them against reference devices, and relate them to the outcomes your study cares about.

EXAMPLE WEEK · STEPS PER DAYMONTUEWEDTHUFRISATSUN8,000 TARGETWear time 96% · 3 sedentary bouts over 2 h flagged
What we deliver

Physical activity and gait analytics, end to end.

Activity

Activity and sedentary time

Steps, intensity minutes, sedentary bouts and daily activity profiles, with wear-time detection so missing wear is not mistaken for inactivity.

Energy

Energy expenditure

Estimates from accelerometry, reported with the uncertainty each device and population allows.

Gait

Gait parameters

Cadence, stride time and step length from inertial sensors, with clear reporting of which parameters agree well with reference systems.

Validation

Device comparison

Agreement with research-grade accelerometers such as ActiGraph, as in our Oura ring validation study.

Populations

Pediatric and clinical cohorts

Device and cut-point choices checked for the population you study, including children.

Outcomes

Activity and health outcomes

Effects of activity on sleep, quality of life or symptoms, including causal machine learning.

Methods

How we analyze it

  • Accelerometry preprocessing and wear-time detection
  • Bout and intensity classification
  • Agreement analysis against reference devices
  • Mixed-effects and causal models for longitudinal outcomes
Deliverables

What you receive

  • Daily and weekly activity summaries per participant
  • Figures and tables ready for publication
  • A methods section reviewers can follow
  • Documented, reproducible code
Evidence

Selected peer-reviewed work by our team.

  1. Niela-Vilén et al. Comparison of Oura smart ring against ActiGraph accelerometer for measurement of physical activity and sedentary time in a free-living context. CIN: Computers, Informatics, Nursing, 2022. doi:10.1097/CIN.0000000000000885
  2. Kazemi et al. Impact of physical activity on quality of life during pregnancy: a causal ML approach. IEEE EMBC, 2024. doi:10.1109/EMBC53108.2024.10782584
  3. Kazemi et al. Can sleep quality attributes be predicted from physical activity in everyday settings?. IEEE EMBC, 2023. doi:10.1109/EMBC40787.2023.10340421
  4. Khatibi et al. Impact of COVID-19 pandemic on sleep including HRV and physical activity as mediators: a causal ML approach. IEEE BSN, 2023. doi:10.1109/BSN58485.2023.10331423