The sensor
Attaches to the shoe with a clip or an elastic band and records the movement of the foot over 100 times per second.
Fall prevention technology from CSIC
We analyse gait to prevent falls
Two small sensors on the shoes and a walk of less than a minute. Moverics measures how a person walks and estimates their fall risk and frailty, with a clear report for the professional.
- Developed at CSIC
- Tested with hundreds of older adults
- Free app and console
A gait analysis lab that fits in your pocket
Fast
Less than two minutes per assessment, with results in seconds.
Accurate
Less than 2% error in gait speed and stride length compared with optical lab systems, also across different ways of walking.
Simple
No calibration or setup: the app guides every step by voice.
Why it matters
Falls are one of the biggest health problems for older people
842,000deaths from falls worldwide in 2023
2ndleading cause of unintentional injury deaths
71%of those deaths, in people over 60
The good news: physical exercise (balance, strength and flexibility) reduces falls by around 23%. The key is to detect the risk early and check whether it improves.
What Moverics is
One system, three parts
Everything needed to measure gait outside the lab, with no calibration and no setup.
The app
Guides the test step by step with spoken instructions and shows the result straight away. Android and iOS.
The web console
Each person’s history and progress, PDF reports, multi-centre studies and analytics for research.
How it works
Easy to use, anywhere
A full assessment takes less than two minutes, in a clinic, in a care home corridor or at home.
1Attach the sensors
One on each shoe, clipped to the laces or with the band. No cables.
2Press start
The app gives spoken instructions. The person stands still for a few seconds.
3Walk
There and back at their usual pace, up to about 30 steps: less than a minute.
- 4
See the result
In seconds: fall risk, frailty and gait parameters, with a PDF report.
Quick-start video
In two minutes: what is in the case, how to charge and attach the sensor, and how to run an assessment.
Video in Spanish.
- No lab and no calibration.
- Works without a phone too: record with the sensor’s button and the data uploads itself over WiFi.
- With one or two sensors; with two, you also get the symmetry between feet.
What it measures
Simple on the outside, powerful inside
Every walk becomes useful clinical information, ready for decisions and for tracking progress.
Fall-risk model: AUC 0.84 in cross-validation and 0.92 on an external cohort.
Fall risk
A 0–100 index from a machine-learning model trained on 158 older adults and checked on a separate cohort.
Frailty
Robust, pre-frail or frail, with the model’s confidence level.
25 gait parameters
Speed, stride, cadence, gait phases, foot clearance and angles, turns and regularity.
Both feet
Asymmetries and double support when two sensors are used.
Progress
Compares assessments and shows whether an intervention is working.
Reports and data
PDF report, Excel export, case report forms and built-in statistical analytics.
Real example
See a complete report
The report and data from a real assessment: an older woman from the open GSTRIDE database, with moderate fall risk and pre-frail.
Clinical report (PDF, 10 pages, Spanish)
Fall risk and frailty, gait parameters with their interpretation, gait cycle, foot orientation and comparison with people of the same age.
Download| Gait speed | 0.90 m/s |
|---|---|
| Stride length | 1.04 m |
| Cadence | 103.7 steps/min |
| Foot clearance | 5.9 cm |
| Stance phase | 64.3% |
Gait data (Excel)
A summary of each parameter and the data for every stride, ready for analysis.
DownloadData from the open GSTRIDE database (Zenodo, CC BY 4.0 licence). The person is anonymous; their code and age are blurred.Would you like more examples or a live demo? Write to us.
From data to action
Measure, act and measure again
Moverics fits into a simple prevention cycle: know the starting point, intervene and check the effect.
Assess
A gait test sets the starting point.
Act
The professional prescribes tailored strength, balance and flexibility exercise, for example with Vivifil.
Reassess
A few weeks later, the same test shows the change.
Adjust
With objective data, the intervention is adjusted and the cycle repeats.
Assess → Act → Reassess → Adjust
Who it is for
Designed for those who look after mobility
Clinics and hospitals
An objective gait assessment in minutes, with no lab, alongside the geriatric assessment.
Care homes and day centres
Regular follow-up of each resident to spot loss of mobility early.
Research
Multi-centre studies, traceable data, export and built-in statistical analytics.
Home care
The sensor records with a single button and uploads the data over WiFi, no phone needed.
Evidence
Science behind every step
Moverics builds on more than a decade of research on inertial sensors and gait analysis, with hospitals and universities.
Publications
- Validation of an IMU-based gait analysis method for assessment of fall risk against traditional methods
- Predictors of fall risk in older adults using the G-STRIDE inertial sensor: an observational multicenter case–control study
- A database with frailty, functional and inertial gait metrics for the research of fall causes in older adults
- A comparative study of gait analysis technologies
- Evaluation of gait parameter estimation accuracy: a comparison between commercial IMU and optical capture motion system
- Detecting fall risk and frailty in elders with inertial motion sensors: a survey of significant gait parameters
- Influence of IMU’s measurement noise on the accuracy of stride-length estimation for gait analysis
Open data
GSTRIDE
163 older adults: clinical and functional assessment and inertial gait signals.
View on ZenodoProjects
- MUEVE-TCSIC · IMPULSA-T · 2025–2026
- DISTRIMUSEKDT JU · PCI2024-153514 · 2024–2027
- GAIT2CARETED2021-132429B-I00 · 2022–2024
- INDRIPID2021-122642OB-C43 · 2022–2026
- NextPerceptionH2020-ECSEL-2019-2-RIA · 2020–2023
- G-STRIDEFundación MAPFRE · Ignacio H. de Larramendi · 2021–2022
In collaboration with
- CSIC · Centro de Automática y Robótica (CSIC-UPM)
- Hospital Universitario Infanta Leonor
- Universidad Rey Juan Carlos
- Hospital Universitario Perpetuo Socorro (SESCAM)
Team
Who we are
A research team that brings together geriatrics, biomedical engineering and inertial sensors.
Marta Neira Álvarez
MD, PhD · Geriatrician
Infanta Leonor University Hospital · President of the Spanish Society of Geriatric Medicine (SEMEG)
Luisa M.ª Ruiz Ruiz
PhD in Biomedical Engineering · Data scientist
CSIC · LOPSI group, Centre for Automation and Robotics (CSIC-UPM)
Antonio R. Jiménez Ruiz
PhD in Physics · Research Scientist at CSIC
CSIC · LOPSI group, Centre for Automation and Robotics (CSIC-UPM)
Collaborators: Rey Juan Carlos University (Dr Elisabet Huertas Hoyas and Dr Sara García de Villa) and Perpetuo Socorro University Hospital, Albacete (Dr Rafael García Molina and the team of Prof. Pedro Abizanda Soler).
Contact
Would you like to use Moverics in your centre or study?
Tell us what you need and we will help you get started. The app and the console are free.