Real-Time Foot Height Estimation and Activity Classification Using a Foot-Mounted IMU Implemented on a Smartphone

May 16, 2026·
Ehsan Sharafian
Ehsan Sharafian
,
Babak Hejrati
· 2 min read
Real-time foot height estimation from a foot-mounted IMU
Abstract
We developed a real-time, single-IMU smartphone system that reconstructs continuous foot-height trajectories and simultaneously classifies five locomotion activities entirely on-device. A Zero Height Change (ZHC) constraint corrects the cumulative vertical drift that limits conventional zero-velocity methods, while a heel-strike velocity feature drives adaptive activity classification. Validated on twenty adults, the system reached 96.08% classification accuracy with less than one gait-cycle latency and kept cumulative height errors below 1.1 cm across ramp and stair negotiation — providing a practical basis for real-time gait intervention and fall prevention.
Type
Publication
Sensors, 26(10), 3166
Status
Peer-reviewed Open access
publications

Why it matters

Tripping is a leading cause of falls, and it is closely tied to how high we lift our feet while walking. Measuring foot clearance continuously, out in daily life, could flag fall risk early — but doing it well with a wearable sensor is hard:

  • A single inertial measurement unit (IMU) drifts, so integrated foot height accumulates error over time.
  • The usual fix (zero-velocity updates) does not fully remove vertical drift.
  • More accurate methods work only offline and do not tell you what activity the person is doing.

What I built

A real-time, single-IMU system that runs entirely on a smartphone — no cloud, no multi-sensor setup. It reconstructs the foot’s height trajectory stride by stride and classifies five locomotion activities (level walking, ramp ascent/descent, stair ascent/descent) at the same time.

The IMU is mounted on top of the foot; the toe position is recovered from the sensor pose by a rigid-body transformation

Key contributions:

  • On-device Android app for stride-by-stride foot-clearance measurement and live activity classification.
  • Zero Height Change (ZHC) constraint — a biomechanically grounded correction that cancels cumulative vertical drift at each stride boundary.
  • Heel-strike velocity error used as a compact, physically meaningful feature to adaptively pick the right reconstruction model per activity.
  • Toe-height estimation from the IMU pose through a rigid-body transformation, with accuracy comparable to the foot itself.

The custom Android app: (a) system and sensor controls, (b) activity labeling, (c) live activity detection with gait parameters, (d) deployment on stairs

Validating the height measurement

The reconstructed foot trajectories separate cleanly by activity, and the continuous height signal tracks real-world ramps and stairs across a full walking circuit.

Reconstructed foot trajectories for the five activities; dashed boxes mark stair entry/exit transition strides

Continuous foot-height trajectory across a full circuit of level ground, ramps, and stairs, with zero-velocity points marked

To confirm the system measures real clearance, participants stepped over boxes of known heights and the estimated peak stride height was checked against each obstacle.

Obstacle-clearance validation: participants step over boxes of increasing height, and the peak vertical displacement is compared against each box

Results

  • 96.08% overall activity-classification accuracy, with less than one gait cycle of latency.
  • Level walking stayed at the ground reference (0.0 cm, 95% CI −1.8 to 1.8 cm).
  • Cumulative height error stayed below 1.1 cm across ramp and stair negotiation (mean absolute error 0.42%).
  • Toe height was recovered with accuracy comparable to foot height.

Together, this gives a practical foundation for real-time gait feedback and fall-prevention tools that could one day run on the phone already in someone’s pocket.

Wearable Sensing Foot Clearance Drift Correction Zero Height Change Object Crossing Locomotion Activities Smartphone Applications
Ehsan Sharafian
Authors
Ehsan Sharafian
PhD Candidate in Mechanical Engineering
PhD candidate in Mechanical Engineering at the University of Maine. Focused on intelligent wearable systems that combine biomechanics, IMU-based sensing, machine learning, and real-time mobile applications for human movement analysis, gait assessment, activity recognition, and digital health.