IMU Based Deep Stride Length Estimation With Self-Supervised Learning

Jien De Sui, Tian-Sheuan Chang

Research output: Contribution to journalArticlepeer-review


Stride length estimation using inertial measurement unit (IMU) sensors is getting popular recently as one representative gait parameter for health care and sports training. The traditional estimation method requires some explicit calibrations and design assumptions. Current deep learning methods suffer from few labeled data problem. To solve above problems, this paper proposes a single convolutional neural network (CNN) model to predict stride length of running and walking and classify the running or walking type per stride. The model trains its pretext task with self-supervised learning on a large unlabeled dataset for feature learning, and its downstream task on the stride length estimation and classification tasks with supervised learning with a small labeled dataset. The proposed model can achieve better average percent error, 4.78%, on running and walking stride length regression and 99.83% accuracy on running and walking classification, when compared to the previous approach, 7.44% on the stride length estimation.

Original languageEnglish
Pages (from-to)7380-7387
Number of pages8
JournalIEEE Sensors Journal
StatePublished - Mar 2021


  • Accelerometers
  • convolutional neural networks
  • Estimation
  • gait parameter
  • inertial-measurement-unit sensor
  • Legged locomotion
  • self-supervised
  • Sensor signal processing
  • Sensor systems
  • Sensors
  • stride length
  • Task analysis
  • Training

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