2026

Journal

Optimal Sensor Placement and Minimal Sensor Combination for Step Width Estimation Using Inertial Measurement Unit-based Gait Data

Kyosuke Fukamachi, Tomohito Yamamoto, Susumu Sato, Takashi Kawanami

Graduate Program in Information and Computer Engineering, Graduate School of Engineering, Kanazawa Institute of Technology, Nonoichi, Ishikawa, Japan

Keywords

step width, gait analysis, insole, inertial measurement unit, daily use

Abstract

Step width is an important indicator of gait stability and fall risk, and continuous assessment in daily life is required. Although step width can be estimated from gait data collected using inertial measurement units (IMUs), the minimal IMU placement suitable for continuous use in daily life has not been clarified. In this study, we evaluated the step width estimation accuracy across different sensor configurations using gait data collected from IMUs attached to the waist and shank, and, for the first time, an IMU embedded in the insole. Gait data from 24 healthy males were used, and step width was estimated using a deep learning model to evaluate the performance of each sensor configuration. Overall, the combination of insole and waist IMUs showed the highest accuracy, achieving a mean absolute error (MAE) of 39.95 ± 13.39 mm, and among single sensor configurations, the insole IMU achieved the best performance with an MAE of 45.41 ± 14.87 mm. Considering ease of attachment and practical use in daily life, the insole IMU may be a promising sensor configuration that enables high accuracy step width estimation with minimal burden.

Moticon's Summary

Step width estimation — a key indicator of gait stability and fall risk — was investigated across multiple inertial measurement unit (IMU) sensor configurations in 24 healthy males. Moticon OpenGo sensor insoles were placed inside participants' shoes to capture IMU and pressure data at 100 Hz, providing foot-based gait measurements alongside waist- and shank-mounted IMUs. A bidirectional long short-term memory (BiLSTM) deep learning model was applied to each sensor configuration, enabling systematic comparison of estimation accuracy. The insole sensor alone achieved a mean absolute error of 45.41 ± 14.87 mm — the best performance among all single-sensor setups — while the insole and waist IMU combination yielded the highest overall accuracy of 39.95 ± 13.39 mm. These results demonstrate that OpenGo sensor insoles offer a practical and accurate solution for continuous step width monitoring in daily-life environments.

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