An Accessible, Low-Cost method for Gait Monitoring with a Raspberry Pi and MPU 6050: Proof of Concept

Abstract

Gait deviations are common in aging populations and those with neurological or musculoskeletal disorders, leading to reduced independence and higher healthcare costs. Even though optical motion-capture systems deliver the accuracy necessary for kinematic analysis, the cost associated with it restricts the usage in laboratory settings. We developed a low-cost, wearable gait measuring device that can be held in the hand, utilizing a Raspberry Pi 5 and an MPU6050 IMU. The system recorded six motion features from an inertial measurement unit and compared walking trials against a user-specific baseline using a Euclidean distance metric. Unlike camera-based or smartphone-dependent systems, our proposed device operates independently of external infrastructure and is designed to support patient-specific calibration using minimal computational resources. We hypothesized that gyroscope-based motion features would be more sensitive than accelerometer features for distinguishing deviations from normal gait. The tool has the potential to enhance remote gait monitoring, rehabilitation, as well as the early detection of conditions of gait abnormalities, thus improving the quality of life for patients suffering from gait disorders. Preliminary testing using simulated gait conditions demonstrated greater variability in gyroscopic features compared to accelerometer features, with correct classification in approximately 75% of tested conditions across multiple simulated gait scenarios.