Movement disorders, such as Parkinson's disease, affect more than 10 million people worldwide. Gait analysis is a critical step in the diagnosis and rehabilitation of these disorders. Specifically, step length provides valuable insights into the gait quality and rehabilitation process. However, traditional approaches for estimating step length are not suitable for continuous daily monitoring since they rely on special mats and clinical environments. To address this limitation, we present a novel and practical step-length estimation technique using low-power wearable bend and inertial sensors. Experimental results show that the proposed model estimates step length with 5.49% mean absolute percentage error and provides accurate real-time feedback to the user.

Sizhe An

Yigit Tuncel

Toygun Basaklar

Gokul K. Krishnakumar

Ganapati Bhat

Umit Y. Ogras

ACM Transactions on Internet of Things

<i>MGait</i> : Model-Based Gait Analysis Using Wearable Bend and Inertial Sensors

A novel and practical step-length estimation technique using low-power wearable bend and inertial sensors is presented that estimates step length with 5.49% mean absolute percentage error and provides accurate real-time feedback to the user.

MGait : Model-Based Gait Analysis Using Wearable Bend and Inertial Sensors

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