  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.
