LAUSR

The utility of wearable sensors for continuous gait monitoring has grown substantially, enabling novel applications on mobility assessment in healthcare. Existing approaches for gait cycle detection rely on predefined or experimentally tuned platform parameters and are often platform specific, parameter sensitive, and unreliable in noisy environments with constrained generalizability. To address these challenges, we introduce CyclePro,1 novel framework for reliable and platform-independent gait cycle detection. CyclePro offers unique features: 1) it leverages physical properties of human gait to learn model parameters; 2) captured signals are transformed into signal magnitude and processed through a normalized cross-correlation module to compensate for noise and search for repetitive patterns without predefined parameters; and 3) an optimal peak detection algorithm is developed to accurately find strides within the motion sensor data. To demonstrate the efficiency of CyclePro, three experiments are conducted: a clinical study including a visually impaired group of patients with glaucoma and a control group of healthy participants; a clinical study involving children with Rett syndrome; and an experiment involving healthy participants. The performance of CyclePro is assessed under varying platform settings and demonstrates to maintain over 93% accuracy under noisy signal, varying bit resolutions, and changes in sampling frequency. This translates into a recall of 95.3% and a precision of 93.4%, on average. Moreover, CyclePro can detect strides and estimate cadence using data from different sensors, with accuracy higher than 95%, and it is robust to random sensor orientations with a recall of 91.5% and a precision of 99.2%, on average.1Software code and sample datasets for CyclePro are publicly available at https://github.com/y-max/CycleProa