Machine Learning
Gait analyzer based on a cell phone with a single three-axis accelerometer
Proceedings of the 8th conference on Human-computer interaction with mobile devices and services
Portable Activity Monitoring System for Temporal Parameters of Gait Cycles
Journal of Medical Systems
Gait Analysis Using a Shoe-Integrated Wireless Sensor System
IEEE Transactions on Information Technology in Biomedicine
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Gait analysis is the process of measuring and evaluating gait and walking spatio-temporal patterns, namely of human locomotion. This process is usually performed on specialized equipment that is capable of acquiring extensive data and providing a gait analysis assessment based on reference values. Based on gait assessments, therapists and physicians can prescribe medications and provide physical therapy rehabilitation to patients with gait problems. This work is oriented to support the design of ambulatory and ubiquitous technologies for gait monitoring. A probabilistic method to automatically detect human strides from raw signals provided by wireless accelerometers is presented. Local thresholds are extracted from raw acceleration signals, and used to distinguish actual strides from characteristic peaks commonly produced by significant shifts of the acceleration signals. Then, a bayesian classifier is trained with these peaks to detect and count strides. The proposed method has a good precision for classifying strides of raw acceleration signals for both, young and elderly individuals. Strides detection is required to calculate gait parameters and provide a clinical assessment.