Assessment of gait patterns of chronic low back pain patients: A smart mobile phone based approach

Chronic low back pain is a common and costly condition and has been shown to affect gait. This paper describes the use of gait analysis as measured by a smart phone in a group of chronic low back pain subjects. Reliability of features extracted from the smart phone sensors was investigated using a mutual information based minimum redundancy and maximum relevance feature selection method to identify a key feature set related to lower back pain. This analysis was carried out using a KStar classification model. Results indicate the feasibility of reducing gait features to 6 key components while still achieving very promising classification accuracy (92.50%). The results also demonstrated that it is feasible to use a smart mobile phone in gait tele-monitoring and tele-assessment suggesting potential as both a prognostic and potential treatment outcome. In addition, we show that predicting context such as age and gender using smart mobile phones is achievable, which has potential to provide personalised services and context-related monitoring and intervention.