Smart mobile phone based gait assessment of patients with low back pain

Smart mobile phones have become a ubiquitous device that many people have access to and are commonly used in daily living. This paper explores the iPhone as a device, which can potentially be a feasible alternative to the conventional methods of data collection for gait analysis and assessment. Within the iPhone, the internal accelerometer is a micro electro-mechanical system (MEMS) based sensor. This study compared the phone sensor against a commercial standalone accelerometer, Minimod developed by McRoberts. One of the most common and costly conditions that people may encounter at one point in their lives is non-specific chronic low back pain (nscLBP). Due to the nature of the condition, it is difficult to determine the aetiology and assess the status of recovery. Machine learning (ML) algorithms were implemented to determine whether patients with LBP and healthy controls could be classified based on gait characteristics recorded by the smart phone. The results show that 85.7% accuracy can be achieved from the features extracted from the iPhone accelerometer, with improvements of 88.8% when feature selection methods are applied. To investigate the feasibility of using iPhone embedded MEMS sensors, intraclass correlation coefficient (ICC) were performed to determine the agreement between the features extracted from the portable devices. A Mann-Whitney U-Test was employed to determine whether features were significantly different between the groups of subjects. It can be concluded from this study that, using the iPhone accelerometer, features can be successfully extracted with high agreement. Classification is achieved with significant accuracy to differentiate between patient and control groups. The experiments demonstrate that the iPhone and smart phone equivalents can provide an accurate and precise measurement that can be used for gait assessment and analysis.