Development and Validation of the Pre- and Post-Processing Algorithms for Quantitative Gait Analysis using a Prototype Wearable Sensor System

Abstract

Walking is the most common form of human locomotion and the systematic study thereof is known as gait analysis. Measurement and assessment thereof have application in many fields including clinical diagnosis, rehabilitation and biomechanics. The process of gait evaluation is typically done using an optical motion analysis system combined with stationary force platforms. This is considered the gold standard, but unfortunately, has several drawbacks. It is expensive, requires dedicated laboratories with spatial restrictions, calls for lengthy set up and post-processing times and cannot be used in 'real-world' environments. Alternative systems based on wearable sensors have been developed to overcome these limitations. The Council for Scientific and Industrial Research (CSIR) has therefore developed a prototype wearable sensor unit consisting of an inertial measurement unit (IMU). The objective of the current study is, therefore, to advance the prototype to a wearable multi-sensor system for quantitative gait analysis. The focus is on the development of the pre- and post-processing algorithms and methods used to transform the measurements into interpretable information. The focus outlined includes establishing techniques for synchronising the data from the sensors offline, pre-processing the signals, developing algorithms for stride and gait event detection, selecting an appropriate gait model and defining methods for estimating gait parameters. The determined parameters were the spatio-temporal and joint kinematics (hip, knee and ankle). The algorithms and new system were validated against the Vicon motion capture system through gait analyses. The twenty able-bodied volunteers that took part were required to walk across the laboratory six times at three self-selected walking speeds (slow, normal and fast). For the sake of simplicity and due to various limitations, only data in the sagittal plane of the right lower limb of each volunteer was used to validate the wearable system and associated algorithms. The results obtained were then evaluated against several validation criteria. The absolute mean difference between the estimated timing of detected gait events of the two systems was consistently small (between 0.021 and 7.25% of the gait cycle overall). The spatially dependent parameters, stride length and walking speed, had significant maximum mean absolute percentage errors (31.9 and 34.5% respectively), but with little variation. Excluding outliers, that of the temporal parameters, stride time and cadence, was significantly lower (5.7 and 5.6% respectively). The kinematic results were substantially comparable with a minimum correlation co-efficient of 0.86 and a maximum RMSE of 7.8 degrees with little variation implying repeatability. Although there were some discrepancies between the outputs, the wearable sensor system and its corresponding algorithms were considered feasible and potentially beneficial to developing countries like South Africa. Recommendations for future work include synchronising data between the wearable and reference system for stride-to-stride comparisons and validating algorithms using a known reliable wearable system.