Development of an embedded gait analysis system for brain damaged patients

Abstract

Optical motion analysis systems are widely used to evaluate the efficiency of treatment/rehabilitation for poststroke patients. These systems are only available in clinical settings due to their size/cost and need of complicate post processing. This paper presents the development of an embedded low cost and easy-to-use system for gait analysis, which measures the joints angles and automatically detects foot-off (FO) and foot-contact (FC) events of stroke walking. The system uses 7 wireless inertial measurement unit (IMUs) to measure the body segments orientations and 2 ultra-wideband (UWB) rangefinders placed near the heels for foot relative distance measurement. Data from sensors are synchronized and recorded in SD card at 70 Hz by controller (AtoutNovation, France). The locomotion of 14 stroke and 4 healthy subjects have been evaluated simultaneously with proposed system and a 3D motion analysis system. Eight gait records at a self-selected speed along 8 m walkway were performed by each subject. The joint angles in sagittal plane were calculated using orientations of segments. To avoid perturbation of external magnetic field (e.g. treadmill walking), the magnetometer was not used, therefore the system cannot calculate 3D joints values. Relative feet distance and foot angular velocity were used to determinate gait events. The joints angles in sagittal plane are segmented, normalized and averaged in gait cycle automatically based on detected gait events. The comparisons between proposed system and motion capture system showed a precision of 28.6 ± 28.3 ms for FC detection and 18.1 ± 18.0 for FO events. 98.9% of FC and 98.8% of FO events were automatically detected. The comparisons show a precision of 4.3 ± 2.9° for knee, 2.9 ± 2.8° for ankle and 4.8 ± 3.4° for hip. Correlation coefficient of all joint angles are higher than 0.97. The proposed system constitutes a new low cost and wearable device allowing to assess stroke patient's locomotion. Indeed, gait event detection accuracy is acceptable since the data are sampled each 14 ms. The joint angles have a high correlation coefficient and the differences between the 2 systems are limited. Its performance and easy use make it a good candidate for pathological gait analysis in ecological conditions.