Real-time lower limbs motion or gait measurement is an important part in human-robotic interaction for the control of robotic walkers and rehabilitation devices. Laser range finder or infrared sensor that is mounted on the device has been widely used in applications. Although these sensors can provide accurate horizontal motion information of lower limbs during human walking, it is still difficult to measure the angular motion of lower limbs due to their functional principles. Using inertial measurement units (IMU) can measure the angular motion of lower limbs, but it requires a large amount of IMU units for measurements of all lower limb segments. In this study, a novel method is developed for real-time monitoring lower limbs (shanks and thighs) motion in human walking using just two shank-mounted IMUs. A pose prediction model based on multiple linear regression and Kalman filter is proposed. The root-mean-square error (RMSE) of the thigh orientation and the knee joint angle estimation in sagittal plane are 6.1 ± 1.3 and 6.8 ± 1.4 degs, respectively. The RMSE of the ankle, knee, and hip position estimation are 4.2 ± 1.3, 4.2 ± 1.1 and 3.5 ± 0.9 cm, respectively.

Inertial measurement units (IMU) have been used for gait analysis in many clinical studies, as a more convenient, low cost and less restricted alternative to the laboratory-based motion capture systems or instrumented walkways. Spatial-temporal gait parameters such as gait cycle duration and stride length calculated from the IMUs were often used in these studies for evaluating the impaired gait. However, the spatial-temporal information provided by IMUs is limited, and sometime suffers incomplete and less effective evaluation. In this study, we develop a novel IMU-based method for clinical gait evaluation. Nine gait variables including three spatial-temporal parameters and six kinematic parameters are extracted from two shank-mounted IMUs for quantifying patient's gait deviations. Based on those parameters, an IMU-based gait normalcy index (INI) is derived to evaluate the overall gait performance. Eight inpatient subjects with gait impairments caused by n-hexane neuropathy and ten healthy subjects were recruited. The proposed gait variables and INI were examined on the inpatients at three to five time instants during the rehabilitation process until being discharged. A comparison with healthy subjects and statistical analysis for the changes of gait variables and INI demonstrated that the proposed new set of gait variables and INI can provide adequate and effective information for quantifying gait abnormalities, and help understanding the progress of gait and effectiveness of therapy during rehabilitation process.