Ambulatory Measurement and Analysis of the Lower Limb 3D Posture Using Wearable Sensor System
ABSTRACT An original approach for ambulatory measurement and analysis of lower limb 3D gait posture was presented, and a wearable sensor system was developed according to the approach. To explicate the lower limb posture, thigh orientation angles were calculated based on a virtual sensor at the hip joint and double analog inertial sensors (MAG3) on the thigh; Knee joint angle in sagittal plane was calculated with combination of angular accelerations and angular velocities measured by two MAG3 on the thigh and shank on the basis of the virtual-sensor based algorithm. The developed wearable sensor system was evaluated on the lower limb. Without integration of angular acceleration or angular velocity for the thigh orientation angles and the knee joint angle, the calculated result was not distorted by offset and drift. Using virtual sensors at the hip joint and the knee joint were more simple, practical and effective than fixing physical sensors at these joints. Compared with the result from the reference system, the measured result with the developed wearable sensor system was feasible to do gait analysis for the patients in the daily life, and the method can also be used in other conditions such as measuring rigid segment posture with less sensors and high degree of accuracy.
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ABSTRACT: This contribution is concerned with joint angle calculation based on inertial measurement data in the context of human motion analysis. Unlike most robotic devices, the human body lacks even surfaces and right angles. Therefore, we focus on methods that avoid assuming certain orientations in which the sensors are mounted with respect to the body segments. After a review of available methods that may cope with this challenge, we present a set of new methods for: (1) joint axis and position identification; and (2) flexion/extension joint angle measurement. In particular, we propose methods that use only gyroscopes and accelerometers and, therefore, do not rely on a homogeneous magnetic field. We provide results from gait trials of a transfemoral amputee in which we compare the inertial measurement unit (IMU)-based methods to an optical 3D motion capture system. Unlike most authors, we place the optical markers on anatomical landmarks instead of attaching them to the IMUs. Root mean square errors of the knee flexion/extension angles are found to be less than 1° on the prosthesis and about 3° on the human leg. For the plantar/dorsiflexion of the ankle, both deviations are about 1°.Sensors 04/2014; 14(4):6891-909. DOI:10.3390/s140406891 · 2.05 Impact Factor
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ABSTRACT: This study proposes a method to measure the twisting angle of a rod during a spinal correction surgery in real-time without performing an alignment procedure by using the six-axis data (tri-axis acceleration, tri-axis rate gyro) of the IMU and a Tait-Bryan Euler angle algorithm. To determine whether the twisting angle calculation algorithm offered an improvement, typical procedures described in existing studies were implemented using the same experimental data and were then validated using a three-dimensional motion analysis system (Vicon 460 motion analysis system). The correlation coefficients and the RMSE of the proposed method were 0.904 and 0.680° in the servo-motor experiment, and 0.988 and 0.691° in the mock surgery experiment, respectively, and these values were not significantly different from those calculated through other methods in previous studies. Therefore, if the proposed method is used during surgery, an alignment procedure and the assumptions following the morphology of body, which are limitations of prior research, are not necessary. Also the twisting angle of the rod can be observed without using magnetic data of IMU in real time during surgery. It is expected that the correction loss, which is a serious problem that can occur in patients after spinal correction, could be prevented.Journal of applied biomedicine 01/2015; 13(2). DOI:10.1016/j.jab.2015.01.001 · 1.78 Impact Factor
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