This paper proposes a method of estimating the knee joint angle during walking using nine-axis motion sensors in a varying magnetic field. The nine-axis motion sensor comprises a three-axis gyro sensor, a three-axis acceleration sensor, and a three-axis geomagnetic sensor. It can estimate joint angles during exercise by correcting the drift of the three-axis gyro sensor using information obtained from the other two sensors. However, the magnetic field cannot be measured correctly using a three-axis geomagnetic sensor in a variable magnetic field. Therefore, the joint angle estimation accuracy is lowered. For this study, the authors corrected the outputs of a three-axis geomagnetic sensor using the three-axis angular velocity obtained from a three-axis gyro sensor. During the laboratory experiment, the 3D motion analysis system and two nine-axis motion sensors measured walking exercise. The knee joint angle results estimated using the two nine-axis motion sensors using corrected outputs of a three-axis geomagnetic sensor generally agreed with the 3D motion analysis system results. Furthermore, two nine-axis motion sensors measured walking exercise outside for about one hour. In the results of knee joint angle estimation using uncorrected outputs of a three-axis geomagnetic sensor, the boundaries between swing phases and stance phases were unclear. Results of knee joint angle estimation using corrected outputs of a three-axis geomagnetic sensor indicate a similar tendency to that found for results of the walking cycle from the laboratory experiment that comprised swing phases and stance phases. This analytical method is anticipated for use in estimating motion in a varying magnetic field.

This paper presents examination of a proposed method for center of gravity (COG) velocity estimation during walking using information obtained from lower limb motion measurements. Lower limb joints and muscles around these joints are used during walking. Gait velocity changes according to lower limb muscle activity. Some earlier reports of relevant studies have suggested that lower limb muscle weakness reduces the walking rate, which increases the probability of falling. Therefore, the relation between the COG velocity and lower limb motion during walking must be clarified. For this study, we constructed gait COG velocity models that represent the relation between the COG velocity and lower limb joint power for each gait phase. For this experiment, gait was measured using a 3D motion analysis system and floor reaction force gauges. We estimated the gait COG velocity models' parameters by applying Kalman filtering using measurement information. The results of analyses using gait COG velocity models indicate a quantitative relation between the COG velocity and the lower limb joint power during walking. Furthermore, results demonstrated that the lower limb joint power that influences the COG movement differs in each gait phase. This analytical method is anticipated for use in the evaluation of healthy feet and ill feet and for evaluating the balance of left and right feet.

No abstract