Browsing by Author "Cheng, Chi-Yuan"

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A Kinematic Human-Walking Model for the Normal-Gait-Speed Estimation Using Tri-Axial Acceleration Signals at Waist Location
(2013-08-01)
Hu, Jwu-Sheng
;
Sun, Kuan-Chun
;
Cheng, Chi-Yuan
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This study aims at estimating the human walking speed using wearable accelerometers by proposing a novel virtual inverted pendulum model. This model not only keeps the important characteristics of both the biped rolling-foot and the inverted pendulum model, but also makes the speed estimation feasible using human body acceleration. Rather than using statistical methods, the proposed kinematic walking model enables calibration of the parameters during walking using only one tri-axial accelerometer on the waist that collects the user's body acceleration. In addition, this model also includes the effect of rotation of the waist within a walking cycle, which improves the estimation accuracy. Experimental results for a group of humans show a 0.58% absolute error mean and 0.72% error deviation, which is far better than the results of other known studies with accelerometers mounted on the upper body.
A Model-based Human Walking Speed Estimation Using Body Acceleration Data
(2012)
Hu, Jwu-Sheng
;
Sun, Kuan-Chun
;
Cheng, Chi-Yuan
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This study aims at estimating the human walking speed using wearable accelerometers by proposing a novel virtual inverted pendulum model. This model not only keeps the important characteristic in biped rolling-foot model, but also makes the speed estimation feasible using human body acceleration. Rather than the statistical methods, the proposed kinematic walking model enables calibration of the parameters during walking using only one tri-axial accelerometer on waist that reflects the user's inertia information during walking. In addition, this model also includes the effect of rotation of waist within a walking cycle that improves the estimation accuracy. Experimental results on a group of humans show a 1.22% error mean and 2.78% deviation, which is far better than other known studies.
GNSS/INS Sensor Fusion Using Kalman Filter with Covariance Adaptation
(2013-01-01)
Cheng, Chi-Yuan
;
Sun, Kuan-Chun
;
Hu, Jwu-Sheng
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a new method which estimates the Kalman filter\'s measurement noise covariance matrix in a loosely coupled GNSS/INS sensor fusion system. The signals received from the satellites are heavily influenced by the environmental conditions, and will cause the noise variance to change dramatically. Thus, estimating the measurement noise to improve the fusion quality is very important. Most of the proposed methods use the moving average method to estimate the measurement noise online from the innovation sequence of the Kalman filter in the window interval. However, those methods can only have a good performance in a quasi-static environment, e. g., freeway where the signals are rarely blocked. In dynamic environments such as the urban area, the satellite signals are often blocked by the buildings, resulting in a large range of the noise variance. This paper proposes to use Savitzky-Golay filter with degree adaptation (ADSG) to estimate the measurement noise. The ADSG filter uses local polynomial regression method to fit the delayed-sample innovation sequence in the window interval, and adjust the polynomial order by the statistics F-test. Experiments show that the proposed method\'s horizontal position error is about 0.3m, which is better than several existing methods.