scholarly journals Estimation of a Human-Maneuvered Target Incorporating Human Intention

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5316
Author(s):  
Yongming Qin ◽  
Makoto Kumon ◽  
Tomonari Furukawa
Keyword(s):  
Conventional Technique ◽  
Estimation Accuracy ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
State Estimator ◽  
New Approach ◽  
Motion Patterns ◽  
Continuous States ◽  
Human Intention ◽  
The Mean

This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states. In the preprocessing, an Interacting Multiple Model (IMM) estimation technique is used to infer the intentions and extract motions, which eventually construct the intention-pattern model. Once the intention-pattern model has been constructed, the proposed approach incorporate the intention-pattern model to estimation using any state estimator including Kalman filter. The proposed approach not only estimates the mean using the human intention more accurately but also updates the covariance using the human intention more precisely. The performance of the proposed approach was investigated through the estimation of a human-maneuvered multirotor. The result of the application has first indicated the effectiveness of the proposed approach for constructing the intention-pattern model. The ability of the proposed approach in state estimation over the conventional technique without intention incorporation has then been demonstrated.

scholarly journals Adaptive Tracking Method for Non-Cooperative Continuously Thrusting Spacecraft

Aerospace ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 244
Author(s):  
Juqi Yin ◽  
Zhen Yang ◽  
Yazhong Luo
Keyword(s):  
Kalman Filter ◽  
Estimation Accuracy ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Tracking Method ◽  
Adaptive Tracking ◽  
Convergence Performance ◽  
Model Set ◽  
Traditional Approaches ◽  
A Current

Performance of the traditional Kalman filter and its variants can seriously degrade when they are used to track a non-cooperative continuously thrusting spacecraft. To overcome this shortcoming, an adaptive tracking method for relative state estimation of a non-cooperative target is proposed based on the interacting multiple model (IMM) algorithm. First, built upon a current statistical jerk (CSJerk) model, a robust CSJerk filtering (RCSJF) algorithm is developed, which can address the issue of low estimation accuracy and instability of traditional approaches at the moments when the spacecraft starts and ends thrusting. Second, the developed RCSJF algorithm is further used to form the model set of the IMM by incorporating different maximum jerk values, based on which an adaptive tracking method is presented that can track a non-cooperative target with different maneuvering levels. Simulation results show that the proposed method can effectively track the target across all thrusts levels under the conditions considered, and the convergence performance of the proposed method is improved in comparison to the CSJerk-based extended Kalman filter, especially at the start and end time of the maneuver.

scholarly journals Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 798
Author(s):  
Hamed Darbandi ◽  
Filipe Serra Bragança ◽  
Berend Jan van der Zwaag ◽  
John Voskamp ◽  
Annik Imogen Gmel ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Biomechanical Analysis ◽  
Estimation Accuracy ◽  
Speed Estimation ◽  
Motion Patterns ◽  
Positioning Systems ◽  
Machine Learning Approach ◽  
Global Positioning ◽  
Measurement Units

Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.

A new simpler approach to measure the strength of toe plantar flexion requiring no mechanical restraint with a light-weight device

2021 ◽  
pp. 095441192110029
Author(s):  
Yuko Komuro ◽  
Yuji Ohta
Keyword(s):  
Plantar Flexion ◽  
Mean Value ◽  
Simple Approach ◽  
Light Weight ◽  
New Approach ◽  
Mean Values ◽  
Mechanical Restraint ◽  
Significant Difference ◽  
Ankle Joints ◽  
The Mean

Conventionally, the strength of toe plantar flexion (STPF) is measured in a seated position, in which not only the target toe joints but also the knee and particularly ankle joints, are usually restrained. We have developed an approach for the measurement of STPF which does not involve restraint and considers the interactions of adjacent joints of the lower extremities. This study aimed to evaluate this new approach and comparing with the seated approach. A thin, light-weight, rigid plate was attached to the sole of the foot in order to immobilize the toe area. Participants were 13 healthy young women (mean age: 24 ± 4 years). For measurement of STPF with the new approach, participants were instructed to stand, raise the device-wearing leg slightly, plantar flex the ankle, and push the sensor sheet with the toes to exert STPF. The sensor sheet of the F-scan II system was inserted between the foot sole and the plate. For measurement with the seated approach, participants were instructed to sit and push the sensor with the toes. They were required to maintain the hip, knee, and ankle joints at 90°. The mean values of maximum STPF of the 13 participants obtained with each approach were compared. There was no significant difference in mean value of maximum STPF when the two approaches were compared (new: 59 ± 23 N, seated: 47 ± 33 N). The coefficient of variation of maximum STPF was smaller for data obtained with the new approach (new: 39%, seated: 70%). Our simple approach enables measurement of STPF without the need for the restraints that are required for the conventional seated approach. These results suggest that the new approach is a valid method for measurement of STPF.

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