Study of Zero Velocity Update for Both Low- and High-Speed Human Activities

2012 ◽  
pp. 65-84
Author(s):  
R. Zhang ◽  
M. Loschonsky ◽  
L.M. Reindl
Keyword(s):  
Human Activities ◽  
High Speed ◽  
Inertial Sensor ◽  
Stair Climbing ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Fast Walking ◽  
Zero Velocity ◽  
Wide Range ◽  
Algorithm Implementation

Previous studies show that inertial sensor-based personal positioning benefited from Zero Velocity Update (ZUPT) method by resetting the foot speed at every foot step. However, only the solution for normal pedestrian movement with small velocity like walking was given. This paper presents a novel ZUPT system which can be used in a wide range of human activities, including walking, running, and stair climbing by using two inertial measurement unit (IMU) modules. One is attached on the centre of the human body for human activities’ classification and recognition. The other one is mounted on the foot for ZUPT algorithm implementation based on the result of activities’ recognition. Test cases include stair climbing by walking and running, walking, fast walking, and running. In all cases, most of the steps are able to be detected and the new ZUPT system can be successfully implemented.

Study of Zero Velocity Update for Both Low- and High-Speed Human Activities

2011 ◽  
Vol 2 (2) ◽  
pp. 46-67 ◽  
Author(s):  
R. Zhang ◽  
M. Loschonsky ◽  
L.M. Reindl
Keyword(s):  
Human Activities ◽  
Inertial Measurement Unit ◽  
High Speed ◽  
Inertial Sensor ◽  
Stair Climbing ◽  
Measurement Unit ◽  
Test Cases ◽  
Zero Velocity ◽  
Wide Range ◽  
Algorithm Implementation

Previous studies show that inertial sensor-based personal positioning benefited from Zero Velocity Update (ZUPT) method by resetting the foot speed at every foot step. However, only the solution for normal pedestrian movement with small velocity like walking was given. This paper presents a novel ZUPT system which can be used in a wide range of human activities, including walking, running, and stair climbing by using two inertial measurement unit (IMU) modules. One is attached on the centre of the human body for human activities’ classification and recognition. The other one is mounted on the foot for ZUPT algorithm implementation based on the result of activities’ recognition. Test cases include stair climbing by walking and running, walking, fast walking, and running. In all cases, most of the steps are able to be detected and the new ZUPT system can be successfully implemented.

New method to evaluate three-dimensional push-off angle during short-track speed skating using wearable inertial measurement unit sensors

2019 ◽  
Vol 233 (4) ◽  
pp. 476-480 ◽  
Author(s):  
Kyungsoo Kim ◽  
Jun Seok Kim ◽  
Tserenchimed Purevsuren ◽  
Batbayar Khuyagbaatar ◽  
SuKyoung Lee ◽  
...  
Keyword(s):  
Motion Analysis ◽  
Inertial Measurement Unit ◽  
High Speed ◽  
Three Dimensional ◽  
Analysis Data ◽  
New Method ◽  
Measurement Unit ◽  
Angle Estimation ◽  
Inertial Measurement ◽  
Speed Skating

The push-off mechanism to generate forward movement in skating has been analyzed by using high-speed cameras and specially designed skates because it is closely related to skater performance. However, using high-speed cameras for such an investigation, it is hard to measure the three-dimensional push-off force, and a skate with strain gauges is difficult to implement in the real competitions. In this study, we provided a new method to evaluate the three-dimensional push-off angle in short-track speed skating based on motion analysis using a wearable motion analysis system with inertial measurement unit sensors to avoid using a special skate or specific equipment insert into the skate for measurement of push-off force. The estimated push-off angle based on motion analysis data was very close to that based on push-off force with a small root mean square difference less than 6% when using the lateral marker in the left leg and the medial marker in the right leg regardless of skating phase. These results indicated that the push-off angle estimation based on motion analysis data using a wearable motion capture system of inertial measurement unit sensors could be acceptable for realistic situations. The proposed method was shown to be feasible during short-track speed skating. This study is meaningful because it can provide a more acceptable push-off angle estimation in real competitive situations.

Stride segmentation of inertial sensor data using statistical methods for different walking activities

Robotica ◽  
2021 ◽  
pp. 1-14
Author(s):  
Rahul Jain ◽  
Vijay Bhaskar Semwal ◽  
Praveen Kaushik
Keyword(s):  
Autocorrelation Function ◽  
Electronic Device ◽  
Inertial Sensor ◽  
Linear Acceleration ◽  
Sensor Data ◽  
Human Gait ◽  
Tuning Parameter ◽  
Measurement Unit ◽  
Specific Information ◽  
Inertial Measurement

Abstract Human gait data can be collected using inertial measurement units (IMUs). An IMU is an electronic device that uses an accelerometer and gyroscope to capture three-axial linear acceleration and three-axial angular velocity. The data so collected are time series in nature. The major challenge associated with these data is the segmentation of signal samples into stride-specific information, that is, individual gait cycles. One empirical approach for stride segmentation is based on timestamps. However, timestamping is a manual technique, and it requires a timing device and a fixed laboratory set-up which usually restricts its applicability outside of the laboratory. In this study, we have proposed an automatic technique for stride segmentation of accelerometry data for three different walking activities. The autocorrelation function (ACF) is utilized for the identification of stride boundaries. Identification and extraction of stride-specific data are done by devising a concept of tuning parameter ( $t_{p}$ ) which is based on minimum standard deviation ( $\sigma$ ). Rigorous experimentation is done on human activities and postural transition and Osaka University – Institute of Scientific and Industrial Research gait inertial sensor datasets. Obtained mean stride duration for level walking, walking upstairs, and walking downstairs is 1.1, 1.19, and 1.02 s with 95% confidence interval [1.08, 1.12], [1.15, 1.22], and [0.97, 1.07], respectively, which is on par with standard findings reported in the literature. Limitations of accelerometry and ACF are also discussed. stride segmentation; human activity recognition; accelerometry; gait parameter estimation; gait cycle; inertial measurement unit; autocorrelation function; wearable sensors; IoT; edge computing; tinyML.

Wireless Input Technology Based on MEMS Inertial Measurement Unit - A Survey

2017 ◽  
Vol 870 ◽  
pp. 79-84
Author(s):  
Zhen Xian Fu ◽  
Guang Ying Zhang ◽  
Yu Rong Lin ◽  
Yang Liu
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Propagation Model ◽  
Measurement Unit ◽  
Input Device ◽  
Operational Environment ◽  
Inertial Measurement ◽  
Mems Imu

Rapid progress in Micro-Electromechanical System (MEMS) technique is making inertial sensors increasingly miniaturized, enabling it to be widely applied in people’s everyday life. Recent years, research and development of wireless input device based on MEMS inertial measurement unit (IMU) is receiving more and more attention. In this paper, a survey is made of the recent research on inertial pens based on MEMS-IMU. First, the advantage of IMU-based input is discussed, with comparison with other types of input systems. Then, based on the operation of an inertial pen, which can be roughly divided into four stages: motion sensing, error containment, feature extraction and recognition, various approaches employed to address the challenges facing each stage are introduced. Finally, while discussing the future prospect of the IMU-based input systems, it is suggested that the methods of autonomous and portable calibration of inertial sensor errors be further explored. The low-cost feature of an inertial pen makes it desirable that its calibration be carried out independently, rapidly, and portably. Meanwhile, some unique features of the operational environment of an inertial pen make it possible to simplify its error propagation model and expedite its calibration, making the technique more practically viable.

Tracking Using an Inertial Measurement Unit (IMU) and GPS

2012 ◽  
Vol 229-231 ◽  
pp. 1469-1475 ◽  
Author(s):  
Hussein M. Magboub ◽  
Mohamed A. Msallem ◽  
Nasser Ali
Keyword(s):  
Inertial Measurement Unit ◽  
Simulation Software ◽  
Measurement Unit ◽  
Matlab Simulation ◽  
Tracking Accuracy ◽  
Inertial Measurement ◽  
Wide Range ◽  
Different Types ◽  
Significant Performance ◽  
Significant Attention

Since the last decade, vehicle tracking has been attracting significant attention in a wide range of applications. To deliver on their requirements, these applications need a specific tracking accuracy. However, current tracking techniques lack the required accuracy, especially for mission critical applications. Although these techniques have demonstrated significant performance improvement, there remain situations that give rise to degraded tracking accuracy, a deficiency that many applications cannot tolerate. This has motivated the research and development of advanced tracking. In this paper will be the design and implementation of an inertial navigation system (INS) using an inertial measurement unit (IMU) and GPS by Matlab simulation software. The INS is capable of providing continuous estimates of a vehicle’s position and orientation. And Comparative study of different types of estimation filters (KF, EKF) which has high accuracy is used to improve system state estimation.

scholarly journals Machine Learning to Improve Orientation Estimation in Sports Situations Challenging for Inertial Sensor Use

2021 ◽  
Vol 3 ◽  
Author(s):  
Marit P. van Dijk ◽  
Manon Kok ◽  
Monique A. M. Berger ◽  
Marco J. M. Hoozemans ◽  
DirkJan H. E. J. Veeger
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Inertial Sensor ◽  
Measurement Unit ◽  
Body Segment ◽  
Inertial Measurement ◽  
Wheelchair Sports ◽  
Trunk Inclination ◽  
Better Than

In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a small correction magnitude is beneficial from instances in which a large correction magnitude is beneficial, to eventually arrive at accurate body segment orientations in IMU-challenging sports situations. A machine learning algorithm was trained to make this distinction based on raw IMU data. Experiments on wheelchair sports were performed to assess the validity of the extended MW filter, and to compare the extended MW filter with the original MW filter based on comparisons with a motion capture-based reference system. Results indicate that the extended MW filter performs better than the original MW filter in assessing instantaneous trunk inclination (7.6 vs. 11.7° root-mean-squared error, RMSE), especially during the dynamic, IMU-challenging situations with moving athlete and wheelchair. Improvements of up to 45% RMSE were obtained for the extended MW filter compared with the original MW filter. To conclude, the machine learning-based extended MW filter has an acceptable accuracy and performs better than the original MW filter for the assessment of body segment orientation in IMU-challenging sports situations.

Experimental research on the muzzle response characteristics of small unmanned ground vehicles with small arms

2021 ◽  
pp. 095440622110570
Author(s):  
Yugang Ding ◽  
Kedong Zhou ◽  
Lei He ◽  
Haomin Yang
Keyword(s):  
High Speed ◽  
Threshold Function ◽  
Measurement Unit ◽  
Unmanned Ground Vehicles ◽  
Vehicle Speed ◽  
Ground Vehicles ◽  
Small Arms ◽  
Inertial Measurement ◽  
Variable Threshold ◽  
Response Characteristics

The muzzle response is the main feature affecting the firing accuracy of weapons. To research the muzzle response characteristics of small unmanned ground vehicles with small arms (SUGVsSA) during shooting, this paper designs a test method that combines an inertial measurement system (IMS) with a high-speed photogrammetric system (HSPS) to measure the muzzle response. That is, an inertial measurement unit (IMU) is fixed onto the gun body to record the three-dimensional angular motion of the barrel; meanwhile, a high-speed camera is used to capture the characteristic markers of the unmanned ground vehicle from the side. After data processing, the muzzle response curves during four consecutive firings when the vehicle is running at different speeds and firing angles are obtained. Considering the presence of noise in muzzle response signals, the wavelet threshold de-noising (WTD) algorithm based on a novel variable threshold function is used to de-noise the test signal. The processing results demonstrate that the WTD algorithm based on the novel variable threshold function can not only suppress noise in the muzzle response signal but also retain the local details of the signal. The combination of the IMS and HSPS complements the muzzle response data and can comprehensively and accurately reflect the muzzle response characteristics of SUGVsSA. As the vehicle speed and firing angle increase, the muzzle vibration intensifies, only when the vehicle speed is 0.3 m/s, and the muzzle maximum elevation angle displacement after each firing decreases when it is stationary. The results presented in this paper may provide a workable reference for understanding the muzzle response characteristics of SUGVsSA and evaluating the firearm compatibility of other unmanned systems.

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