Differences in Motion Accuracy of Baduanjin between Novice and Senior Students on Inertial Sensor Measurement Systems

This study aimed to evaluate the motion accuracy of novice and senior students in Baduanjin (a traditional Chinese sport) using an inertial sensor measurement system (IMU). Study participants were nine novice students, 11 senior students, and a teacher. The motion data of all participants were measured three times with the IMU. Using the motions of the teacher as the standard motions, we used dynamic time warping to calculate the distances between the motion data of the students and the teacher to evaluate the motion accuracy of the students. The distances between the motion data of the novice students and the teacher were higher than that between senior students and the teacher (p < 0.05 or p < 0.01). These initial results showed that the IMU and the corresponding mathematical methods could effectively distinguish the differences in motion accuracy between novice and senior students of Baduanjin.

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A Differential Evolution Approach to Optimize Weights of Dynamic Time Warping for Multi-Sensor Based Gesture Recognition

Sensors ◽

10.3390/s19051007 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1007 ◽

Cited By ~ 5

Author(s):

James Rwigema ◽

Hyo-Rim Choi ◽

TaeYong Kim

Keyword(s):

Differential Evolution ◽

Gesture Recognition ◽

Dynamic Time Warping ◽

Inertial Sensor ◽

Human Action ◽

Sensor Data ◽

Time Warping ◽

Optimal Values ◽

Dynamic Time ◽

Evolution Approach

In this research, we present a differential evolution approach to optimize the weights of dynamic time warping for multi-sensory based gesture recognition. Mainly, we aimed to develop a robust gesture recognition method that can be used in various environments. Both a wearable inertial sensor and a depth camera (Kinect Sensor) were used as heterogeneous sensors to verify and collect the data. The proposed approach was used for the calculation of optimal weight values and different characteristic features of heterogeneous sensor data, while having different effects during gesture recognition. In this research, we studied 27 different actions to analyze the data. As finding the optimal value of the data from numerous sensors became more complex, a differential evolution approach was used during the fusion and optimization of the data. To verify the performance accuracy of the presented method in this study, a University of Texas at Dallas Multimodal Human Action Datasets (UTD-MHAD) from previous research was used. However, the average recognition rates presented by previous research using respective methods were still low, due to the complexity in the calculation of the optimal values of the acquired data from sensors, as well as the installation environment. Our contribution was based on a method that enabled us to adjust the number of depth cameras and combine this data with inertial sensors (multi-sensors in this study). We applied a differential evolution approach to calculate the optimal values of the added weights. The proposed method achieved an accuracy 10% higher than the previous research results using the same database, indicating a much improved accuracy rate of motion recognition.

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Motion Classification and Features Recognition of a Traditional Chinese Sport (Baduanjin) Using Sampled-Based Methods

Applied Sciences ◽

10.3390/app11167630 ◽

2021 ◽

Vol 11 (16) ◽

pp. 7630

Author(s):

Hai Li ◽

Hwa Jen Yap ◽

Selina Khoo

Keyword(s):

Neural Network ◽

Processing Time ◽

Nearest Neighbor ◽

Inertial Sensor ◽

K Nearest Neighbor ◽

Motion Classification ◽

Motion Recognition ◽

One Dimensional ◽

Motion Data ◽

Sensor Measurement

This study recognized the motions and assessed the motion accuracy of a traditional Chinese sport (Baduanjin), using the data from the inertial sensor measurement system (IMU) and sampled-based methods. Fifty-three participants were recruited in two batches to participate in the study. Motion data of participants practicing Baduanjin were captured by IMU. By extracting features from motion data and benchmarking with the teacher’s assessment of motion accuracy, this study verifies the effectiveness of assessment on different classifiers for motion accuracy of Baduanjin. Moreover, based on the extracted features, the effectiveness of Baduanjin motion recognition on different classifiers was verified. The k-Nearest Neighbor (k-NN), as a classifier, has advantages in accuracy (more than 85%) and a short average processing time (0.008 s) during assessment. In terms of recognizing motions, the classifier One-dimensional Convolutional Neural Network (1D-CNN) has the highest accuracy among all verified classifiers (99.74%). The results show, using the extracted features of the motion data captained by IMU, that selecting an appropriate classifier can effectively recognize the motions and, hence, assess the motion accuracy of Baduanjin.

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Template-Based Recognition of Human Locomotion in IMU Sensor Data Using Dynamic Time Warping

Sensors ◽

10.3390/s21082601 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2601

Author(s):

Kim S. Sczuka ◽

Marc Schneider ◽

Alan K. Bourke ◽

Sabato Mellone ◽

Ngaire Kerse ◽

...

Keyword(s):

Dynamic Time Warping ◽

Inertial Sensor ◽

Vigorous Physical Activity ◽

Human Locomotion ◽

Recognition Algorithm ◽

Stair Climbing ◽

Sensor Data ◽

Time Warping ◽

Lower Back ◽

Dynamic Time

Increased levels of light, moderate and vigorous physical activity (PA) are positively associated with health benefits. Therefore, sensor-based human activity recognition can identify different types and levels of PA. In this paper, we propose a two-layer locomotion recognition method using dynamic time warping applied to inertial sensor data. Based on a video-validated dataset (ADAPT), which included inertial sensor data recorded at the lower back (L5 position) during an unsupervised task-based free-living protocol, the recognition algorithm was developed, validated and tested. As a first step, we focused on the identification of locomotion activities walking, ascending and descending stairs. These activities are difficult to differentiate due to a high similarity. The results showed that walking could be recognized with a sensitivity of 88% and a specificity of 89%. Specificity for stair climbing was higher compared to walking, but sensitivity was noticeably decreased. In most cases of misclassification, stair climbing was falsely detected as walking, with only 0.2–5% not assigned to any of the chosen types of locomotion. Our results demonstrate a promising approach to recognize and differentiate human locomotion within a variety of daily activities.

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