Monitoring of gait performance using dynamic time warping on IMU-sensor data

2016 ◽  
Author(s):  
Xingchen Wang ◽  
Maria Kyrarini ◽  
Danijela Ristic-Durrant ◽  
Matthias Spranger ◽  
Axel Graser
Keyword(s):  
Dynamic Time Warping ◽  
Sensor Data ◽  
Time Warping ◽  
Gait Performance ◽  
Dynamic Time

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

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1007 ◽  
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.

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

Sensors ◽  
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.

Fingers and Gesture Recognition with Kinect v2 Sensor

2016 ◽  
Vol 14 (3) ◽  
pp. 24-30
Author(s):  
A. Lekova ◽  
D. Ryan ◽  
R. Davidrajuh
Keyword(s):  
Gesture Recognition ◽  
Dynamic Time Warping ◽  
Sensor Data ◽  
Hand Gesture ◽  
Time Warping ◽  
Depth Sensor ◽  
Kinect V2 ◽  
Innovative Solutions ◽  
Finger Tracking ◽  
Dynamic Time

Abstract The paper presents enhancements and innovative solutions of the proposed in [3] algorithms for fingers tracking and hand gesture recognition based on new defined features describing hand gestures and exploiting new-tracked tip and thumb joints from Kinect v2 sensor. Dynamic Time Warping (DTW) algorithm is used for gestures recognition. We increased its accuracy, scale and rotational invariance by defining new 3D featuring angles describing gestures and used for training a gesture database. 3D positions for fingertips are extracted from depth sensor data and used for calculation of featuring angles between vectors. The provided by Kinect v2 3D positions for thumb, tip and hand joints also participates during the phases of recognition. A comparison with the latest published approach for finger tracking has been performed. The feasibility of the algorithms have been proven by real experiments.

scholarly journals Stride Segmentation during Free Walk Movements Using Multi-Dimensional Subsequence Dynamic Time Warping on Inertial Sensor Data

Sensors ◽  
2015 ◽  
Vol 15 (3) ◽  
pp. 6419-6440 ◽  
Author(s):  
Jens Barth ◽  
Cäcilia Oberndorfer ◽  
Cristian Pasluosta ◽  
Samuel Schülein ◽  
Heiko Gassner ◽  
...  
Keyword(s):  
Dynamic Time Warping ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Time Warping ◽  
Dynamic Time

Multichannel sparse spike deconvolution based on dynamic time warping

2021 ◽  
Author(s):  
Xiaowei Zhao ◽  
Shangxu Wang ◽  
Sanyi Yuan ◽  
Liang Cheng ◽  
Youjun Cai
Keyword(s):  
Dynamic Time Warping ◽  
Time Warping ◽  
Dynamic Time
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