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.

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 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

scholarly journals FIELD-PROGRAMMABLE GATE ARRAY IMPLEMENTATION OF THE DYNAMIC TIME WARPING ALGORITHM FOR SPEECH RECOGNITION

2017 ◽  
Vol 10 (13) ◽  
pp. 248
Author(s):  
John Sahaya Rani Alex ◽  
Mitali Bhojwani
Keyword(s):  
Speech Recognition ◽  
Field Programmable Gate Array ◽  
Dynamic Time Warping ◽  
Recognition Algorithm ◽  
Consumer Electronics ◽  
Time Warping ◽  
Time To Market ◽  
Computing Power ◽  
Field Programmable ◽  
Dynamic Time

Objective of this research is to implement a speech recognition algorithm in smaller form factor device. Speech recognition is an extensively used inmobile and in numerous consumer electronics devices. Dynamic time warping (DTW) method which is based on dynamic programming is chosen tobe implemented for speech recognition because of the latest trend in evolving computing power. Implementation of DTW in field-programmable gatearray is chosen for its featured flexibility, parallelization and shorter time to market. The above algorithm is implemented using Verilog on Xilinx ISE.The warping cost is less if the similarity is found and is more for dissimilar sequences which is verified in the simulation output. The results indicatethat real time implementation of DTW based speech recognition could be done in future.

Study of English Pronunciation Quality Evaluation System with Tone and Emotion Analysis Capabilities

2013 ◽  
Vol 475-476 ◽  
pp. 318-323
Author(s):  
Xin Guang Li ◽  
Su Mei Li ◽  
Li Rui Jiang ◽  
Sheng Bin Zhang
Keyword(s):  
Neural Network ◽  
Dynamic Time Warping ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Probabilistic Neural Network ◽  
Recognition Algorithm ◽  
Time Warping ◽  
English Sentence ◽  
Dynamic Time ◽  
Quality Evaluation System

During the study of English sentence pronunciation evaluation system, we found that sentence pronunciation emotion and intonation evaluation are very important. Probabilistic neural network has been used to study English sentence pronunciation emotion, and DTW (Dynamic Time Warping) algorithm has been used in the intonation analysis. The probability neural network basic principle is introduced in this paper. An emotion recognition algorithm based on MFCC（Mel　Frequency　Cepstrum　Coefficient）is present. The keynote and energy of the sentences are used to analyse the accuracy of the tones.　The experimental results of the proposed method effectiveness are given.

The Research of Judging the Phenomenon of Roof Separation Based on Principle of Dynamic Time Warping Algorithm

2014 ◽  
Vol 945-949 ◽  
pp. 2187-2190
Author(s):  
Dan Dan Liu ◽  
Guang Cai Qiu ◽  
Hua Long Che
Keyword(s):  
Dynamic Time Warping ◽  
Sequence Data ◽  
Recognition Algorithm ◽  
The State ◽  
Time Warping ◽  
Voice Signal ◽  
The Status ◽  
Model Database ◽  
Dynamic Time ◽  
The Voice

Applying Wall tapping and roof sounding for coal blasting mining, excavation face to determine whether the status of roadway roof separation occurs. Firstly through beating the different parts of rock layers and record the received voice signal, which do the DTW feature extraction of the voice signal. Secondly based on the model database of constructed roof separated layer, determining the state of the roof combining with BP neural network recognition algorithm, after the extraction of DTW feature and the sequence data generated. Finally simulation and experiments show that identification method based on DTW, determining the state of the roof characteristic. Whether the roof separation of roadway occurs, to avoid the misjudgment problems of lack of experience in practical works.

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.

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