scholarly journals Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm

Algorithms ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 253 ◽  
Author(s):  
Tao Zhen ◽  
Lei Yan ◽  
Peng Yuan
Keyword(s):  
Short Term Memory ◽  
Disease Diagnosis ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Data Set ◽  
Softmax Classifier ◽  
Acceleration Sensors ◽  
Gait Phase ◽  
Phase Recognition ◽  
Acceleration Signals

Gait phase detection is a new biometric method which is of great significance in gait correction, disease diagnosis, and exoskeleton assisted robots. Especially for the development of bone assisted robots, gait phase recognition is an indispensable key technology. In this study, the main characteristics of the gait phases were determined to identify each gait phase. A long short-term memory-deep neural network (LSTM-DNN) algorithm is proposed for gate detection. Compared with the traditional threshold algorithm and the LSTM, the proposed algorithm has higher detection accuracy for different walking speeds and different test subjects. During the identification process, the acceleration signals obtained from the acceleration sensors were normalized to ensure that the different features had the same scale. Principal components analysis (PCA) was used to reduce the data dimensionality and the processed data were used to create the input feature vector of the LSTM-DNN algorithm. Finally, the data set was classified using the Softmax classifier in the full connection layer. Different algorithms were applied to the gait phase detection of multiple male and female subjects. The experimental results showed that the gait-phase recognition accuracy and F-score of the LSTM-DNN algorithm are over 91.8% and 92%, respectively, which is better than the other three algorithms and also verifies the effectiveness of the LSTM-DNN algorithm in practice.

scholarly journals Walking Gait Phase Detection Based on Acceleration Signals Using Voting-Weighted Integrated Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Lei Yan ◽  
Tao Zhen ◽  
Jian-Lei Kong ◽  
Lian-Ming Wang ◽  
Xiao-Lei Zhou
Keyword(s):  
Neural Network ◽  
Information Acquisition ◽  
Classification Accuracy ◽  
Back Propagation ◽  
Disease Diagnosis ◽  
Human Gait ◽  
Phase Detection ◽  
Efficient Design ◽  
Gait Phase ◽  
Acceleration Signals

Human gait phase recognition is a significant technology for rehabilitation training robot, human disease diagnosis, artificial prosthesis, and so on. The efficient design of the recognition method for gait information is the key issue in the current gait phase division and eigenvalues extraction research. In this paper, a novel voting-weighted integrated neural network (VWI-DNN) is proposed to detect different gait phases from multidimensional acceleration signals. More specifically, it first employs a gait information acquisition system to collect different IMU sensors data fixed on the human lower limb. Then, with dimensionality reduction and four-phase division preprocessing, key features are selected and merged as unified vectors to learn common and domain knowledge in time domain. Next, multiple refined DNNs are transferred to design a multistream integrated neural network, which utilizes the mixture-granularity information to exploit high-dimensional feature representative. Finally, a voting-weighted function is developed to fuse different submodels as a unified representation for distinguishing small discrepancy among different gait phases. The end-to-end implementation of the VWI-DNN model is fine-tuned by the loss optimization of gradient back-propagation. Experimental results demonstrate the outperforming performance of the proposed method with higher classification accuracy compared with the other methods, of which classification accuracy and macro-F1 is up to 99.5%. More discussions are provided to indicate the potential applications in combination with other works.

scholarly journals Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1081
Author(s):  
Tamon Miyake ◽  
Shintaro Yamamoto ◽  
Satoshi Hosono ◽  
Satoshi Funabashi ◽  
Zhengxue Cheng ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Detection System ◽  
Calibration Method ◽  
Detection Algorithm ◽  
Video Data ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Gait Phase ◽  
Logistic Regression Algorithm ◽  
Short Time

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

IMU-Based Gait Phase Recognition for Stroke Survivors

Robotica ◽  
2019 ◽  
Vol 37 (12) ◽  
pp. 2195-2208 ◽  
Author(s):  
Yu Lou ◽  
Rongli Wang ◽  
Jingeng Mai ◽  
Ninghua Wang ◽  
Qining Wang
Keyword(s):  
Inertial Measurement Unit ◽  
Detection System ◽  
Effective Means ◽  
Human Motion ◽  
Measurement Unit ◽  
Stroke Survivors ◽  
Phase Detection ◽  
Wearable Robots ◽  
Gait Phase ◽  
Phase Recognition

SummaryUsing wearable robots is an effective means of rehabilitation for stroke survivors, and effective recognition of human motion intentions is a key premise in controlling wearable robots. In this paper, we propose an inertial measurement unit (IMU)-based gait phase detection system. The system consists of two IMUs that are tied on the thigh and on the shank, respectively, for collecting acceleration and angular velocity. Features were extracted using a sliding window of 150 ms in length, which was then fed into a quadratic discriminant analysis (QDA) classifier for classification. We recruited five stroke survivors to test our system. They walked at their own preferred speed on the level ground. Experimental results show that our proposed system has the ability of recognizing the gait phase of stroke survivors. All recognition accuracy results are above 96.5%, and detections are about 5–15 ms in advance of time. In addition, using only one IMU can also give reliable recognition results. This paper proposes an idea about the further research on human–computer interaction for the control of wearable robots.

scholarly journals Surgical phase recognition by learning phase transitions

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Manish Sahu ◽  
Angelika Szengel ◽  
Anirban Mukhopadhyay ◽  
Stefan Zachow
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Phase Detection ◽  
Short Term ◽  
Video Frames ◽  
Usage Patterns ◽  
The Difference ◽  
Automatic Tool ◽  
Context Aware System ◽  
Phase Recognition

AbstractAutomatic recognition of surgical phases is an important component for developing an intra-operative context-aware system. Prior work in this area focuses on recognizing short-term tool usage patterns within surgical phases. However, the difference between intra- and inter-phase tool usage patterns has not been investigated for automatic phase recognition. We developed a Recurrent Neural Network (RNN), in particular a state-preserving Long Short Term Memory (LSTM) architecture to utilize the long-term evolution of tool usage within complete surgical procedures. For fully automatic tool presence detection from surgical video frames, a Convolutional Neural Network (CNN) based architecture namely ZIBNet is employed. Our proposed approach outperformed EndoNet by 8.1% on overall precision for phase detection tasks and 12.5% on meanAP for tool recognition tasks.

scholarly journals Gait Phase Estimation by Using LSTM in IMU-Based Gait Analysis—Proof of Concept

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5749
Author(s):  
Mustafa Sarshar ◽  
Sasanka Polturi ◽  
Lutz Schega
Keyword(s):  
Gait Analysis ◽  
Short Term Memory ◽  
Inertial Sensors ◽  
Validation Dataset ◽  
Phase Detection ◽  
Short Term ◽  
Velocity Magnitude ◽  
Gait Phase ◽  
Long Short Term Memory ◽  
Do So

Gait phase detection in IMU-based gait analysis has some limitations due to walking style variations and physical impairments of individuals. Therefore, available algorithms may not work properly when the gait data is noisy, or the person rarely reaches a steady state of walking. The aim of this work was to employ Artificial Intelligence (AI), specifically a long short-term memory (LSTM) algorithm, to overcome these weaknesses. Three supervised LSTM-based models were designed to estimate the expected gait phases, including foot-off (FO), mid-swing (MidS) and foot-contact (FC). For collecting gait data two tri-axial inertial sensors were located above each ankle. The angular velocity magnitude, rotation matrix magnitude and free acceleration magnitude were captured for data labeling and turning detection and to strengthen the model, respectively. To do so, a train dataset based on a novel movement protocol was acquired. A validation dataset similar to a train dataset was generated as well. Five test datasets from already existing data were also created to independently evaluate the models. After testing the models on validation and test datasets, all three models demonstrated promising performance in estimating desired gait phases. The proposed approach proves the possibility of employing AI-based algorithms to predict labeled gait phases from a time series of gait data.

scholarly journals Analysis of Japanese Expressions and Semantics Based on Link Sequence Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yanyan Shi ◽  
Yuting Liang
Keyword(s):  
Speech Recognition ◽  
Error Detection ◽  
Large Scale ◽  
Short Term Memory ◽  
Target Language ◽  
Classification Model ◽  
Syntactic Analysis ◽  
Detection Accuracy ◽  
Data Set ◽  
End To End

Based on locness corpus, this paper uses Wordsmith 6.0, SPSS 24, and other software to explore the use of temporal connectives in Japanese writing by Chinese Japanese learners. This paper proposes a method of tense classification based on the Japanese dependency structure. This method analyzes the results of the syntactic analysis of Japanese dependence and combines the tense characteristics of the target language to extract tense-related information and construct a maximum entropy tense classification model. The model can effectively identify the tense, and its classification accuracy shows the effectiveness of the classification method. This paper proposes a temporal feature extraction algorithm oriented to the hierarchical phrase expression model. The end-to-end speech recognition system has become the development trend of large-scale continuous speech recognition because of its simplicity and efficiency. In this paper, the end-to-end technology based on link timing classification is applied to Japanese speech recognition. Taking into account the characteristics of Japanese hiragana, katakana, and Japanese kanji writing forms, through experiments on the Japanese data set, different suggestions are explored. The final effect is better than mainstream speech recognition systems based on hidden Markov models and two-way long and short-term memory networks. This algorithm can extract the temporal characteristics of rules that meet certain conditions while extracting expression rules. These tense characteristics can guide the selection of rules in the expression process, make the expression results more in line with linguistic knowledge, and ensure the choice of relevant vocabulary and the structural ordering of the language. Through the analysis of time series and static information, we combine the time and space dimensions of the network structure. Using connectionist temporal classification (CTC) technology, an end-to-end speech recognition method for pronunciation error detection and diagnosis tasks is established. This method does not require phonemic information nor does it require forced alignment. The extended initials and finals are the error primitives, and 64 types of errors are designed. The experimental results show that the method can effectively detect the wrong pronunciation, the detection accuracy rate is 87.07%, the false rejection rate is 7.83%, and the error rate is 87.07%. The acceptance rate is 25.97%. This method uses network information more comprehensively than traditional methods, and the model is more effective. After detailed experiments, this article evaluates the prediction effect of this method and previous methods on the data set. This method improves the prediction accuracy by about 15% and achieves the expected goal of the work in this paper.

scholarly journals ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for Powered Prostheses

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2389 ◽  
Author(s):  
Huong Vu ◽  
Felipe Gomez ◽  
Pierre Cherelle ◽  
Dirk Lefeber ◽  
Ann Nowé ◽  
...  
Keyword(s):  
Gait Cycle ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Deep Learning Algorithm ◽  
Gait Phase ◽  
Validation Set ◽  
Fully Connected

Throughout the last decade, a whole new generation of powered transtibial prostheses and exoskeletons has been developed. However, these technologies are limited by a gait phase detection which controls the wearable device as a function of the activities of the wearer. Consequently, gait phase detection is considered to be of great importance, as achieving high detection accuracy will produce a more precise, stable, and safe rehabilitation device. In this paper, we propose a novel gait percent detection algorithm that can predict a full gait cycle discretised within a 1% interval. We called this algorithm an exponentially delayed fully connected neural network (ED-FNN). A dataset was obtained from seven healthy subjects that performed daily walking activities on the flat ground and a 15-degree slope. The signals were taken from only one inertial measurement unit (IMU) attached to the lower shank. The dataset was divided into training and validation datasets for every subject, and the mean square error (MSE) error between the model prediction and the real percentage of the gait was computed. An average MSE of 0.00522 was obtained for every subject in both training and validation sets, and an average MSE of 0.006 for the training set and 0.0116 for the validation set was obtained when combining all subjects’ signals together. Although our experiments were conducted in an offline setting, due to the forecasting capabilities of the ED-FNN, our system provides an opportunity to eliminate detection delays for real-time applications.

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