Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm

Zhou, Hui; Ji, Ning; Samuel, Oluwarotimi Williams; Cao, Yukun; Zhao, Zhe-Yi; Chen, Shixiong; Li, Guanglin

doi:10.3390/s16101634

Cited by 45 publications

(41 citation statements)

References 30 publications

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“…where, ja S ( j) is the comprehensive change rate of acceleration, after smooth filtering, while the window size, considered in this study, is 30 ms [37].…”

Section: Preliminary Segmentationmentioning

confidence: 99%

“…Acceleration signals, collected by inertial sensors, have been widely used to divide the two gait events of HS and TO [35][36][37]. Studies have proved that the threshold of comprehensive change rate of the acceleration signal can be used to determine HS and TO events, but the final result will be affected by the degree of smoothing filtering of acceleration signals [38].…”

Section: Introductionmentioning

confidence: 99%

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A Determination Method for Gait Event Based on Acceleration Sensors

Mei

Gao

Liu

2019

Sensors

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A gait event is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. However, for the data acquisition of a three-dimensional motion capture (3D Mo-Cap) system, the high cost of setups, such as the high standard laboratory environment, limits widespread clinical application. Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. Inertial sensors are now sufficiently small in size and light in weight to be part of a body sensor network for the collection of human gait data. The acceleration signal has found important applications in human gait recognition. In this paper, using the experimental data from the heel and toe, first the wavelet method was used to remove noise from the acceleration signal, then, based on the threshold of comprehensive change rate of the acceleration signal, the signal was primarily segmented. Subsequently, the vertical acceleration signals, from heel and toe, were integrated twice, to compute their respective vertical displacement. Four gait events were determined in the segmented signal, based on the characteristics of the vertical displacement of heel and toe. The results indicated that the gait events were consistent with the synchronous record of the motion capture system. The method has achieved gait event subdivision, while it has also ensured the accuracy of the defined gait events. The work acts as a valuable reference, to further study gait recognition.

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“…where, ja S ( j) is the comprehensive change rate of acceleration, after smooth filtering, while the window size, considered in this study, is 30 ms [37].…”

Section: Preliminary Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Determination Method for Gait Event Based on Acceleration Sensors

Mei

Gao

Liu

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition, comparison of temporal gait parameters and Tinetti gait and balance average scores between the two groups of young and pre-fail older adults has been performed [160]. Real-time Detection of gait events (HS, TO) based on single accelerometer placed on the right lower leg considering different terrains has been performed using time-frequency analysis and Peak Heuristics Algorithm [161]. The walking conditions include level walking as well as upstairs and downstairs walking.…”

Section: A Platforms For Validationmentioning

confidence: 99%

A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications

Jarchi

Pope

Lee

et al. 2018

IEEE Rev. Biomed. Eng.

170

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Gait analysis continues to be an important technique for many clinical applications to diagnose and monitor certain diseases. Many mental and physical abnormalities cause measurable differences in a person's gait. Gait analysis has applications in sport, computer games, physical rehabilitation, clinical assessment, surveillance, human recognition, modeling, and many other fields. There are established methods using various sensors for gait analysis, of which accelerometers are one of the most often employed. Accelerometer sensors are generally more user friendly and less invasive. In this paper, we review research regarding accelerometer sensors used for gait analysis with particular focus on clinical applications. We provide a brief introduction to accelerometer theory followed by other popular sensing technologies. Commonly used gait phases and parameters are enumerated. The details of selecting the papers for review are provided. We also review several gait analysis software. Then we provide an extensive report of accelerometry-based gait analysis systems and applications, with additional emphasis on trunk accelerometry. We conclude this review with future research directions.

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“…Zhou et al and Bayón et al used the three acceleration axes (x-, y-, and z-axes) data acquired using a smartphone to identify three walking modes using a one-dimensional convolutional neural network algorithm. (13,14) Because the mobile phone was placed in the hand or pocket during data collection, the movement of the hand or pocket during walking made it difficult to accurately measure the gait data. Altilio et al and Aoike et al used the decision tree algorithm to classify the gait types of the elderly using pressure sensors, acceleration, and gyro data.…”

Section: Introductionmentioning

confidence: 99%

Recognition of the Gait Phase Based on New Deep Learning Algorithm Using Multisensor Information Fusion

Wang¹,

Yan²,

Jiang³

2019

Sensors and Materials

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Gait phase recognition is an effective method of analyzing human motion and behavior that can be very meaningful in people's daily life, especially when struggling with assisted rehabilitation. In this paper, a new algorithm that can recognize a human gait phase more accurately is proposed. The new gait phase recognition algorithm is based on a deep memory convolutional neural network (DM-CNN) using multiple sensor fusion. We used the plantar pressure sensor array and acceleration sensor array gait data, and then extracted the gait features using the DM-CNN. The measured data of the continuous gait cycle were divided into unit steps, and the data were analyzed and preprocessed. Then, a feature map of each sensor array was extracted by constructing a separate DM-CNN. Finally, each feature map was combined into a fully connected network, and a memory function was introduced to simulate historical behavior. We then tested the algorithm on the phases of a gait cycle and compared the evaluation indicators of each phase. In the experiment, we compared single-mode and multimode recognition results, and compared those with the new hidden Markov model (N-HMM), K-nearest neighbor (KNN), and hidden Markov model (HMM) algorithms. The experimental results show that when the multisensor data are fused, the average recognition accuracy can reach 97.1%, which is higher than those of the other algorithms and improves the recognition of a human gait phase. The accurate recognition of human gait can provide a better theoretical basis for the design of exoskeleton robot control strategies.

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Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm

Cited by 45 publications

References 30 publications

A Determination Method for Gait Event Based on Acceleration Sensors

A Determination Method for Gait Event Based on Acceleration Sensors

A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications

Recognition of the Gait Phase Based on New Deep Learning Algorithm Using Multisensor Information Fusion

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