Gait Recognition and Understanding Based on Hierarchical Temporal Memory Using 3D Gait Semantic Folding

Luo, Jian; Tjahjadi, Tardi

doi:10.3390/s20061646

Cited by 20 publications

(6 citation statements)

References 51 publications

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“…Several HTM use cases in the research studies [ 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 ] and practices that have already been commercially tested include server anomaly detection using Grok [ 70 ], stock volume anomalies [ 71 ], rogue human behavior [ 72 ], natural language prediction [ 73 ], and geospatial tracking [ 74 ].…”

Section: Related Workmentioning

confidence: 99%

IoT and Deep Learning-Based Farmer Safety System

Adhitya

Mulyani

Koeppen

et al. 2023

Sensors

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Farming is a fundamental factor driving economic development in most regions of the world. As in agricultural activity, labor has always been hazardous and can result in injury or even death. This perception encourages farmers to use proper tools, receive training, and work in a safe environment. With the wearable device as an Internet of Things (IoT) subsystem, the device can read sensor data as well as compute and send information. We investigated the validation and simulation dataset to determine whether accidents occurred with farmers by applying the Hierarchical Temporal Memory (HTM) classifier with each dataset input from the quaternion feature that represents 3D rotation. The performance metrics analysis showed a significant 88.00% accuracy, precision of 0.99, recall of 0.04, F_Score of 0.09, average Mean Square Error (MSE) of 5.10, Mean Absolute Error (MAE) of 0.19, and a Root Mean Squared Error (RMSE) of 1.51 for the validation dataset, 54.00% accuracy, precision of 0.97, recall of 0.50, F_Score of 0.66, MSE = 0.06, MAE = 3.24, and = 1.51 for the Farming-Pack motion capture (mocap) dataset. The computational framework with wearable device technology connected to ubiquitous systems, as well as statistical results, demonstrate that our proposed method is feasible and effective in solving the problem’s constraints in a time series dataset that is acceptable and usable in a real rural farming environment for optimal solutions.

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Section: Related Workmentioning

confidence: 99%

IoT and Deep Learning-Based Farmer Safety System

Adhitya

Mulyani

Koeppen

et al. 2023

Sensors

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“…Luo et al. [15] targeted difficulties in real three‐dimensional structured data and proposed a hierarchical temporal memory network using convolution neural network and recurrent neural network. Their proposed method uses an estimation technique to detect body shape, images of body‐parsing, and virtual garments.…”

Section: Literature Reviewmentioning

confidence: 99%

A robust covariate‐invariant gait recognition based on pose features

Parashar

Shekhawat

2022

IET Biometrics

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Gait recognition uses video of human gait processed by computer vision methods to identify people based on walking style. The complexity introduced by covariates makes the previous methods less efficient and inaccurate. This study proposes an approach based on pose features to attempt gait recognition of people with an overcoat, carrying objects, or other covariates. It aims to estimate human locomotion using Convolutional Neural Networks. Gathering video data, extracting video frames in a particular order, posture estimation for each frame, using multilayer RNN for gait recognition from the pose, and obtaining one‐dimensional object vectors, are all critical steps. Furthermore, these one‐dimensional identification vectors are stored in a data set along with the name of the person walking in the video. The proposed data set is used to train a classification model to predict the person in a new video by first processing it to get its identification vector and then to use it as a test case in the classification model. A graphical user interface was also developed so that anyone with no programming or technical experience can easily use the tool. The developed application does everything for gait detection from mp4 videos by obtaining the identification vectors and saving them into the data set. Using this application, one can quickly identify the person walking in a video. The results obtained offered an accuracy from 60.88% to 95.23%.

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“…Krestinskaya et al develop circuits and systems to achieve the optimized design of an HTM SP, an HTM TM, and a memristive analog pattern matcher for pattern recognition applications [13]. SPL abstracts the input features through a hierarchical structure [14], which makes HTM have wideranging applications in recognition and classification, such as data classification [15], face recognition [16], speech recognition [17], biometric recognition [18], detection of multiple objects located in clutter color images [19], handwriting recognition [20], action recognition [21], gait recognition and understanding [22], and natural language processing [23].…”

Section: Htm Is a New Artificial Neural Network Model Based On Jeffmentioning

confidence: 99%

A Temporal Pool Learning Algorithm Based on Location Awareness

Zhu

Cai

et al. 2021

Scientific Programming

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Hierarchical Temporal Memory is a new type of artificial neural network model, which imitates the structure and information processing flow of the human brain. Hierarchical Temporal Memory has strong adaptability and fast learning ability and becomes a hot spot in current research. Hierarchical Temporal Memory obtains and saves the temporal characteristics of input sequences by the temporal pool learning algorithm. However, the current algorithm has some problems such as low learning efficiency and poor learning effect when learning time series data. In this paper, a temporal pool learning algorithm based on location awareness is proposed. The cell selection rules based on location awareness and the dendritic updating rules based on adjacent inputs are designed to improve the learning efficiency and effect of the algorithm. Through the algorithm prototype, three different datasets are used to test and analyze the algorithm performance. The experimental results verify that the algorithm can quickly obtain the complete characteristics of the input sequence. No matter whether there are similar segments in the sequence, the proposed algorithm has higher prediction recall and precision than the existing algorithms.

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Gait Recognition and Understanding Based on Hierarchical Temporal Memory Using 3D Gait Semantic Folding

Cited by 20 publications

References 51 publications

IoT and Deep Learning-Based Farmer Safety System

IoT and Deep Learning-Based Farmer Safety System

A robust covariate‐invariant gait recognition based on pose features

A Temporal Pool Learning Algorithm Based on Location Awareness

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