Hybrid Deep-Readout Echo State Network and Support Vector Machine with Feature Selection for Human Activity Recognition

2021 ◽  
pp. 150-167
Author(s):  
Shadi Abudalfa ◽  
Kevin Bouchard
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Support Vector ◽  
Echo State Network ◽  
Selection For

scholarly journals HARTH: A Human Activity Recognition Dataset for Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7853
Author(s):  
Aleksej Logacjov ◽  
Kerstin Bach ◽  
Atle Kongsvold ◽  
Hilde Bremseth Bårdstu ◽  
Paul Jarle Mork
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Human Activity Recognition ◽  
Support Vector ◽  
Free Living

Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera’s video signal and achieved high inter-rater agreement (Fleiss’ Kappa =0.96). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of 0.81 (standard deviation: ±0.18), recall of 0.85±0.13, and precision of 0.79±0.22 in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living.

scholarly journals Human activity recognition using support vector machine for automatic security system

2019 ◽  
Vol 1192 ◽  
pp. 012017
Author(s):  
Taufiq Bagaskara Supriyatna ◽  
Surya Michrandi Nasution ◽  
Ratna Astuti Nugraheni
Keyword(s):  
Support Vector Machine ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Security System ◽  
Support Vector

scholarly journals Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 317 ◽  
Author(s):  
Nadeem Ahmed ◽  
Jahir Ibna Rafiq ◽  
Md Rashedul Islam
Keyword(s):  
Feature Selection ◽  
Activity Recognition ◽  
Human Activity ◽  
Selection Process ◽  
Human Life ◽  
Elderly Care ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Support Vector ◽  
Feature Vectors

Human activity recognition (HAR) techniques are playing a significant role in monitoring the daily activities of human life such as elderly care, investigation activities, healthcare, sports, and smart homes. Smartphones incorporated with varieties of motion sensors like accelerometers and gyroscopes are widely used inertial sensors that can identify different physical conditions of human. In recent research, many works have been done regarding human activity recognition. Sensor data of smartphone produces high dimensional feature vectors for identifying human activities. However, all the vectors are not contributing equally for identification process. Including all feature vectors create a phenomenon known as ‘curse of dimensionality’. This research has proposed a hybrid method feature selection process, which includes a filter and wrapper method. The process uses a sequential floating forward search (SFFS) to extract desired features for better activity recognition. Features are then fed to a multiclass support vector machine (SVM) to create nonlinear classifiers by adopting the kernel trick for training and testing purpose. We validated our model with a benchmark dataset. Our proposed system works efficiently with limited hardware resource and provides satisfactory activity identification.

scholarly journals Human activity recognition using skeleton data and support vector machine

2019 ◽  
Vol 1192 ◽  
pp. 012044 ◽  
Author(s):  
Mandira G A Komang ◽  
Michrandi N Surya ◽  
Astuti N Ratna
Keyword(s):  
Support Vector Machine ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Support Vector

scholarly journals A Method of Human Activity Recognition in Transitional Period

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 416 ◽  
Author(s):  
Lei Chen ◽  
Shurui Fan ◽  
Vikram Kumar ◽  
Yating Jia
Keyword(s):  
Support Vector Machine ◽  
Activity Recognition ◽  
Human Activity ◽  
Inertial Sensor ◽  
Human Activity Recognition ◽  
Transitional Period ◽  
Sensor Data ◽  
Support Vector ◽  
Feature Subset ◽  
Posture Classification

Human activity recognition (HAR) has been increasingly used in medical care, behavior analysis, and entertainment industry to improve the experience of users. Most of the existing works use fixed models to identify various activities. However, they do not adapt well to the dynamic nature of human activities. We investigated the activity recognition with postural transition awareness. The inertial sensor data was processed by filters and we used both time domain and frequency domain of the signals to extract the feature set. For the corresponding posture classification, three feature selection algorithms were considered to select 585 features to obtain the optimal feature subset for the posture classification. And We adopted three classifiers (support vector machine, decision tree, and random forest) for comparative analysis. After experiments, the support vector machine gave better classification results than other two methods. By using the support vector machine, we could achieve up to 98% accuracy in the Multi-class classification. Finally, the results were verified by probability estimation.

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