Logarithmic Incremental Parameter Tuning of Support Vector Machines for Human Activity Recognition

Recognizing human activity in real time with a limited dataset is possible on a resource-constrained device. However, most classification algorithms such as Support Vector Machines, C4.5, and K Nearest Neighbor require a large dataset to accurately predict human activities. In this paper, we present a novel real-time human activity recognition model based on Gaussian Naïve Bayes (GNB) algorithm using a personalized JavaScript object notation dataset extracted from the publicly available Physical Activity Monitoring for Aging People dataset and University of Southern California Human Activity dataset. With the proposed method, the personalized JSON training dataset is extracted and compressed into a 12×8 multi-dimensional array of the time-domain features extracted using a signal magnitude vector and tilt angles from tri-axial accelerometer sensor data. The algorithm is implemented on the Android platform using the Cordova cross-platform framework with HTML5 and JavaScript. Leave-one-activity-out cross validation is implemented as a testTrainer() function, the results of which are presented using a confusion matrix. The testTrainer() function leaves category K as the testing subset and the remaining K-1 as the training dataset to validate the proposed GNB algorithm. The proposed model is inexpensive in terms of memory and computational power owing to the use of a compressed small training dataset. Each K category was repeated five times and the algorithm consistently produced the same result for each test. The result of the simulation using the tilted angle features shows overall precision, recall, F-measure, and accuracy rates of 90%, 99.6%, 94.18%, and 89.51% respectively, in comparison to rates of 36.9%, 75%, 42%, and 36.9% when the signal magnitude vector features were used. The results of the simulations confirmed and proved that when using the tilt angle dataset, the GNB algorithm is superior to Support Vector Machines, C4.5, and K Nearest Neighbor algorithms.

Download Full-text

Human Activity Recognition Based on Motion Projection Profile Features in Surveillance Videos Using Support Vector Machines and Gaussian Mixture Models

Communications in Computer and Information Science - Security in Computing and Communications ◽

10.1007/978-3-319-22915-7_38 ◽

2015 ◽

pp. 412-423 ◽

Cited By ~ 1

Author(s):

J. Arunnehru ◽

M. Kalaiselvi Geetha

Keyword(s):

Support Vector Machines ◽

Mixture Models ◽

Activity Recognition ◽

Human Activity ◽

Gaussian Mixture Models ◽

Gaussian Mixture ◽

Support Vector ◽

Surveillance Videos ◽

Projection Profile ◽

Vector Machines

Download Full-text

A Hybrid Approach for Human Activity Recognition with Support Vector Machine and 1D Convolutional Neural Network

2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) ◽

10.1109/aipr50011.2020.9425332 ◽

2020 ◽

Author(s):

Md Maruf Hossain Shuvo ◽

Nafis Ahmed ◽

Koundinya Nouduri ◽

Kannappan Palaniappan

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Activity Recognition ◽

Human Activity ◽

Hybrid Approach ◽

Human Activity Recognition ◽

Support Vector

Download Full-text

Approximate Parameter Tuning of Support Vector Machines

2011 3rd International Workshop on Intelligent Systems and Applications ◽

10.1109/isa.2011.5873377 ◽

2011 ◽

Author(s):

Shizhong Liao ◽

Chenhao Yang ◽

Lizhong Ding

Keyword(s):

Support Vector Machines ◽

Parameter Tuning ◽

Support Vector ◽

Vector Machines

Download Full-text

HARTH: A Human Activity Recognition Dataset for Machine Learning

Sensors ◽

10.3390/s21237853 ◽

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.

Download Full-text

Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting

Health Informatics Journal ◽

10.1177/1460458216656471 ◽

2016 ◽

Vol 24 (1) ◽

pp. 24-42 ◽

Cited By ~ 11

Author(s):

Claudia Ehrentraut ◽

Markus Ekholm ◽

Hideyuki Tanushi ◽

Jörg Tiedemann ◽

Hercules Dalianis

Keyword(s):

Support Vector Machines ◽

Significant Risk ◽

Parameter Tuning ◽

Hospital Staff ◽

Support Vector ◽

Patient Records ◽

Hospital Acquired Infections ◽

Vector Machines ◽

Patient Health ◽

Hospital Acquired

Hospital-acquired infections pose a significant risk to patient health, while their surveillance is an additional workload for hospital staff. Our overall aim is to build a surveillance system that reliably detects all patient records that potentially include hospital-acquired infections. This is to reduce the burden of having the hospital staff manually check patient records. This study focuses on the application of text classification using support vector machines and gradient tree boosting to the problem. Support vector machines and gradient tree boosting have never been applied to the problem of detecting hospital-acquired infections in Swedish patient records, and according to our experiments, they lead to encouraging results. The best result is yielded by gradient tree boosting, at 93.7 percent recall, 79.7 percent precision and 85.7 percent F1 score when using stemming. We can show that simple preprocessing techniques and parameter tuning can lead to high recall (which we aim for in screening patient records) with appropriate precision for this task.

Download Full-text