Hierarchical evolutionary classification framework for human action recognition using sparse dictionary optimization

Jansi, R.; Amutha, R.

doi:10.1016/j.swevo.2021.100873

Cited by 7 publications

(2 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [ 38 ] proposed that shown in Figure 13 , a sparse representation based hierarchical (SRH) classifier. Figure 14 shows the comparison of accuracy of different methods in the UCI data set.…”

Section: Performance Evaluationmentioning

confidence: 99%

A Novel Segmentation Scheme with Multi-Probability Threshold for Human Activity Recognition Using Wearable Sensors

Zhou

Wang

Huan

et al. 2022

Sensors

View full text Add to dashboard Cite

In recent years, much research has been conducted on time series based human activity recognition (HAR) using wearable sensors. Most existing work for HAR is based on the manual labeling. However, the complete time serial signals not only contain different types of activities, but also include many transition and atypical ones. Thus, effectively filtering out these activities has become a significant problem. In this paper, a novel machine learning based segmentation scheme with a multi-probability threshold is proposed for HAR. Threshold segmentation (TS) and slope-area (SA) approaches are employed according to the characteristics of small fluctuation of static activity signals and typical peaks and troughs of periodic-like ones. In addition, a multi-label weighted probability (MLWP) model is proposed to estimate the probability of each activity. The HAR error can be significantly decreased, as the proposed model can solve the problem that the fixed window usually contains multiple kinds of activities, while the unknown activities can be accurately rejected to reduce their impacts. Compared with other existing schemes, computer simulation reveals that the proposed model maintains high performance using the UCI and PAMAP2 datasets. The average HAR accuracies are able to reach 97.71% and 95.93%, respectively.

show abstract

Section: Performance Evaluationmentioning

confidence: 99%

A Novel Segmentation Scheme with Multi-Probability Threshold for Human Activity Recognition Using Wearable Sensors

Zhou

Wang

Huan

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In contrast, these hurdles can be alleviated and accurate action recognition can be achieved by the usage of wearable sensors 13–16 . Wearable sensor‐based techniques acquire data from sensors attached to the human body, such as accelerometers, gyroscopes, magnetometers and so forth.…”

Section: Introductionmentioning

confidence: 99%

Representative‐discriminative dictionary learning algorithm for human action recognition using smartphone sensors

Rajamoney¹,

Ramachandran²

2022

Concurrency and Computation

View full text Add to dashboard Cite

SUMMARY With the advancement of mobile computing, understanding, and interpretation of human activities has become increasingly popular as an innovative human computer interaction application over the past few decades. This article presents a new scheme for action recognition based on sparse representation theory using a novel dictionary learning algorithm. This system employs two types of inertial signals from smartphones namely, accelerometer and gyroscope sensory data. Attainment of higher values of classification accuracy depends on the creation of effective dictionaries that completely retain the important features of every action while maintaining the least correlation with the features of other actions. Accordingly, in this research, we propose a new algorithm for learning dictionaries with two levels of dictionary training that aims at learning a compact, representative, and discriminative dictionary for each class. Unlike typical dictionary learning algorithms that aim at the creation of dictionaries that best represents the features of each class, our proposed algorithm incorporates a discriminative criterion that eventually produces better classification results. To validate the proposed framework, all the experiments were performed using three publicly available datasets.

show abstract

One-Class Classification Approach in Accelerometer-Based Remote Monitoring of Physical Activities for Healthcare Applications

et al. 2022

View full text Add to dashboard Cite

Hierarchical evolutionary classification framework for human action recognition using sparse dictionary optimization

Cited by 7 publications

References 64 publications

A Novel Segmentation Scheme with Multi-Probability Threshold for Human Activity Recognition Using Wearable Sensors

A Novel Segmentation Scheme with Multi-Probability Threshold for Human Activity Recognition Using Wearable Sensors

Representative‐discriminative dictionary learning algorithm for human action recognition using smartphone sensors

One-Class Classification Approach in Accelerometer-Based Remote Monitoring of Physical Activities for Healthcare Applications

Contact Info

Product

Resources

About