Fuzzy rule inference based human activity recognition

Chang, Jyh‐Yeong; Shyu, Jia-Jye; Cho, Chien-Wen

doi:10.1109/cca.2009.5280999

Cited by 15 publications

(17 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Datasets. We test our algorithm on two datasets: the Weizmann human motion dataset [17], the KTH human action dataset [18,19], and the HumanEva dataset [3,20]. All the experiments are conducted on a Pentium 4 machine with 2 GB of RAM, using the implementation on MATLAB.…”

Section: Resultsmentioning

confidence: 99%

“…The HumanEva dataset [3,20] is used for evaluation. It contains six different motions: Walking, Jogging, Gestures, Boxing, and Combo.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, we identify jogging, running, walking and boxing and compare the proposed method with the four state-of-the-art methods in the literature: Blank et al [18], Lu et al [19], Sigal et al [3], Chang et al [20] and Juan Carlos Niebles [21] in three dataset. As shown in the Tables 1, 2 and 3, the existing methods, the low recognition accuracy because these action are not only occlusion situation are complex, but also the legs have complex beat, motion and other group actions.…”

Section: Comparison Kth Datasetmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Self-Occlusion Behavior Recognition Based on pLSA

Xia

Liu

2013

Journal of Applied Mathematics

View full text Add to dashboard Cite

Human action recognition is an important area of human action recognition research. Focusing on the problem of self-occlusion in the field of human action recognition, a new adaptive occlusion state behavior recognition approach was presented based on Markov random field and probabilistic Latent Semantic Analysis (pLSA). Firstly, the Markov random field was used to represent the occlusion relationship between human body parts in terms an occlusion state variable by phase space obtained. Then, we proposed a hierarchical area variety model. Finally, we use the topic model of pLSA to recognize the human behavior. Experiments were performed on the KTH, Weizmann, and Humaneva dataset to test and evaluate the proposed method. The compared experiment results showed that what the proposed method can achieve was more effective than the compared methods.

show abstract

Section: Resultsmentioning

confidence: 99%

“…The HumanEva dataset [3,20] is used for evaluation. It contains six different motions: Walking, Jogging, Gestures, Boxing, and Combo.…”

Section: Resultsmentioning

confidence: 99%

Section: Comparison Kth Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Self-Occlusion Behavior Recognition Based on pLSA

Xia

Liu

2013

Journal of Applied Mathematics

View full text Add to dashboard Cite

show abstract

“…The use of appropriate cameras for capturing the activity has great impact on the overall functionality of the recognition system. In fact, these cameras have been instrumental to the progression of research in the field of computer vision [21][22][23][24][25]. According to the The handcrafted representation-based approach mainly follows the bottom-up strategy for HAR.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

2017

View full text Add to dashboard Cite

Human activity recognition (HAR) is an important research area in the fields of human perception and computer vision due to its wide range of applications. These applications include: intelligent video surveillance, ambient assisted living, human computer interaction, human-robot interaction, entertainment, and intelligent driving. Recently, with the emergence and successful deployment of deep learning techniques for image classification, researchers have migrated from traditional handcrafting to deep learning techniques for HAR. However, handcrafted representation-based approaches are still widely used due to some bottlenecks such as computational complexity of deep learning techniques for activity recognition. However, approaches based on handcrafted representation are not able to handle complex scenarios due to their limitations and incapability; therefore, resorting to deep learning-based techniques is a natural option. This review paper presents a comprehensive survey of both handcrafted and learning-based action representations, offering comparison, analysis, and discussions on these approaches. In addition to this, the well-known public datasets available for experimentations and important applications of HAR are also presented to provide further insight into the field. This is the first review paper of its kind which presents all these aspects of HAR in a single review article with comprehensive coverage of each part. Finally, the paper is concluded with important discussions and research directions in the domain of HAR.

show abstract

“…In which, two well established types of FIS can be utilized: i.e., the Mamdani type which uses typical membership functions of output features for defuzzification [35] and the Sugeno type which entails weight average by constant or linear expression to compute crisp output [36]. The FIS was employed in many studies to distinguish patterns of human activities that were measured by various mobile devices with an approximate 90%–100% recognition rate dependent on features [37,38,39,40,41]. In addition, the neuro-fuzzy algorithm was suggested to adapt the training-based dataset to the training procedure for comprehensive and accurate recognition of adopted features [42,43,44,45].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement

Chiang

Kan

Chen

et al. 2016

Sensors

View full text Add to dashboard Cite

Ubiquitous health care (UHC) is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN). The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS) can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC.

show abstract

Fuzzy rule inference based human activity recognition

Cited by 15 publications

References 10 publications

Adaptive Self-Occlusion Behavior Recognition Based on pLSA

Adaptive Self-Occlusion Behavior Recognition Based on pLSA

A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement

Contact Info

Product

Resources

About