Delay reduction in real-time recognition of human activity for stroke rehabilitation

Nabiei, Roozbeh; Najafian, Maryam; Parekh, Manish; Jančovič, Peter; Russell, Martin J.

doi:10.1109/splim.2016.7528413

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…However these do not provide sufficiently rich information, and an antenna needs to be worn. This paper extends our recent research on AR using instrumented objects [12], [13], which demonstrated the use of a set of GMM-HMM detectors, each modelling a particular component of a task, in a scenario with a known set of objects. We present the development of a DNN-HMM AR system and study of compensation approaches to deal with object variability.…”

Section: Introductionsupporting

confidence: 55%

Compensating for Object Variability in DNN–HMM Object-Centered Human Activity Recognition

Peng

Jančovič

Russell

2019

2019 27th European Signal Processing Conference (EUSIPCO)

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Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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Section: Introductionsupporting

confidence: 55%

Compensating for Object Variability in DNN–HMM Object-Centered Human Activity Recognition

Peng

Jančovič

Russell

2019

2019 27th European Signal Processing Conference (EUSIPCO)

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“…In the next stage of this research, we will investigate the on-line GMM-HMM based approach proposed in [36] to further improve our system's accuracy. In addition to that, we will apply subsequent analysis based on the word count [37], speech recognition [38,39,40], and keyword spotting [41].…”

Section: Summary and Future Workmentioning

confidence: 99%

Automatic measurement and analysis of the child verbal communication using classroom acoustics within a child care center

Najafian¹,

Irvin²,

Luo³

et al. 2016

5th Workshop on Child Computer Interaction (WOCCI 2016)

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Understanding the language environment of early learners is a challenging task for both human and machine, and it is critical in facilitating effective language development among young children. This papers presents a new application for the existing diarization systems and investigates the language environment of young children using a turn taking strategy employing an i-vector based baseline that captures adult-to-child or child-tochild conversational turns across different classrooms in a child care center. Detecting speaker turns is necessary before more in depth subsequent analysis of audio such as word count, speech recognition, and keyword spotting which can contribute to the design of future learning spaces specifically designed for typically developing children, or those at-risk with communication limitations. Experimental results using naturalistic childteacher classroom settings indicate the proposed rapid childadult speech turn taking scheme is highly effective under noisy classroom conditions and results in 27.3% relative error rate reduction compared to the baseline results produced by the LIUM diarization toolkit.

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“…The time-frequency analysis of LFPs has been considered to classify different human behavioral activities [15][16][17]. Several classification methods based on Hidden Markov Model (HMM) and Deep Neural Network have been suggested in [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

An FFT-based synchronization approach to recognize human behaviors using STN-LFP signal

Golshan

Hebb

Hanrahan

et al. 2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Classification of human behavior is key to developing closed-loop Deep Brain Stimulation (DBS) systems, which may be able to decrease the power consumption and side effects of the existing systems. Recent studies have shown that the Local Field Potential (LFP) signals from both Subthalamic Nuclei (STN) of the brain can be used to recognize human behavior. Since the DBS leads implanted in each STN can collect three bipolar signals, the selection of a suitable pair of LFPs that achieves optimal recognition performance is still an open problem to address. Considering the presence of synchronized aggregate activity in the basal ganglia, this paper presents an FFT-based synchronization approach to automatically select a relevant pair of LFPs and use the pair together with an SVM-based MKL classifier for behavior recognition purposes. Our experiments on five subjects show the superiority of the proposed approach compared to other methods used for behavior classification.

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Delay reduction in real-time recognition of human activity for stroke rehabilitation

Cited by 12 publications

References 21 publications

Compensating for Object Variability in DNN–HMM Object-Centered Human Activity Recognition

Compensating for Object Variability in DNN–HMM Object-Centered Human Activity Recognition

Automatic measurement and analysis of the child verbal communication using classroom acoustics within a child care center

An FFT-based synchronization approach to recognize human behaviors using STN-LFP signal

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