Dynamic time wrapping based gesture recognition

Masood, Saad; Qureshi, Majid Parvez; Shah, Mukesh; Ashraf, Salman; Halim, Zahid; Abbas, Ghulam

doi:10.1109/icreate.2014.6828366

Cited by 15 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As there is no sign to label the start point of an activity, the system should be capable of automatically identifying the inactive and active periods. Traditional methods such as manifold alignment and dynamic time wrapping (DTW) [33] transform the general problem to highdimensional vectors and therefore require high computational power which is not ideal for our system. In comparison, we transform this challenge into a pulse detection problem by calculating the power intensity P I of Doppler sequence:…”

Section: Time Alignmentmentioning

confidence: 99%

Log-Likelihood Clustering-Enabled Passive RF Sensing for Residential Activity Recognition

Tan

et al. 2018

IEEE Sensors J.

View full text Add to dashboard Cite

Physical activity recognition is an important research area in pervasive computing because of its importance for in e-healthcare, security and human-machine interaction. Among various approaches, passive RF sensing on the basis of well-tried radar principle has potential to provides unique non-invasive human activity detection and recognition solution, and draws more attention. However, this technology is far from mature. This paper presents a novel HMM-log-likelihood matrix based feature characterizing of the Doppler shifts to break the fixed sliding window limitation in traditional feature extraction approaches. We prove the effectiveness of proposed feature extraction method in K-means&K-medoids clustering algorithms with experimental Doppler data gathered collected from a passive radar system. The time adaptive log-likelihood matrix-based approach outperforms the traditional SVD, PCA and physical feature based approaches, and reaches 80% in activity recognizing rate.

show abstract

Section: Time Alignmentmentioning

confidence: 99%

Log-Likelihood Clustering-Enabled Passive RF Sensing for Residential Activity Recognition

Tan

et al. 2018

IEEE Sensors J.

View full text Add to dashboard Cite

show abstract

“…79 The system consists of two modes of operations, the recording mode and the translation mode. The recording mode enables the recording of a prede¯ned sign gesture, and during the translation mode, the current gesture is compared with the recorded gesture for recognition using DTW.…”

Section: Speech and Sign Language Recognitionmentioning

confidence: 99%

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Lun

Zhao

2015

Int. J. Patt. Recogn. Artif. Intell.

273

109

View full text Add to dashboard Cite

Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classi¯cation of motion recognition techniques to highlight the di®erent approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lowerlevel computer vision tasks such as segmentation, object detection and human pose estimation.

show abstract

“…Then, given an unknown motion, various methods are employed to compare the new motion with each in the database, and the unknown motion is classified as the one with the minimum distance between the two. The most popular method is dynamic time warping, which could compute the similarity between two temporal sequences despite timing and speed differences [6].…”

Section: Direct Comparisonmentioning

confidence: 99%