A Device-Free Number Gesture Recognition Approach Based on Deep Learning

Zhou, Qizhen; Xing, Jianchun; Li, Juelong; Yang, Qiliang

doi:10.1109/cis.2016.0022

Cited by 34 publications

(22 citation statements)

References 13 publications

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“…contained in signal sequences [14,19,20,23,24,40,44,[82][83][84][85][86]108]. In order to reduce the impact of environmental changes, the cutting modules extract features from the amplitude stream [44]. The threshold calculation on the amplitude difference is shown in Equation (28).…”

Section: Time-domain Thresholdmentioning

confidence: 99%

“…Template matching recognition is often training-free [10,19,20,39,55,74,91,93,94]. Training-once classification requires the valid features robust to the variations in the surrounding environment [12,13,32,37,40,42,44,45,[47][48][49]56,59,60,70,72,73,75,76,78,79,83,84,89,90,92,127,130]. Deep learning automatically extracts features, which often requires only one time of training [16,17,21,48,59,67,77,88,95,96,108].…”

Section: Activity Classificationmentioning

confidence: 99%

“…Pros: no training needed; Cons: accuracy depending on the specific template Euclidean distance [19,94], DTW [10,20,39,42,55,74,91,93,97], EMD [54,70,81,89], Jaccard coefficients [69] Machine Learning Pros: high efficiency and robustness; Cons: a lot of training data required KNN [18,40,46,48,51,52,59,82,99,124], SVM [12,13,32,37,40,42,44,45,[47][48][49]56,59,60,70,72,73,75,76,78,79,83,84,89,...…”

Section: Template Matchingmentioning

confidence: 99%

“…SVM is widely used in various human activity detection and classification systems [12,13,32,37,40,42,44,45,[47][48][49]56,59,60,70,72,73,75,76,78,79,83,84,89,90,92,127,130]. SVM classifier are used to classify 10 digital finger gestures [78], eight human movement directions [59], fall detection [37,92], walking direction detection [70], fitness activity classification [56,79], human daily activities [13,40,42,72,75,76,84,90], and human gestures [12,32,73,75,83,89].…”

Section: Machine Learningmentioning

confidence: 99%

“…Human activity recognitions, can be further divided into hand/finger gesture recognition [14,19,20,39,44,46,47,61,63,69,75,78,85,111,135], limb gesture recognition [16,55,62,64,75,80,82], daily activity recognition [12,13,36,42,43,47,[75][76][77]81,84,91,[95][96][97]99,123,128], fitness activity recognition [10,56,74,79,84,88], human movement direction recognition [57,59], mouth movement recognition [93], driving gestures…”

Section: Recognition Applicationsmentioning

confidence: 99%

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Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

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Wireless networks have been widely deployed with a high demand for wireless data traffic. The ubiquitous availability of wireless signals brings new opportunities for non-intrusive human activity sensing. To enhance a thorough understanding of existing wireless sensing techniques and provide insights for future directions, this survey conducts a review of the existing research on human activity sensing with wireless signals. We review and compare existing research of wireless human activity sensing from seven perspectives, including the types of wireless signals, theoretical models, signal preprocessing techniques, activity segmentation, feature extraction, classification, and application. With the development and deployment of new wireless technology, there will be more sensing opportunities in human activities. Based on the analysis of existing research, the survey points out seven challenges on wireless human activity sensing research: robustness, non-coexistence of sensing and communications, privacy, multiple user activity sensing, limited sensing range, complex deep learning, and lack of standard datasets. Finally, this survey presents four possible future research trends, including new theoretical models, the coexistence of sensing and communications, awareness of sensing on receivers, and constructing open datasets to enable new wireless sensing opportunities on human activities.

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