A research on CSI-based human motion detection in complex scenarios

Liu, Jialin; Wang, Lei; Guo, Linlin; Fang, Jian; Lu, Bingxian; Zhou, Wei

doi:10.1109/healthcom.2017.8210800

Cited by 23 publications

(22 citation statements)

References 9 publications

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“…Therefore, there is no sampling frequency difference among the antennas on the same NIC. Thus, many researchers adopt phase differences as input for models associated with human motion and wireless signals [37,38,[70][71][72]. The phase difference can derive the rough speed of the human movement.…”

Section: Modeling Human Activity With Wireless Signalmentioning

confidence: 99%

“…The phase difference can be used to separated walking and running from other static actions, such as sitting, lying, and standing. For the action recognition problem, with the assistance of feature extraction, phase difference helps in distinguishing multiple actions [39,[70][71][72]. Phase differences can hardly solve motion tracking problems because the spatial details (distance, direction) are hard to derive from phase differences.…”

Section: Phasementioning

confidence: 99%

“…Time-domain Pros: relatively simple calculation; Cons: vulnerable to environmental changes and noise maximum, minimum, mean, standard deviation, kurtosis, skewness, variance, median and median absolute deviation, percentiles, root sum square, interquartile range [13,18,37,40,42,43,46,[48][49][50]62,72,73,75,76,[78][79][80][83][84][85][88][89][90]97,108,127,130], time lag [37], power decline ratio [37], amplitude sequence [20,54,74,91], phase sequence [39,51,107], CV [38] Frequency-domain Pros: capture the periodical characteristics of human motion; Cons: large amount of calculation FFT coefficient [12,83], dominant frequency [43], power spectrum density [88], spectral entropy [43,57,70,72,…”

Section: Category Pros and Cons Featuresmentioning

confidence: 99%

“…The statistics feature often characterize the shape of the received waveform in the time domain. There are a large amount of studies [13,37,40,42,43,70,72,73,76,79,80,83,85,[88][89][90]108] that extract time-domain statistical features (maximum, minimum, mean, standard deviation, kurtosis, skewness, variance, median and median absolute deviation, percentiles, root sum square, interquartile range) from amplitude or phase streams. In particular, WIG [78] extracts the above time-domain features from anomaly data series obtained by the LOF-based segmentation method.…”

Section: Spatial Domainmentioning

confidence: 99%

“…WiSee [55] and FEMO [10] use the Doppler shifts in the classification of template matching. Many studies [43,70,72,73,80,90,92] extract spectral entropy from frequency streams as the classification features.…”

Section: Spatial Domainmentioning

confidence: 99%

See 4 more Smart Citations

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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