Wi-Multi: A Three-Phase System for Multiple Human Activity Recognition With Commercial WiFi Devices

Feng, Chunhai; Arshad, Sheheryar; Zhou, Siwang; Cao, Dun; Liu, Yonghe

doi:10.1109/jiot.2019.2915989

Cited by 62 publications

(31 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…For features that change dramatically with the environment, multiple times of training should be performed when there is some change in the environments. Wi-Multi [97] propose a three-phase system using CSI according to the size of available training samples. Table 8 describes a summary of the activity classification techniques.…”

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%

“…LSTM (long short-term memory) is a typical kind of RNN, which solves the long-term dependency problem. Wi-Multi [97] proposes a deep learning network structure based on LSTM with 936 CSI samples as training data, which achieves a classification accuracy of 96.1%.…”

Section: Deep 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%

See 4 more Smart Citations

Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

View full text Add to dashboard Cite

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.

show abstract