Towards Environment Independent Device Free Human Activity Recognition

Jiang, Wenjun; Miao, Chenglin; Ma, Fenglong; Yao, Shuochao; Ya-qing, Wang; Yuan, Ye; Xue, Hongfei; Song, Chen; Ma, Xin; Koutsonikolas, Dimitrios; Xu, Wenyao; Lu, Shan

doi:10.1145/3241539.3241548

Cited by 434 publications

(229 citation statements)

References 48 publications

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“…If |xi − mi |/σ i is larger than a predefined threshold, the current point xi is viewed as an outlier and replaced with the median m i . EI [77] uses the Hampel filter to remove outliers.…”

Section: Noise Reductionmentioning

confidence: 99%

“…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]. For features that change dramatically with the environment, multiple times of training should be performed when there is some change in the environments.…”

Section: Activity Classificationmentioning

confidence: 99%

“…The average recognition accuracy of SignFi is 86.66% for 7,500 instances of 150 sign gestures performed by five volunteers. In order to achieve the environment-independent human motion recognition, EI [77] collects the activities set of 40 subject-room pairs (about 1,200 in total) to train a CNN classifier for six human daily activities sensing (wiping the whiteboard, walking, moving a suitcase, rotating the chair, sitting, standing up and sitting down). Its classification accuracy is between 61-75%.…”

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%

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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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Section: Noise Reductionmentioning

confidence: 99%

Section: Activity Classificationmentioning

confidence: 99%

Section: Deep Learningmentioning

confidence: 99%

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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“…It has been widely used in the image processing area [30,31]. We have to clarify that our methods are quite different from those in [32,33]. Jiang et al [32] used deep learning based methods to extract environment independent features.…”

Section: Introductionmentioning

confidence: 99%

Towards Location Independent Gesture Recognition with Commodity WiFi Devices

Wang

2019

Electronics

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Recently, WiFi-based gesture recognition has attracted increasing attention. Due to the sensitivity of WiFi signals to environments, an activity recognition model trained at a specific place can hardly work well for other places. To tackle this challenge, we propose WiHand, a location independent gesture recognition system based on commodity WiFi devices. Leveraging the low rank and sparse decomposition, WiHand separates gesture signal from background information, thus making it resilient to location variation. Extensive evaluations showed that WiHand can achieve an average accuracy of 93% for various locations. In addition, WiHand works well under through the wall scenario.

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