WiFall: Device-free fall detection by wireless networks

Cui, Han; Wu, Kaishun; Wang, Yuxi; Ni, Lionel M.

doi:10.1109/infocom.2014.6847948

Cited by 222 publications

(117 citation statements)

References 17 publications

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“…Applications include fall detection [8], activity detection for energy saving at homes or offices [9], 24-hour sleep-wake monitoring in narcolepsy [10], a detection system for motion disorders in Autism patients [11], and other uses leveraging IoTs [12][13][14][15][16][17][18]. The methods introduced in [13,14] leverage body sensor nodes powered by human energy harvesting and wireless sensor networks for remote patient monitoring.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things for Sensing: A Case Study in the Healthcare System

et al. 2018

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Medical healthcare is one of the fascinating applications using Internet of Things (IoTs). The pervasive smart environment in IoTs has the potential to monitor various human activities by deploying smart devices. In our pilot study, we look at narcolepsy, a disorder in which individuals lose the ability to regulate their sleep-wake cycle. An imbalance in the brain chemical called orexin makes the sleep pattern irregular. This sleep disorder in patients suffering from narcolepsy results in them experience irrepressible sleep episodes while performing daily routine activities. This study presents a novel method for detecting sleep attacks or sleepiness due to immune system attacks and affecting daily activities measured using the S-band sensing technique. The S-Band sensing technique is channel sensing based on frequency spectrum sensing using the orthogonal frequency division multiplexing transmission at a 2 to 4 GHz frequency range leveraging amplitude and calibrated phase information of different frequencies obtained using wireless devices such as card, and omni-directional antenna. Each human behavior induces a unique channel information (CI) signature contained in amplitude and phase information. By linearly transforming raw phase measurements into calibrated phase information, we ascertain phase coherence. Classification and validation of various human activities such as walking, sitting on a chair, push-ups, and narcolepsy sleep episodes are done using support vector machine, K-nearest neighbor, and random forest algorithms. The measurement and evaluation were carried out several times with classification values of accuracy, precision, recall, specificity, Kappa, and F-measure of more than 90% that were achieved when delineating sleep attacks.

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Section: Related Workmentioning

confidence: 99%

Internet of Things for Sensing: A Case Study in the Healthcare System

et al. 2018

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“…Recently, CSI has been extended to recognize human motions such as fall detection [27], daily activity recognition [28], micro-movement recognition [29], and gesture recognition [30,31]. Nandakumar et al [30] presented a hand gesture recognition system that can identify four hand gestures with an accuracy of 91%, and 89% in LOS and in a backpack, respectively.…”

Section: Csimentioning

confidence: 99%

WiGeR: WiFi-Based Gesture Recognition System

Al-qaness

2016

IJGI

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Abstract:Recently, researchers around the world have been striving to develop and modernize human-computer interaction systems by exploiting advances in modern communication systems. The priority in this field involves exploiting radio signals so human-computer interaction will require neither special devices nor vision-based technology. In this context, hand gesture recognition is one of the most important issues in human-computer interfaces. In this paper, we present a novel device-free WiFi-based gesture recognition system (WiGeR) by leveraging the fluctuations in the channel state information (CSI) of WiFi signals caused by hand motions. We extract CSI from any common WiFi router and then filter out the noise to obtain the CSI fluctuation trends generated by hand motions. We design a novel and agile segmentation and windowing algorithm based on wavelet analysis and short-time energy to reveal the specific pattern associated with each hand gesture and detect duration of the hand motion. Furthermore, we design a fast dynamic time warping algorithm to classify our system's proposed hand gestures. We implement and test our system through experiments involving various scenarios. The results show that WiGeR can classify gestures with high accuracy, even in scenarios where the signal passes through multiple walls.

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“…They extract several video features and apply one-class classification techniques to determine whether the new instances are in the "fall region" or outside it to distinguish a fall from other activities such as walking, sitting, standing, or lying. Han et al [56] establish a "WiFall" system to detect indoor falls for elderly people based on the advanced wireless technologies that employs the time variability and special diversity of channel state information (CSI) as the indicator of human activities. Zhang et al [57] establish a fall detection system named "Anti-Fall" based on the CSI phase difference over two antennas.…”

Section: Ambient Assistive Technology For Indoor Fall Riskmentioning

confidence: 99%

Ambient Technology to Assist Elderly People in Indoor Risks

Yared

Abdulrazak

2016

Computers

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While elderly people perform their daily indoor activities, they are subjected to several risks. To improve the quality of life of elderly people and promote healthy aging and independent living, elderly people need to be provided with an assistive technology platform to rely on during their activities. We reviewed the literature and identified the major indoor risks addressed by assistive technology that elderly people face during their indoor activities. In this paper, we identify these risks as: fall, wrong self-medication management, fire, burns, intoxication by gas/smoke, and the risk of inactivity. In addition, we discuss the existing assistive technology systems and classify the risk detection algorithms, techniques and the basic system principles and interventions to enhance safety of elderly people.

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WiFall: Device-free fall detection by wireless networks

Cited by 222 publications

References 17 publications

Internet of Things for Sensing: A Case Study in the Healthcare System

Internet of Things for Sensing: A Case Study in the Healthcare System

WiGeR: WiFi-Based Gesture Recognition System

Ambient Technology to Assist Elderly People in Indoor Risks

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