Test Event Generation for a Fall-Detection IoT System

Gutierrez-Madronal, Lorena; Blunda, Luigi La; Wagner, Matthias; Medina‐Bulo, Inmaculada

doi:10.1109/jiot.2019.2909434

Cited by 34 publications

(31 citation statements)

References 11 publications

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“…However Walking, Standing up, Empty, fall and falSE alaRm activities are classified with high accuracy. It is a common, known (and intuitive to understand) phenomenon that falling is difficult to distinguish from sitting down, see (Gutiérrez-Madroñal et al 2019) or (Li et al 2009).…”

Section: Resultsmentioning

confidence: 99%

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Device free human activity and fall recognition using WiFi channel state information (CSI)

Damodaran

Haruni

Kokhkharova

et al. 2020

CCF Trans. Pervasive Comp. Interact.

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Human activity recognition (HAR) is a broad research area. While there exist solutions based on sensors and vision-based technologies, these solutions suffer from considerable limitations. Thus in order to mitigate or avoid these limitations, device free solutions based on radio signals like (home) WiFi, in particular 802.11 are considered. Recently, channel state information (CSI), available in WiFi 802.11n networks have been proposed for fine-grained analysis. We are able to detect human activities like Walk, Sit, Stand, Run (in the sequel, any human activity used for classification is capitalised, i.e. is denoted by its corresponding label. For example, "standing" is denoted as Stand, the activity "sitting" is denoted by Sit and so on), etc. in a line-of-sight (LOS) scenario and a non-line-of-sight (N-LOS) scenario within an indoor environment. We propose two algorithms-one using a support vector machine (SVM) for classification and another one using a long shortterm memory (LSTM) recurrent neural network. While the former uses sophisticated pre-processing and feature extraction techniques based on wavelet analysis, the latter processes the raw data directly (after denoising). We show that it is possible to characterize activities and/or human body presence with high accuracy and we compare both approaches with regard to accuracy and performance. Furthermore, we extend the experimental setup to detect human falls, too which is a relevant use-case in the context of ambient assisted living (AAL) and show that with the developed algorithms it is possible to detect falls with high accuracy. In addition, we also show that the algorithms can be used to count the number of people in a room based on the CSI-data, which is a first step towards detecting more complex social behavior and activities. Our paper is an extended version of the paper (Damodaran and Schäfer, Device free human activity recognition using wifi channel state

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Section: Resultsmentioning

confidence: 99%

“…Most approaches require patients to wear sensor belts or equivalent devices, see (Özdemir 2014;Gutiérrez-Madroñal et al 2019;La Blunda and Wagner 2016a, b). All of the above fall detection technologies are based on sensor fusion of specific sensors e.g.…”

Section: Prior Workmentioning

confidence: 99%

Device free human activity and fall recognition using WiFi channel state information (CSI)

Damodaran

Haruni

Kokhkharova

et al. 2020

CCF Trans. Pervasive Comp. Interact.

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“…A BAN generates a large amount of data in real time. Therefore, data analysis technologies have to be used [2]. Complex event processing (CEP) [3] is a data analysis technology that is used to manage and monitor in real time a large volume of information that arrives in the form of events with the lowest latency time.…”

Section: Volume 4 2016mentioning

confidence: 99%

“…Since testing the system plays an essential role in verifying the reliability of our fall detection prototype by simulating all possible fall events, the IoT-TEG tool [2], [4] is used. With reference to the development of a reliable fall detection solution and the abovementioned expectations, our ongoing research must answer the following questions:…”

Section: Volume 4 2016mentioning

confidence: 99%

A Wearable Fall Detection System Based on Body Area Networks

et al. 2020

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Falls can have serious consequences for people, leading to restrictions in mobility or, in the worst case, to traumatic-based cases of death. To provide rapid assistance, a portable fall detection system has been developed that is capable of detecting fall situations and, if necessary, alerting emergency services without any user interaction. The prototype is designed to facilitate reliable fall detection and to classify several fall types and human activities. This solution represents a life-saving service for every person that will significantly improve assistance in the case of fall events, which are a part of daily life. Additionally, this approach facilitates independent system operation, since the system does not depend on sensor or network units located within a building structure. This paper also introduces fall analysis. To guarantee functional safety, a hazard analysis method named system-theoretic accident model and processes (STAMP) is applied.

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“…The aim of this work is to design and fabricate automatic drilling and tapping machine [1] monitoring using nodeMCU. This monitors the machine status and also counts number of jobs to the server through anyone of internet protocol [2] with the help of ESP8266 controller.…”

Section: Introductionmentioning

confidence: 99%

Automatic Drilling and Tapping Machine Monitoring using NodeMcu

Kamalakkannan¹,

Lavanya²

2020

Bull. Sci. Res.

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Industrial automation is most emerging technology, and now a day’s industries almost are operated by automation. Most of the industrial plants, are automated without any operator machine interaction. Using arduino IDE and matlab simulink software, program is developed to indicate the machine status and number of job counts to the server. In this work, consideration was given to nodeMCU controller for drilling and tapping machine monitoring which can be operated both manually and automatically. This system gives a real-time view of the industrial plant, reduction in troubleshooting time for faults and also it assures safety of operator.

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Test Event Generation for a Fall-Detection IoT System

Cited by 34 publications

References 11 publications

Device free human activity and fall recognition using WiFi channel state information (CSI)

Device free human activity and fall recognition using WiFi channel state information (CSI)

A Wearable Fall Detection System Based on Body Area Networks

Automatic Drilling and Tapping Machine Monitoring using NodeMcu

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