Wearable low power pre-fall detection system with IoT and bluetooth capabilities

Rathi, Neeraj; Kakani, Monika; El-Sharkawy, Mohamed; Rizkalla, Maher

doi:10.1109/naecon.2017.8268778

Cited by 15 publications

(7 citation statements)

References 8 publications

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“…There are smarter systems that track the patient's gait, detect the patient's unbalanced movements, and warn before the person falls. [ 20 21 ] Finally, the alarm provides the necessary information to the medical center or family members after accurate verification. This technology can be implemented in many smart homes.…”

Section: Discussionmentioning

confidence: 99%

The Application of Internet of Things for the Elderly Health Safety: A Systematic Review

Sattari¹,

Dorri²,

Zabolinezhad³

2023

Advanced Biomedical Research

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The elderly population is projected to increase from 8.5% in 2015 to 12% in 2030 and 16% in 2050. This growing demographic is chronically vulnerable to various age-related diseases and injuries like falling, leading to long-term pain, disability, or death. Thus, there is a need to use the potential of novel technologies to support the elderly regarding patient safety matters in particular. Internet of Things (IoT) has recently been introduced to improve the lifestyle of the elderly. This study aimed to evaluate the studies that have researched the use of the IoT for elderly patients’ safety through performance metrics, accuracy, sensitivity, and specificity. We conducted a systematic review on the research question. To do this, we searched PubMed, EMBASE, Web of Science, Scopus, Google Scholar, and Science Direct databases by combining the related keywords. A data extraction form was used for data gathering through which English, full-text articles on the use of the IoT for the safety of elderly patients were included. The support vector machine technique has the most frequency of use compared to other techniques. Motion sensors were the most widely used type. The United States with four studies had the highest frequencies. The performance of IoT to ensure the elderly's safety was relatively good. It, however, needs to reach a stage of maturity for universal use.

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Section: Discussionmentioning

confidence: 99%

The Application of Internet of Things for the Elderly Health Safety: A Systematic Review

Sattari¹,

Dorri²,

Zabolinezhad³

2023

Advanced Biomedical Research

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“…Depending on the device and the method used, fall recognition algorithms are divided into three main areas: wearable sensor-based methods, scene-based methods, and vision-based methods [ 3 ]. Wearable sensor-based approaches have many advantages because they use sensors to automatically detect falls and send help-seeking messages to health care workers through communication devices such as WIFI, mobile networks, and Bluetooth [ 21 , 22 , 23 , 24 , 25 ]. However, elderly people often forget to wear sensors.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition

Zhao

Zhang

Shang

2022

Sensors

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Falls pose a great danger to social development, especially to the elderly population. When a fall occurs, the body’s center of gravity moves from a high position to a low position, and the magnitude of change varies among body parts. Most existing fall recognition methods based on deep learning have not yet considered the differences between the movement and the change in amplitude of each body part. Besides, some problems exist such as complicated design, slow detection speed, and lack of timeliness. To alleviate these problems, a lightweight subgraph-based deep learning method utilizing skeleton information for fall recognition is proposed in this paper. The skeleton information of the human body is extracted by OpenPose, and an end-to-end lightweight subgraph-based network is designed. Sub-graph division and sub-graph attention modules are introduced to add a larger perceptual field while maintaining its lightweight characteristics. A multi-scale temporal convolution module is also designed to extract and fuse multi-scale temporal features, which enriches the feature representation. The proposed method is evaluated on a partial fall dataset collected in NTU and on two public datasets, and outperforms existing methods. It indicates that the proposed method is accurate and lightweight, which means it is suitable for real-time detection and rapid response to falls.

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“…However, the defence mechanisms run into the issue of using more power than the attack itself. One solution that has been looked at is a zero-power notification which will create an alert when an attack is being detected [26].…”

Section: Vampire Attackmentioning

confidence: 99%

Battery draining attacks against edge computing nodes in IoT networks

Smith

Palin

Ioulianou

et al. 2020

Cyber-Physical Systems

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Many IoT devices, especially those deployed at the network edge have limited power resources. A number of attacks aim to exhaust these resources and drain the batteries of such edge nodes. In this work, we study the effects of a variety of battery draining attacks against edge nodes. Through simulation, we clarify the extent to which such attacks are able to increase the usage and hence waste the power resources of edge nodes. Specifically, we implement hello flooding, packet flooding, selective forwarding, rank attack, and versioning attack in ContikiOS and simulate them in the Cooja simulator, and measure and report a number of time and power resource usage metrics including CPU time, low power mode time, TX/RX time, and battery consumption. Besides, we test the stretch attack with three different batteries as an extreme scenario. Our extensive measurements enable us to compare the effectiveness of these attacks. Our results show that Versioning attack is the most severe attack in terms of draining the power resources of the network, followed by Packet Flooding and Hello Flood attacks. Furthermore, we confirm that Selective Forwarding and Rank attacks are not able to considerably increase the power resource usage in our scenarios. By quantifying the effects of these attacks, we demonstrate that under specific scenarios, Versioning attack can be three to four times as effective as Packet Flooding and Hello Flood attacks in wasting network resources, while Packet Flooding is generally comparable to Hello Flood in CPU and TX time usage increase but twice as powerful in draining device batteries.

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Wearable low power pre-fall detection system with IoT and bluetooth capabilities

Cited by 15 publications

References 8 publications

The Application of Internet of Things for the Elderly Health Safety: A Systematic Review

The Application of Internet of Things for the Elderly Health Safety: A Systematic Review

A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition

Battery draining attacks against edge computing nodes in IoT networks

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