Women's Safety with a Smart Foot Device

Raja, S. Pravinth; Rachel, S. Sheeba; Sapna, R.

doi:10.1109/iccct53315.2021.9711778

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…The monitoring of a user's eye mobility while seeing merchandise could be another factor in determining favorite selections. To improve the prediction outcomes, more robust characteristics and classifier configurations should be investigated [ 8 – 13 ].…”

Section: Discussionmentioning

confidence: 99%

Classification of EEG Signals Using Neural Network for Predicting Consumer Choices

Rani

Raja

Sinthuja

et al. 2022

Computational Intelligence and Neuroscience

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EEG, or Electroencephalogram, is an instrument that examines the brain’s functions while it is executing any activity. EEG signals to aid in the identification of brain processes and movements and are thus useful in the detection of neurobiological illnesses. Pulses have a very weak magnitude and are recorded from peak to peak, with pulse width ranging from 0.5 to 100 V, which is around 100 times below than ECG signals. As a result, many types of noise can easily influence them. Because EEG signals are so important in detecting brain illnesses, it is critical to preprocess them for accurate assessment and detection. The crown of your head The EEG is a weighted combination of the signals generated by the different small locations beneath the electrodes on the cortical plate. The rhythm of electrical impulses is useful for evaluating a broad range of brain diseases. Hypertension, Alzheimer, and brain damage are all possibilities. We can compare and distinguish the brainwaves for different emotions and illnesses linked with the brain by studying the EEG signal. Multiple research studies and methodologies for preprocessing, extraction of features, and evaluation of EEG data have recently been created. The use of EEG in human-computer communication could be a novel and demanding field that has acquired traction in recent years. We present predictive modeling for analyzing the customer’s preference of likes and dislikes via EEG signal in our report. The impulses were obtained when clients used the Internet to seek for multiple items. The studies were carried out on a dataset that included a variety of consumer goods.

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Section: Discussionmentioning

confidence: 99%

Classification of EEG Signals Using Neural Network for Predicting Consumer Choices

Rani

Raja

Sinthuja

et al. 2022

Computational Intelligence and Neuroscience

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“…It has the same drawback of human interaction, as seen in many other devices. A low-cost smart footwear device [13] that is activated with the tapping of one foot in front of the other three times. This activates the GPS and GSM.…”

Section: Heartbeat Sensormentioning

confidence: 99%

“…Sensors associated to the specific wearables. [9] Smart ring None [16] Smart band Heartbeat and temperature sensors [40] Smart garb Pressure sensor [46] Smart band Vibration sensor [24] Smart jacket Temperature, heartbeat and vibration sensor [19] Inner wear Flex sensor [33] Smart jacket Flex sensor [29] Smart band Pulse-rate and temperature sensor [21] Smart band Pulse-rate and tilt sensor [37] Smart band Pulse-rate and tilt sensor [28] Smart band Breathing and heartbeat sensor [13] Smart shoe Acceleration sensor [15] Smart band Pulse-rate sensor [34] Smart band Pulse-rate, temperature, motion, vibration N. Bharadwaj designed a device named suraksha [48] for the safety and protection of women travelling alone and Dr. K.M Anand Kumar [40] designed a smart garb, which is any kind of special cloth. So, these systems can be used with various forms of cloths.…”

Section: Figure 8 Percentages Of Wearables Used In Women's Safety Dev...mentioning

confidence: 99%

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The Role of IoT in Woman’s Safety: A Systematic Literature Review

et al. 2023

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Women's safety has been highlighted as one of the major concerns of any society where several women are dealing with various safety issues like harassment, rape, molestation, and domestic violence due to different social or cultural reasons. Internet of Things (IoT) is becoming a promising technology to support day-to-day concerns and provide support in handling various affairs. Many IoT-based devices have been introduced by the community to help women deal with their potential safety threats. This study presents a systematic literature review of research studies exhibiting the IoT devices for women's safety, the main features these devices offer as well as the wearable, sensors used, and the machine learning algorithms used. The review is carried out by carefully examining and synthesizing the research articles published between 2016 to 2022 in well-reputed research venues. The results revealed that different types of sensors are used to capture the state of women undergoing safety issues where the pulse-rate, and pressure sensors are most commonly used sensors in these devices. In addition, the devices used different technology to transmit the alerts including global positing system (GPS), global system for mobile communication (GSM), and Raspberry pi. Furthermore, several machine learning algorithms such as logistic regression, hidden Markov, and decision trees are used to identify the potential under threat women and help prevent the undesirable situation for women beforehand. It was identified that despite producing notable research in the underlying domain the systems emphasizing auto-activation of alert generation with lesser human interaction and improved accuracies are required to be developed for effectively addressing the concern. In addition to reviewing the literature, this study suggests a taxonomy posing different techniques, features, wearables, and sensors used in IoT-based women safety devices. Furthermore, the gaps and challenges pertaining to the IoT devices and their usability for women's safety have also been highlighted. In addition, this work proposes an architectural model that presents prominent components necessary to develop IoT-based women's safety devices. Lastly, this study emphasizes the use of combinations of sensors to get multiple types of input data that could lead to determining the possibility of threat with better accuracies and precisions.

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Comprehensive Evaluation of Women’s Safety Solutions in IoT Environment

Khan,

Amjad

2024

Lecture Notes in Networks and Systems

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Women's Safety with a Smart Foot Device

Cited by 9 publications

References 3 publications

Classification of EEG Signals Using Neural Network for Predicting Consumer Choices

Classification of EEG Signals Using Neural Network for Predicting Consumer Choices

The Role of IoT in Woman’s Safety: A Systematic Literature Review

Comprehensive Evaluation of Women’s Safety Solutions in IoT Environment

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