Virtual Friendly Device for Women Security

Sumathy, B.; Shiva, P. Deepan; Mugundhan, P.; Rakesh, R.; Prasath, S. Sai

doi:10.1088/1742-6596/1362/1/012042

Cited by 14 publications

(4 citation statements)

References 2 publications

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“…For the protection of women's safety, many systems have been created, including FIGHT BACK, STREETSAFE, SENTINEL, ONWATCH, GUARDLY, SURAKSHA, BONITTA, and others. [7]- [13]. When a person is being resisted against an assault, methods or technologies that continually monitor the variables are required.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of Violent Response using Feature Extraction Matrix Algorithm of a Time Series Data (FEM)

Randhawa

Shanthagiri²,

Alharbi

et al. 2022

Preprint

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There are several women safety devices in the market today. Women who suffer these atrocities are even denied basic human rights, as set out in the Criminal Code. Women who are not as fit (physically) as men need to be protected from the evils of society. The introduction of actions and procedures for healthier women is not adequate and needs to be well improved. However, these devices are not fool- proof. The crux of this paper is a partial result of a challenging problem faced during design and construction a fool-proof Smart Jacket for women’s safety using fabric sensors. The smart jacket is envisioned for the women to wear on all occasions. An alert message and subjects’ Geo-location is sent to pre-assigned phone number if the subject is faced with violent situation. The jacket consists of fabric Sensors, Accelerometer, Gyroscope and Magnetometer, which are strategically placed to record maximum variations in signal for minimum movement in subject’s body. The primary challenge is not in design or construction of a jacket, but in accurately classifying violent activity from animated activity. Both violent activity and animated activities have commonality in sensor excitation. There are subtle differences which needs to be extracted to train a Machine learning algorithm to learn these patterns. Multivariate Regression Analysis (MRA) is used to highlight explanatory variables that can produce better convergence. With MRA results signifying a high degree of noise in the data, a newer approach, wherein, the ordinality of the data was ignored and cardinality of the data was considered for analysis. Machine learning approaches is then used to build a model using features extracted through MRA. Different machine learning techniques have been applied to classify the violent attack from the different activities such as stationary subject, walking, dancing. This study present that Support vector machine shows the better classification accuracy, computation time as compared to other algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Identification of Violent Response using Feature Extraction Matrix Algorithm of a Time Series Data (FEM)

Randhawa

Shanthagiri²,

Alharbi

et al. 2022

Preprint

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“…A smartphone application prototype was introduced in [30], which is primarily a safety decision aid designed to help women estimate the risk in their abusive relationships, set safety priorities, and develop a personalized safety plan. A "Virtual Friendly" device was created by [31], consisting mainly of GPS, GSM modem, microcontroller, RF transceiver, temperature sensor, voice recognizer to provide trouble-safety. The GPS receiver obtains location information in the form of latitude and longitude from satellites.…”

Section: Related Workmentioning

confidence: 99%

Design and Development of an Advanced Affordable Wearable Safety Device for Women: Freedom Against Fearsome

Humaira¹,

Ahmed²,

Roy³

et al. 2021

Adv. sci. technol. eng. syst. j.

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Harassment and violence against women have become one of the social security problems in Bangladesh. In this paper, we aim to develop safety devices for women named BOHNNI and BADHON which resemble legitimate jewelry. We used a microcontroller for the hardware device to make it most decisive and less immoderate. BOHNNI, a locating device, is the imitator of a locket including a voice recognizer, Bluetooth, Arduino, GPS, and GSM module. BADHON which imitates a bracelet is a rescue device for the victim whenever she thinks of herself being in a very deliberate situation. Both devices are activated by the user's voice commands and also by a manual switch. The devices are aesthetically designed which will make the users enthusiastic to wear them. The device will generate messages to the predefined relative's numbers with the victim's location and relevant surrounding information. The device can also be used as a self-defending weapon as it can produce a shock up to 10 mA with an interval of two seconds which can temporarily paralyze or freeze a person. After calculating, we have obtained the lowest response time of BOHNNI and BADHON which is 1.95s, and the highest accuracy level of 91.67% in different situations that ensure the superlative performance level of our devices. We found our device as an all-in-one device that combines all the features in it regarding safety.

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“…Many researchers have underlined that the voice is a key aspect of emotion recognition [65] and certain vocal monitoring and detection solutions have been implemented, as indicated by Reference [66]. A further element which can contain important information is ambient or environmental, sound, which allows the inference of tension, fear or similar emotions in the voice of the subject.…”

Section: Voice Ambient Soundmentioning

confidence: 99%

An Autonomous Alarm System for Personal Safety Assurance of Intimate Partner Violence Survivors Based on Passive Continuous Monitoring through Biosensors

et al. 2020

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Intimate Partner Violence (IPV) dramatically compromises the free and complete development of many women around the world, therefore leading to social asymmetry regarding the right to personal safety. In many cases, a woman who has reported her partner to police for gender-based violence needs to ensure her protection (either before the trial of the aggressor or after their freedom). Thus, it would be ideal if autonomous alarm systems could be developed in order to call the police if necessary. Up to now, many proposals have been presented in this regard, including solutions based on Information and Communication Technologies (ICT) but, unfortunately, these approaches usually rely on the active participation of the victims (survivors), who have to turn the system on by themselves if needed. Therefore, in order to overcome such limitations, in this work, a passive continuous monitoring system is proposed which uses biosensors attached to the survivor as well as machine learning techniques to infer if an abnormal situation related to gender-based violence is taking place, activating in this case an alarm. The monitoring structure of the system supervises a great deal of bio-signals according to the current status of technology of wearables and biomedical devices. The presented biosensors-based surveillance solution can also be manually disconnected for 30/60/90 min (on demand) in order to avoid false positives when a woman is, for example, practicing sports or carrying out other inoffensive activities that could incorrectly activate the alarm.

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Virtual Friendly Device for Women Security

Cited by 14 publications

References 2 publications

Identification of Violent Response using Feature Extraction Matrix Algorithm of a Time Series Data (FEM)

Identification of Violent Response using Feature Extraction Matrix Algorithm of a Time Series Data (FEM)

Design and Development of an Advanced Affordable Wearable Safety Device for Women: Freedom Against Fearsome

An Autonomous Alarm System for Personal Safety Assurance of Intimate Partner Violence Survivors Based on Passive Continuous Monitoring through Biosensors

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