Edge-IoT Facilitated Multimodal Sensor Fusion for Threat Recognition in Women's Safety Wearables

Safety of women is a significant problem on the
global level, and the fact that the time gap between the threat
appearance and its detection can be lowered is one of the
pressing social problems. Smart wearable devices coupled with
smart systems are being explored more and more with this
reason. Conventional safety systems that use only one sensor
like accelerometers or GPS are usually characterized by false
alarms, low accuracy and slow responses. The paper presents
an Edge-IoT-based multimodal sensor fusion model of female
safety wearables in order to overcome these drawbacks. The
framework combines the signal of accelerometer and
gyroscopes and conducts on-device inference to make real-time
responsive and communicate with the cloud to support alerts.
The experimental validation with the UCI Human Activity
Recognition dataset had shown a classification accuracy of 93
percent, lower latency, previously 7.8 s (cloud only) to 3.2 s
(edge), and a reasonable battery consumption (88 percent). The
findings validate the assertion that multimodal sensor fusion
yields consistency and precision in accuracy, responsiveness
and reliability when compared to single-sensor systems. It will
be extended in the future with physiological and acoustic
sensors to enhance the detection of a context-based threat,
accompanied by studies in the real world to assess userfriendliness,
ethical standards, and energy efficiency.
Keywords—Women’s Safety Wearables, Multimodal Sensor
Fusion, Edge-IoT Framework, Human Activity Recognition
(HAR), Threat Detection Systems, Real-Time Monitoring.