Table 1.
Comparison of proposed study with existing studies.
| Ref. | Function | Real-Time Hardware | Ignition Mechanism |
User Authentication |
Communication | Threshold Value for Detection | Proof of Concept |
|---|---|---|---|---|---|---|---|
| [7] | Detection of and tracking of two-wheeler during an accident | Real-time hardware is implemented without customization | Power off the ignition through the accelerometer sensor | The limit switch is useful for detecting the user | GSM for communicating messages and GPS for updating the location | The threshold value is not carried out during the development of the system | Tested in a real-time environment. |
| [19] | Convolutional object detector for detecting non-helmeted motorcyclists at a traffic light | Software and camera-based is implemented | The ignition mechanism is not covered | Authentication of two-wheeler is missing | A request-response protocol act as a medium for routing the videos to the server. | The proposed system is based on the video, so the threshold value is set based on trained images | Yes, implemented in real-time. |
| [34] | Real-time detection of blunt-force impact events on helmets for every individual | Fiber Bragg grating (FBG) sensor is integrated with the customized helmet | The focus of the study is on detecting the blunt force impact on the head. So ignition is not covered in the study | NA | Wireless FBG transceiver is for sending the transient signals | The magnitude and direction of the impact event are provided via transient signals. | Bowling ball Pendulum Impactor System (PIS) was constructed and employed for simulating concussive events |
| [36] | a drowsy driver alert is implemented with the Video Stream Processing (VSP). | Raspberry pi 3 modules are implemented as hardware | The proposed system is limited to the detection of driver drowsiness | User identification is processed with the assistance of an eye | Wi-Fi inbuilt in Raspberry Pi3 | Vision-based information is considered | Implemented the system in real-time with hardware. |
| [37] | Vehicle tracking and accident alert of the car | Fingerprint and Node MCU hardware is integrated | The fingerprint sensor is used for igniting the vehicle | Fingerprint-based user authentication | Wi-Fi is available in Node MCU. | A threshold value is not required. | Integrated the system in the vehicle for enabling ignition and authentication |
| Proposed | Authentication of wearing a helmet for igniting the two-wheeler. | Hardware is realized for real-time implementation | Flex sensor and RFID | RFID tag | 2.4 GHz RF communication to connect helmet node with two-wheeler node and Wi-Fi to connect to the server | the t-test is applied on RAW flex sensor value on different samples | The developed system is implemented on two-wheeler vehicles. |