آخر الأخبار
Smart Helmet for Electric Scooter Users***
Smart Helmet for Electric Scooter Users
Biomedical technologies department, Lebanese German University, Sahel Alma Lebanon
l.boufarah@lgu.edu.lb
مؤلفون Authors/
Accident location localization Emergency service and family member notification
Electric scooter driver’s life is at high risk, daily 3 to 4 drivers in France are dead, aging between 14 to 45 years old. In Europe, daily accidents cause death due to late medical intervention. Our main aim was to develop a scooter helmet that monitors vital signs and tracks the driver’s location in order to save them when an accident happens. The proposed system acts as a health monitor and tracker where all recorded data are sent to a mobile application. But when an accident occurs and the helmet receives a shock an emergency SMS will be sent to a predefined mobile number stating that the driver had an accident and showing the location (via google maps). Furthermore, an SOS call will be made to an emergency center (red cross for example) stating that medical assistance is needed at a specific location and the blood group of the driver will be specified. All the recorded data will also be shared with a predefined medical personnel (family doctor for example) few minutes, during and after the accident in order to get an optimum medical assistance. It is a low cost, efficient and convenient product.
smart, helmet, patient monitoring, mobile application, vital signs
Physiological signals monitoring is a prerequisite for the assessment of the human being body state. Nowadays, this is done increasingly using wearable devices, especially within the era of analog and digital integrated circuit technology. Medical experts state that vital signs monitoring is crucial for electrical scooters drivers. Blood pressure, heart rate and SPO2 level should be monitored in order to collect the data especially before any possible accident, during it and after it. Small sensors are built in the helmet and monitoring starts when the helmet is on.
The main aim of this project is to design a smart helmet which acts as a security and a monitoring system for electric scooter drivers. This system consists of sensors with communication modules which help to monitor and record the driver’s vital signs and to alarm family members and medical assistance in case of accident with the Global Positioning System (GPS) location.
In order to help road accident victims several studies are being carried out. Lately, research is focusing on the enhancement of emergency notification during accidents without any human intervention using machine-to-machine technologies [1]. The system proposed in such technologies is to categorize the severity of the accident (major, minor or moderate) depending on the motion of the sensor [1]. Furthermore, using GPS module the vehicle speed is equated. This system has some limitations especially when considering the severity of the victim’s condition since the accelerometer sensor is mounted on the vehicle surface [1]. Another way of determining the severity of an accident is by assessing the vehicle damage severity [2]. This is done using impact and pressure sensors. Emergency services will receive a notification of the accident. In this case accident severity is equated using the concept of data mining. An estimation of the severity is made following the impact level and the airbags status. Furthermore, Wang et al. [3] provided accident notification through the vibration sensors embedded on the vehicle which senses the shock and alerts emergency services using the Global System for Mobile (GSM) and indicating the GPS coordinates. In Europe, people are using electric scooters instead of driving cars in order to avoid traffic in cities and to help the environment via lowering the pollution level. Unfortunately, young generation (14 to 45 years old) are in danger since accident level is increasing. Between 2017 and 2020 more than 1252 e-scooter accidents were recorded [5,6]. The importance of wearing a helmet has emerged and traffic regulations were strengthened in order to protect users. In 2021, wearing a helmet when driving an electric scoter became mandatory. Preventing road accidents is difficult and a standard helmet helps to reduce the risk of head injury but does not help notifying emergency and does not monitor the driver’s vital signs condition [4]. Most of the existing smart helmets are designed for cyclists. The main issues with these products are: first, that these helmets are controlled by hand (a button should be pressed for it to operate). Second, these helmets convey only one-way communication signals [6]. Therefore, multiple researchers worked on designs which helps to shorten the emergency intervention time following an accident. In this work, a smart helmet including vital signs recording and emergency notification is proposed especially for electric scooter users since those users are mainly the young generation and the occurring accidents are deadly [7].
The smart helmet contains an embedded network of various sensors and modules. The main components of this electric scooter drivers’ smart helmet are the impact sensor, the temperature sensor, the heart rate sensor, the SpO2 sensor, the GPS module and a GSM module.
Once the helmet is on, the temperature sensor, heart rate sensor and the SpO2 sensor are going to detect the body temperature, the heart rate, and the oxygen level respectively. Those values are going to be recorded non-stop and the data are sent to a mobile application.
The impact sensor is mounted in order to determine the impact level with which the helmet hits the floor following an accident. When the impact crosses a specific level (threshold) the GPS module detects the exact location of the accident and with the help of the GSM module an SMS will be sent to a predefined phone number stating the location and the blood group type of the driver. If medical assistance is not required or if the driver drops the helmet accidentally, the emergency notification signal can be canceled by pressing a button at the side of the helmet. This button should be pressed within 90 seconds following the shock. If an impact is detected, the vital signs data are recorded and saved, the family doctor can check those data over his/her phone. Those data will help the doctor to monitor the drivers condition prior, during and following the accident. Once medical assistance reaches the location the helmet will be taken off safely. The medical team can work together now in order to assess the medical state of the driver and its stability. Figure 1 shows the interrelationship among these modules and figure 2 shows flow diagram that emphasizes the process of the helmet system.
Figure 1: Design of the system
Figure 2: Block diagram of the system
A temperature sensor, a heart rate sensor and an oximetry sensor are combined into one small, integrated sensor module called the MAX30100 (figure 3). This sensor integrates red and IR LED drivers to drive LED pulses for SpO2 and heart rate measurements. The sensor illuminates the skin and measures how much light is absorbed or reflected by the blood vessels beneath to determine a person’s pulse rate using optical sensing techniques. The MAX30100 module then processes this data to determine the heart rate. Monitoring body temperature and ambient temperature for environmental control systems is also made by the MAX30100 module, it measures the temperature by recording the voltage across the thermistor, which is the basis of the temperature sensor [12]. Its size (14mm x 10mm x 2.8mm) makes it the perfect option for applications where a compact design is implemented.
Figure 3: MAX30100 Heart rate and oximetry sensor module
The impact sensor (figure 4) determines the level of impact on the helmet surface. It is important in order to confirm if an accident has occurred. Sudden impacts on the helmet surface are detected and the analog value is set as output. This analog value is converted to a digital one in order to get the exact value of the hit. The range of impact handled for a standard helmet is between 250 and 300 g. Concussions, neck break and head damage can happen. The hit impact is derived as such: - The analog impact sensor range is from 0 to 1023 - The digital default range of the sensor is 0 and 5 volts Note that the acceleration due to gravity acting on an object resting on the earth surface is 1g. Also note, for implementation purposes, that the reference voltage value used in the Arduino board is 3.3 V [8, 9]. So, the digital voltage is directly proportional to the acceleration due to gravity or the impact of the hit.
Figure 4: Triple axis accelerometer and gyroscope Mpu-6050 module
Accident location localization The GPS module (figure 5) comes with an antenna, in no time, it is capable of sending the GPS coordinates of the location. The exact location is shown in latitude and longitude coordinates. Once decrypted, the accident address is provided. The embedded GPS module is the NEO-6M GPS with a wired antenna, it gives a fast response of GPS coordinates from the satellite [10].
Figure 5: NEO-6M GPS module
Emergency service and family member notification A GSM module (figure 6) is embedded in the inner part of the helmet. Once the accident location is defined the GSM module comes into action. It takes a Subscriber Identification Module (SIM) in order to send the message to the emergency service and to a predefined family member mobile phone number (via SMS and an SOS call) [11]. This notification can save hundreds of lives every day.
Figure 6: SIM900A GSM module
The ESP8266 12-E NodeMCU (figure 7) is a Wi-Fi module that is used in the development of Internet of Things (IoT) devices. It is based on the ESP8266 system-on-chip which integrates a microcontroller unit and a Wi-Fi radio [13]. Using this unit allows interfacing with external sensors, actuators, and other devices.
Once all signals are met, recorded data can be sent to a mobile application. With the help of MIT application inventor (provided by google) a software application for android system was developed and all sensors’ data were saved and monitored via an android device.
The smart helmet for electric scooter users is a gadget that has the potential to save people life during road travel. Users can ensure their safety. This helmet can be very helpful when accidents occurs since it helps the victims to get immediate medical assistance without any external intervention. It also provides the family doctor / hospital with all the vital signs monitoring data prior, during and after the accident shock. This will make the medical case analysis faster and the medical assistance will be prompt. Some additional features can be done, for example an alcohol detection sensor can be added to the system in order to alert nearby police station of drunken riders. Also, electrocardiogram recordings can be carried out. At the moment, the engineering and electronic safety protocols for the helmet is carried out following international standards in order to be able to submit all required documentation for launching the product. This will help humanity and it will lower the number of daily accidents.
1. R. Sujatha, N. VijayaRagavan, K.S. Suganya, “IOT: To enhance automatic accident notification using M2M technologies”, International Journal of Scientific & Engineering Research, Vol. 6, No. 3, pp. 1-4, March 2015. 2. Fogue M, Garrido P, Martinez FJ, Cano JC, Calafate CT, Manzoni P, “A System for Automatic Notification and Severity Estimation of Automotive Accidents”, IEEE Transactions on Mobile Computing, Vol. 13, No. 5, pp. 948 – 963, May 2001. 3. “Traffic Accident Automatic Detection And Remote Alarm Device” – Wang Wei and Fan Hanbo 4. Smart Helmet with Sensors for Accident Prevention Mohd Khairul Afiq Mohd Rasli, Nina Korlina Madzhi, Juliana Johari Faculty of Electrical Engineering Universiti Teknologi MARA 40450 Shah Alam Selangor, MALAYSIA 5. Smart Helmets for Automatic Control of Headlamps Muthiah M1, Aswin Natesh V2, Sathiendran R K3 Undergraduate Student,Dept. of Electrical and Electronics Engineering1.2, Director3 Sri Venkateswara College of Engineering1.2, Arobot3 Chennai,Tamil Nadu, India 6. A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection Ping Li, Ramy Meziane, Martin J.-D. Otis, Hassan Ezzaidi, REPARTI Center, University of Quebec at Chicoutimi Chicoutimi, Canada 7. K. Sudarsan, P. Kumaraguru Diderot, “Helmet for Road Hazard Warning with Wireless Bike Authentication and Traffic Adaptive Mp3 Playback”, International Journal of Science and Research (IJSR), Vol. 3, No. 3, March 2014. 8. Impact Sensor - http://electronicdesign.com/analog/low-cost-impact-sensor-uses-piezoelectric-device. 9. Accelerometer Sensor - https://www.bosch-sensortec.com/bst/products/motion/ accelerometers/overview_accelerometers. 10. GPS with arduino - http://www.instructables.com/id/Connecting-GPS-module-to-Arduino/. 11. GSM with arduino-https://www.arduino.cc/en/Guide/ArduinoGSMShield. 12. MAX30100 Pulse Oximeter and Heart Rate Sensor with Arduino Interface MAX30100 Pulse Oximeter Sensor with Arduino (microcontrollerslab.com) 13. ESP-12E / ESP-12F / NodeMCU with Arduino IDE Programming ESP-12E / ESP-12F / NodeMCU With Arduino IDE - Circuit Journal
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