Design and Implementation of a Smart Helmet System for Underground Miner’s Safety

In Indonesia, motorcycle traffic accidents have increased rapidly. Traffic accidents result in high mortality. One of the causes is influenced by human psychological factors or human error. However, to improve the behavior of the riders and due reducing traffic accidents, the purpose of this research is developed a Smart Helmet that can detect drowsiness by measuring the heartbeats psychological riders. Besides that, this system equipped with an SOS button. Its function is to detect and help the riders if there were any emergency incidents on the roads. This proposed system designed using a heartbeat pulse sensor, GPS module, GSM module, Arduino Nano, push-button, and buzzer. Smart Helmet examined in several scenarios to test the performance of the drowsiness and the SOS button. The resulting test on 10 respondents defined that the drowsiness can be detected and give a buzzer alert when the heartbeat is below 60 bpm. The information can be seen without delay. The incident location can be tracked down by utilizing the google maps application. The shift position as the error distance of the GPS incident location only happens in the range of 21.96-42.63 meters. The conclusion is the helmet can detect drowsiness based on heartrate and give an alarm. The SOS button is functionally properly as long as the helmet is used in the outdoor area.

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Smart Helmet GPS-Based for Heartbeat Drowsiness Detection and Location Tracking

Fahrurrasyid

Hapsari

Meisaroh

et al. 2022

JBBBE

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Design and Implementation of Hand Gesture Detection System Using HM Model for Sign Language Recognition Development

Milu,

Fathima,

Talukder

et al. 2024

JDAIP

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Gesture detection is the primary and most significant step for sign language detection and sign language is the communication medium for people with speaking and hearing disabilities. This paper presents a novel method for dynamic hand gesture detection using Hidden Markov Models (HMMs) where we detect different English alphabet letters by tracing hand movements. The process involves skin color-based segmentation for hand isolation in video frames, followed by morphological operations to enhance image trajectories. Our system employs hand tracking and trajectory smoothing techniques, such as the Kalman filter, to monitor hand movements and refine gesture paths. Quantized sequences are then analyzed using the Baum-Welch Re-estimation Algorithm, an HMM-based approach. A maximum likelihood classifier is used to identify the most probable letter from the test sequences. Our method demonstrates significant improvements over traditional recognition techniques in real-time, automatic hand gesture recognition, particularly in its ability to distinguish complex gestures. The experimental results confirm the effectiveness of our approach in enhancing gesture-based sign language detection to alleviate the barrier between the deaf and hard-of-hearing community and general people.

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Design and Implementation of a Smart Helmet System for Underground Miner’s Safety

Cited by 2 publications

References 8 publications

Smart Helmet GPS-Based for Heartbeat Drowsiness Detection and Location Tracking

Smart Helmet GPS-Based for Heartbeat Drowsiness Detection and Location Tracking

Design and Implementation of Hand Gesture Detection System Using HM Model for Sign Language Recognition Development

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