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This study introduces a groundbreaking system designed to measure the fatigue levels in hand muscles during different writing speeds. By integrating temperature and heartbeat sensors with advanced algorithms, we establish a method to predict and prevent fatigue, enhancing writing efficiency and comfort. This paper explores the various scenarios under which fatigue is measured, the underlying technology, and the potential implications for future applications in educational and professional settings.
Writing, a fundamental skill, often leads to fatigue, particularly in scenarios requiring extended periods of time.
Understanding and mitigating this fatigue can significantly improve writing performance and prevent long-term health issues. This research proposes a system that employs temperature and heartbeat sensors to monitor the physiological signs of fatigue during writing at various speeds.
Following are the scenarios for measuring the fatigue of hand muscle during movement and writing text based on the proposed system environment:
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When writing at normal speed without exerting pressure on hands. When the person is writing at his normal pace, the temperature of the hand increases slowly. We assume that initially, the body temperature of the person is normal. When he starts writing, the temperature sensor will start to sense the body temperature. Initially, the transmitter of the sensor does not produce alert because the body temperature of the person is slowly increasing and is not in danger. The sensor remains in sleep mode for a specific period of time.
Whenever body temperature crosses the threshold limit, the transmitter sends alert.
Heartbeat sensor checks the heart rate of the person. In the beginning, the heartbeat of a person is normal. When a person starts to write, the heartbeat slowly increases due to pressure. The sensor remains in sleep mode for a normal heartbeat. Heartbeat sensor is triggered whenever palpitations cross a predefined threshold level.
During writing, temperature and heartbeat of soldier increase with time, i.e. implies that glucose level of a body will decrease and the energy is consumed.Sensors sense the values and will send it to the app that will check the received data and also the current energy level of the person using Harris Benedict Formula. For fatigue state calculation the Harris-Benedict Formula is:
BMR = 66.5 + A + B − C
Basal Metabolic Rate (BMR) is the amount of energy required to maintain the normal metabolic rate of the body. This is the energy required for the functioning of vital body parts like heart, lungs, kidneys, liver, nervous system and skin. We take this amount of energy as minimum energy of the person and if the person’s condition meets this level then this is called a ‘state of fatigue’.
If the person is writing slowly then sensors sense heartbeat & temperature. However, in this scenario, the heartbeat & temperature will reach the threshold earlier than normal writing pace. Fast Writing: In the scenario when the person is writing at very high speed the fatigue state will come earlier than normal writing and slow writing.
This formula and calculation of fatigue amount will be the fatigue the muscle will experience during writing and implementing this through an app that checks your fatigue rate in muscle can help the person to improve is writing speed by using it on daily purpose avoiding the state of fatigue. For future implementation we are also trying to perform the same thing using electrode used for ECG scans such that the same thing will be defined too much greater extends as the heartbeat and brain signals are relatable to each other.
Optimizing Writing Performance: Monitoring and Managing Hand Muscle Fatigue. (2024, Feb 16). Retrieved from https://studymoose.com/document/optimizing-writing-performance-monitoring-and-managing-hand-muscle-fatigue
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