Designing a Flex Sensor-Based Posture Trainer
Keywords:
Poor Posture, Lower back pain, Flex sensor, Logistic regressionAbstract
There are several grave health hazards that can arise from poor posture. There are now two main categories of posture trainers on the market: electronic posture reminders and posture braces. Both of them struggle to develop appropriate posture. Flex sensors were utilized in this work as an option to evaluate posture specifically and generate effective posture training. This study aimed to develop a posture trainer that would assess and enhance sedentary posture. The researcher predicted that the posture trainer would lengthen the amount of time participants sat upright and reduce their average slouch score. The tool precisely measures the bend in the lower back using flex sensors and Arduino programming software (slouch value). At the start of the study, the subjects sat in a certain position to allow the researcher to assess a threshold of each subject's individual good and bad posture. For a week, the participants wore the gadget for 30 minutes every day. Their average slouch value and the number of minutes they spent in the "excellent posture threshold" were noted on day one. The device vibrated for the following five days to notify the participants to correct their posture and teach them to sit in the "good posture threshold" if they slouched into the "poor posture threshold." Data was again obtained on the last day and compared to the participant's initial day. Statistics showed that the researcher's findings were statistically significant and that their null hypothesis was wrong. After receiving vibration feedback, using the posture trainer increased the amount of time spent sitting upright and decreased the slouch value. The study also statistically investigates if the weight of posture trainer is affecting the posture of the user. Finally, the data collected from the posture trainer are used for classifying lower back pain from normal using a logistic regression. The model developed showed an accuracy of 0.9, precision of 0.83 and recall of 1.
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