Design of Monitoring System Step Walking With MPU6050 Sensor Based Android

Fitriani, Diah Ayu; Andhyka, Wahyu; Risqiwati, Diah

doi:10.21070/joincs.v1i1.799

Cited by 6 publications

(4 citation statements)

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“…The electrode used is Ag-ACL disposable electrode. The Electromyography block module circuit consists of an instrumentation amplifier circuit that uses an Op-Amp TL074 IC [11], a high pass filter circuit that uses a TL074 IC, a low pass filter circuit that uses a TL074 IC, and an adder circuit consisting of a summing amplifier circuit using IC TL074 and a voltage divider circuit, the accelerometer circuit uses the MPU6050 sensor as the neck angle elevation sensor [12], the Atmega328 microcontroller as the sensor data processor [13]. Oscilloscope with digital storage (TEXTRONIC, DPO2012, Taiwan) to retrieve test point data on analog circuits.…”

Section: ) Materials and Toolmentioning

confidence: 99%

Design of an Electromyograph Equipped with Digital Neck Angle Elevation Gauge

Solihin

Yulianto

Ariswati

et al. 2021

j.electron.electromedical.eng.med.inform

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The development of technology also affects human health, including body posture due to poor human position when using gadgets, both smartphones, and laptops. This study is design a tool that can measure the elevation of a person's neck angle equipped with electromyography, to help health workers, medical rehabilitation doctors to diagnose and provide treatment to patients with a bent head posture or forward head posture. In this research, an electromyography module is designed which consists of a series of instruments, a pre-amplifier circuit, a high pass filter, a low pass filter, and a dc offset regulator to be converted to digital so that it can be displayed on a laptop. In this study, the tapped muscle was the upper trapezius muscle using disposable electrodes. Meanwhile, to measure the angular elevation, the MPU 6050 sensor is used to measure the movement of the head forward. The frequency of the electromyography signal is 20-500 Hz. For software or display readings from this tool is Delphy. Meanwhile, the microcontroller used for ADC communication is Arduino Uno. From the research, it was found that the neck angle elevation gauge has a 0,597% error rate, for conditioning conducted on respondents, all respondents experienced a decrease in amplitude on the same frequency spectrum in the last ten minutes. Meanwhile, a drastic decrease occurred at the neck angle of 60°. Thus, it can be concluded that the forward position of the head affects the frequency spectrum of the neck muscles.

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Section: ) Materials and Toolmentioning

confidence: 99%

Design of an Electromyograph Equipped with Digital Neck Angle Elevation Gauge

Solihin

Yulianto

Ariswati

et al. 2021

j.electron.electromedical.eng.med.inform

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“…In regard to ASR, Hannani et al (2021) realized a noise-robust ASR on a robot applied in daily communications and improved the performance of ASR by using hangover processing based on erosion and dilation. With respect to PR, Fitriani et al (2017) developed a pedometer that can connect to the user's cellphone via Bluetooth. The pedometer recognizes the paces of the wearers through acceleration sensors.…”

Section: Introductionmentioning

confidence: 99%

Study on forearm swing recognition algorithms to drive the underwater power‐assisted device of frogman

Chen

Han

2021

Journal of Field Robotics

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The frogmen have a great demand for the underwater power-assisted device (UPaD) because of their poor working conditions and complex missions. Although the wearable power-assisted device on land has been diversified, the development of UPaDs is relatively slow. Meanwhile, the most of current small frogman transport equipment is needed to be manipulated by the frogmen with their hands with the aim of changing their moving speed or posture, which increases the difficulty of frogmen executing their tasks. The major contribution of this paper is to develop a method that frees the hands and realizes power assisting at the same time for frogmen, which can be applied to drive the UPaDs of frogmen. Two posture recognition algorithms based on the Human-Computer Interface have been promoted.The algorithms can recognize the forearm swings of the wearer of UPaD, calculate the frequency of the wearer's forearm swing, and adjust the moving speed of the wearer's UPaD according to the wearer's forearm swing frequency. Moreover, we evaluate the performance of two algorithms recognizing forearm swings underwater from four different subjects. Meanwhile, one of the two algorithms is improved and evaluated by experiments from the previous four subjects. The results of accuracy of two algorithms recognizing the wearer's forearm swing are more than 90%. The performance of the speed regulating system of UPaD based on the designed two algorithms is also acceptable.

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“…Due to its wide application in human behavior, living assistance, home applied, security, medical, rehabilitation, and wider to smart cities, and transportation topics. From previous research on the medical, and rehabilitation topic has improved health status for diabetic patient, elderly monitoring, noncommunicable disease (NCD), calories [1], [2] and fall detection [3], human behavior [4]. Daily activity recognition requires a robust technique that can be used under free daily motion, for example, recognition of fall detection, especially in elderly fall detection.…”

Section: Introductionmentioning

confidence: 99%

Single Triaxial Accelerometer-Gyroscope Classification for Human Activity Recognition

Minarno

Kusuma

Wibowo

et al. 2020

2020 8th International Conference on Information and Communication Technology (ICoICT)

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Evaluated activity as a detail of the human physical movement has become a leading subject for researchers. Activity recognition application is utilized in several areas, such as living, health, game, medical, rehabilitation, and other smart home system applications. For recognizing the activity, the accelerometer was popular sensors. As well as a gyroscope, in addition to dimension, low computation, and can be embedded in a smartphone. Used smartphone with an accelerometer as a popular solution for recognized daily activity. Signal was generated from the accelerometer as a time-series data is an actual approach like a human activity pattern. Traditional machine learning method in mid of the modern method worth it considering. Single position triaxial accelerometer-gyroscope Motion data have acquired in an of 30 volunteers.

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Design of Monitoring System Step Walking With MPU6050 Sensor Based Android

Cited by 6 publications

References 0 publications

Design of an Electromyograph Equipped with Digital Neck Angle Elevation Gauge

Design of an Electromyograph Equipped with Digital Neck Angle Elevation Gauge

Study on forearm swing recognition algorithms to drive the underwater power‐assisted device of frogman

Single Triaxial Accelerometer-Gyroscope Classification for Human Activity Recognition

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