scholarly journals IOT Enabled Wearable Gloves with SEMG Subsystem with Posture Analysis

2021 ◽  
Vol 9 (9) ◽  
pp. 1690-1695
Author(s):  
Swathi Sangaboina
Keyword(s):  
Electrical Activity ◽  
Real Time ◽  
Muscle Activity ◽  
Patient Treatment ◽  
Raspberry Pi ◽  
Monitoring Device ◽  
Posture Analysis ◽  
Emg Signal ◽  
Remote Server ◽  
Muscle Tissues

Abstract: Electromyogram (EMG) is a technique to track the record , analyze and estimate the electrical activity produced by muscles. This technique is used to detect the muscle issues that harm the nerves activity , muscle tissues and identify the location where they are joined together . This paper discusses the implementation of a project which can be considered as a tool for the acquisition of muscle activity, presentation and real-time attainment of EMG signal using a specific EMG sensor. The live EMG reading is recorded using the Wi-Fi- enabled Raspberrypi and then sent to a remote server in our case ThingSpeak server with the help of IoT concepts which helps in the telemetry of the obtained biomedical signals using the cloud. Results are displayed in ThingSpeak. The live recordings are also obtained on the PC using the serial plotter. This project can also help us in monitor and observe the progress of the patient treatment even if the physiotherapist could not come and data can be directly sent to them. Thus, the project aims to develop an EMG monitoring device based on IoT, for analyzing and acquiring EMG signals. Keywords: EMG sensor, Raspberry pi, LCD, ADS1115

scholarly journals Noninvasive muscle activity imaging using magnetography

2020 ◽  
Vol 117 (9) ◽  
pp. 4942-4947 ◽  
Author(s):  
Rodolfo R. Llinás ◽  
Mikhail Ustinin ◽  
Stanislav Rykunov ◽  
Kerry D. Walton ◽  
Guilherme M. Rabello ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Contrast Media ◽  
Electrical Activity ◽  
Real Time ◽  
Human Body ◽  
Muscle Activity ◽  
Muscular Activity ◽  
Exponential Increase ◽  
Somatic Musculature ◽  
The Magnetic Field

A spectroscopic paradigm has been developed that allows the magnetic field emissions generated by the electrical activity in the human body to be imaged in real time. The growing significance of imaging modalities in biology is evident by the almost exponential increase of their use in research, from the molecular to the ecological level. The method of analysis described here allows totally noninvasive imaging of muscular activity (heart, somatic musculature). Such imaging can be obtained without additional methodological steps such as the use of contrast media.

Electromyography (EMG) Controlled Assistive Rehabilitation System

2014 ◽  
Author(s):  
Robert V. Forshaw ◽  
Nicholas W. Snow ◽  
Jared M. Wolff ◽  
Mansour Zenouzi ◽  
Douglas E. Dow
Keyword(s):  
Electrical Activity ◽  
Motor Function ◽  
Time Window ◽  
Neuromuscular Diseases ◽  
Raspberry Pi ◽  
User Intent ◽  
Triceps Muscle ◽  
Emg Signal ◽  
Rehabilitation System ◽  
Arm Motion

Electromyography (EMG) is a method for monitoring the electrical activity of skeletal muscles. The EMG signal is used to diagnose neuromuscular diseases and muscular injuries. EMG can also be utilized as an indicator of user intent for a muscle contraction for a specific motion. This input signal could be used to control powered exoskeleton prostheses. Limbs with impaired motor function tend to have increased disuse that may result in further muscle weakness. Therapy and other physical activities that increase the use of an impaired limb may contribute to some recovery of motor function. A device that helps to perform activities of daily living may increase usage and enhance recovery. The objective of this project is to make developments toward an EMG controlled assistive rehabilitation system that monitors EMG signals of the bicep and triceps muscles, and drives a motor to assist with arm motion. A motor is used to develop torque that would assist rotations of the arm about the elbow. A pair of EMG sensors (one pair near the biceps and the other near the triceps muscle) transmits electrical activity of the arm to a microcontroller (Raspberry Pi, Raspberry Pi Foundation, United Kingdom). For the prototype, the EMG signal is sampled and rectified within a moving time window to determine the root mean squared (VRMS) value. This value is used by the microcontroller to generate a pulse-width modulated (PWM) signal that controls the motor. Sensors for the motor provide information to an algorithm on the microcontroller. The generated PWM signal is based on the Vrms values for the EMG signal. Testing and analysis has shown a correlation between the EMG Vrms amplitude and muscle generated torque. The EMG controlled assistive rehabilitation system shows promise for assisting motor function for rotations about the elbow. Further algorithmic development is needed to determine the appropriate amount of assistance from the motor for the motor function indicated by user intent.

PROTOTYPE ALAT PEMANTAU SALURAN AIR BAWAH TANAH DENGAN MENGGUNAKAN WEBCAM C170 BERBASIS RASPBERRY PI

2015 ◽  
Vol 1 (1) ◽  
pp. 37-45
Author(s):  
Irwansyah Irwansyah ◽  
Hendra Kusumah ◽  
Muhammad Syarif
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Public Awareness ◽  
Underground Water ◽  
Raspberry Pi ◽  
Laptop Computer ◽  
Web Browser ◽  
Disaster Monitoring ◽  
The Times ◽  
Human Desire

Along with the times, recently there have been found tool to facilitate human’s work. Electronics is one of technology to facilitate human’s work. One of human desire is being safe, so that people think to make a tool which can monitor the surrounding condition without being monitored with people’s own eyes. Public awareness of the underground water channels currently felt still very little so frequent floods. To avoid the flood disaster monitoring needs to be done to underground water channels.This tool is controlled via a web browser. for the components used in this monitoring system is the Raspberry Pi technology where the system can take pictures in real time with the help of Logitech C170 webcam camera. web browser and Raspberry Pi make everyone can control the devices around with using smartphone, laptop, computer and ipad. This research is expected to be able to help the users in knowing the blockage on water flow and monitored around in realtime.

Immediate diaphragmatic electromyogram responses to imperceptible mechanical loads in conscious humans

1992 ◽  
Vol 73 (1) ◽  
pp. 248-259 ◽  
Author(s):  
E. J. Kobylarz ◽  
J. A. Daubenspeck
Keyword(s):  
Electrical Activity ◽  
Lung Volume ◽  
Muscle Activity ◽  
Normal Subjects ◽  
Tonic Activity ◽  
Phasic Activity ◽  
Mechanical Loads ◽  
Oral Airway ◽  
Pattern Regulation ◽  
Adaptive Pattern

We used an esophageal electrode to measure the amplitude and neural inspiratory and expiratory (N TE) timing responses of crural diaphragmatic electrical activity in response to flow-resistive (R) and elastic (E) loads at or below the threshold for conscious detection, applied pseudorandomly to the oral airway of eight normal subjects. We observed a rapid first-breath neural reflex that modified respiratory timing such that N TE lengthened significantly in response to R loads in six of eight subjects and shortened in response to E loading in six of seven subjects. The prolongation of N TE with R loading resulted primarily from lengthening the portion of N TE during which phasic activity in the diaphragm is absent (TE NDIA), whereas E loading shortened N TE mainly by reducing TE NDIA. Most subjects responded to both types of loading by decreasing mean tonic diaphragmatic activity, the average level of muscle activity that exists when no phasic changes are occurring, as well as its variability. The observed timing responses are consistent in direction with optimally adaptive pattern regulation, whereas the modulation of tonic activity may be useful in neural regulation of end-expiratory lung volume.

scholarly journals Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs

Inventions ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 42
Author(s):  
Worasit Sangjan ◽  
Arron H. Carter ◽  
Michael O. Pumphrey ◽  
Vadim Jitkov ◽  
Sindhuja Sankaran
Keyword(s):  
Real Time ◽  
Imaging System ◽  
Crop Growth ◽  
Sensor System ◽  
Plant Phenotyping ◽  
Raspberry Pi ◽  
The Internet ◽  
Growth Stages ◽  
Crop Breeding ◽  
Breeding Programs

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.

scholarly journals Real-time Air Quality Index monitoring experiments using SDS011 sensors and raspberry pi

2021 ◽  
Vol 1098 (4) ◽  
pp. 042090
Author(s):  
D Kurnia ◽  
F S Hadisantoso ◽  
A A Suprianto ◽  
E A Nugroho ◽  
J Janizal
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Quality Index ◽  
Air Quality Index ◽  
Raspberry Pi
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