Design, Simulation, Implementation and Measurement of Respiration Rate by Strain Gauge Sensor and Induction Method

The human body has vital parameters such as respiration, heart rate, and body temperature. Respiratory rate (RR) is a parameter that expresses the rate of respiration per minute. Respiratory activities play an essential role in human life. The rate of respiration has a particular range, which is 14-18 cycles per minute for a healthy and normal person at rest. The oxygen in the blood enters the body during respiration and is expelled as carbon dioxide in return. Any problems with breathing may pose a serious health risk. An abnormal breathing pattern indicates serious illnesses such as cardiac arrest and hospitalization in the intensive care unit (ICU) and when it falls below a certain limit, it indicates a loss of consciousness. This is why it is so imperative to develop devices and methods measuring respiration rates.

Machine Learning Methods for Automatic Silent Speech Recognition Using a Wearable Graphene Strain Gauge Sensor

Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising graphene’s unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition.

Measuring Feed Force in Machining Using a Strain Gage

Measuring the forces that work during machining has been being concerned by researchers for years. There are three main forces that work in turning: thrust force, axial force, and radial force. Thus, feeding force measurement is needed in machine manufacturing. This research attempts to develop measurement method through feeding force, using strain gauge sensor. The aim of measurement of feeding force in this research is to find out the influence parameter of machine towards feeding force. The research used experimental method with design experiment Taguchi to know the influence of machine parameters to feeding force in turning process. The measurement tool is strain gauge sensor connected to cutting tool. The workspace is alluminium 6061 with 15 mm in diameter and 150 mm in length. The parameters for this research are speed rate (140 rpm, 215 rpm, and 330 rpm), feed rate (0,043 mm/r , 0,065 mm/r , and 0,081 mm/r), and depth of cut (0,2 mm, 0,4 mm, and 0,6 mm). The result showed that speed rate is the most significant parameter, with the contribution percentage is 92 %. Speed rate and feed rate parameter have insignificant influence. The contribution percentage of speed rate is 2% while the feed rate has % contribution percentage. The conclusion of the research is that the bigger number of speed rate, the bigger feeding force it will have.

Development of Rocket Telemetry in Chamber Gas Pressure Monitoring with the MPXV7002DP Gas Pressure Sensor

Telemetry is a process used to measure or record a physical quantity at a location far from the center of processing the measurement results. Telemetry systems on unmanned aerial vehicles can provide information such as position, altitude, direction, and status of the vehicle itself in real time when the air vehicle is operated. A rocket is a flying vehicle that moves by getting a boost through the combustion reaction that occurs in the rocket. Implementation of the strain gauge sensor through the MPXV7002DP gas pressure sensor, the amount of gas pressure is obtained at the time of combustion of the rocket and sent via wi-fi telemetry Pixhawk 447 MHz, the data on a laptop can be shown the gas pressure generated in the rocket chamber through the display of the Borland Delphi program with a distance of 150 m.