Gyroscope

A Gough-Stewart platform is a type of parallel robot that has six prismatic actuators, commonly hydraulic jacks or electric actuators, attached in pairs to three positions on the platform's baseplate, crossing over to three mounting points on a top plate. Devices placed on the top plate can be moved in the six degrees of freedom in which it is possible for a freely-suspended body to move. These are the three linear movements x, y, z (lateral, longitudinal and vertical), and the three rotations pitch, roll, & yaw.
Mpu 6050 Gyroscope Sensor, Arduino Mega 2560, Processing IDE based on OPENGL and Java Language. The system is designed for stabilizing the mass on top of the system, horizontal stabilization. System operated in 3 axis for stabilization to occur. Gyroscope sensor used to read the current position of platform before the stabilization occurs, system will try to read the upper platform of the robot and try to make adjustment or counter-action for the lower base and 6 legged rod to try make the upper platform horizontally stable. This system is multi usage for condition that require stabilization but in this project is for turret stabilization in a ship for optimizing aiming and firing performance. System using simple inverse control that inverting the position that given by gyroscope that applied in 6 actuator in form of motor servo. User interface is alsa implemented using Processing IDE
Derived from design and testing can be concluded that the device stabilization  performance to counterback to the 0 degree ranged from 2 to 10 degree rotation. From the 10 to 30 degree stabilization stabilization performance is dropped up to 50% because of mechanical limitation.
Keywords :  Position Sensor, Gyroscope, Stewart Platform, Inverse Control

Sensor devices are an integral part of human movement analysis.  The use of sensors in sports performance and injury prevention is extremely useful.  Over the years wireless systems have become increasingly popular and the analysis of human movement has been therefore simplified.  Inertial measurement units are very popular in physical activity monitoring and have become readily available for consumers.  The use of more advanced sensors to measure electrical and mechanical muscle activity tend to be tethered devices, thus data collection on more complex movements is limited.  This paper looks at the need for wireless solutions to these sensors and the proposal for a multi-channel sensor which incorporates all the necessary sensors for complete human movement analysis.  Wireless systems have the advantage of allowing the participant or athlete to perform the movement in their ecologically valid environment while giving both reliable and accurate results to the practitioner.  In terms of human movement, to achieve accurate information about the position, kinematic and external forces of the segments of the body being studied, a single multi-channel sensor device is necessary.  The combination of sensors in one device can give accurate information on muscle activity, forces, directionality and acceleration.

In this paper sudden shifts of the poles are proposed as an explanation for the recurrent geological crisis during which volcanic eruptions, orogenesis, climatic changes, sea levels variations, together with mass extinctions occur. Such shifts are usually considered impossible on the assumption that only a planetary impact could change in a significant way the Earth’s momentum. The position of the poles, however, is determined by the equatorial bulges; if they are “reshaped” for some reason, the poles shift without any significant change of the momentum. Due to the enormous extend of its oceans, Earth is an inherently unstable planet, so much so that a kick apparently irrelevant can trigger a process that in a very short period, that is instantaneously in geological time, provokes a reshaping of the equatorial bulge, thus making the poles shift permanently. When this happens all the masses on its  surface, atmosphere, oceans and continents, are suddenly subjected to tangential forces, thus triggering a global catastrophe and long lasting effects for what concerns climate, volcanism and circulation cells of the mantle.

—In this paper, we propose an approach to understand the driver behavior using smartphone sensors. The aim for analyzing the sensory data acquired using a smartphone is to design a car-independent system which does not need vehicle mounted sensors measuring turn rates, gas consumption or tire pressure. The sensory data utilized in this paper includes the accelerometer, gyroscope and the magnetometer. Using these sensors we obtain position, speed, acceleration, deceleration and deflection angle sensory information and estimate commuting safety by statistically analyzing driver behavior. In contrast to state of the art, this work uses no external sensors, resulting in a cost efficient, simplistic and user-friendly system.

Abstrak Seiring dengan kemajuan teknologi, kemampuan perangkat-perangkat pintar semakin dikembangkan. Mulai dari digitalisasi antarmuka (Interface), kemampuan pengolahan (Processing), sampai pemberian sensor-sensor pembantu. Salah satu alat vital dalam perangkat-perangkat pintar ini adalah sensor. Sensor berfungsi sebagai pendeteksi perubahan-perubahan lingkungan kemudian diubah menjadi nilai input yang akan diolah. Banyak sekali jenis sensor yang digunakan di perangkat-perangkat pintar. Seperti perangkat navigasi dan orentasi untuk mengetahui arah dan posisi pengguna. Dalam suatu sistem navigasi dibutuhkan ketepatan dalam penentuan keberadaan dan pergerakan suatu benda. Salah satu bagian penting dalam sistem navigasi adalah pendeteksi rotasi. Banyak sekali instrumen yang dapat digunakan untuk mendeteksi rotasi dan salah satunya adalah sensor gyroscope. Sensor gyroscope memiliki kelebihan yaitu sensor ini tidak bersentuhan langsung secara fisik dengan lingkungan sekitar sehingga sangat cocok digunakan pada benda yang bergerak bebas. Oleh karena itu, pada paper ini akan dibahas tentang salah satu sensor pendeteksi orientasi, yaitu Gyroscope lisy300.

In recent years, monitoring a road condition becomes an utmost important. This paper introduces a framework to
monitor road condition, based on sensors (accelerometer, gyroscope and GPS) built in a smartphone. Using machine learning
system, quality of different road sections is obtained and it is possible to visualize a road quality map of a selected region.
Further, this system generates a personal warning system for road condition along with development of a historical record of
road conditions. This provides a constructive feedback to drivers and local authorities. The system reduces the cost of external
hardware, development and maintenance cost involved

In this paper the research surrounding the Augmented Reality in games on Android platform was performed by testing 108 games from Google Play Market and by analyzing the hundreds of user reviews to determine the level of acceptance and the level of technical stability of the mobile games based on that technology. The Location-based, Marker-based and games based on somewhat different approach were studied and compared by the runtimes, game genres and by the featuring aspects including the presence of multiplayer mode, sound effects and the dimension that the virtual objects were positioned in. The overview of the studied games was presented in this paper. The results, for instance, include that the AR game variation is very narrow in terms of gameplay style and technical issues are very commonly encountered and it makes them very influential to the gameplay experience. The rareness of the multiplayer mode among the AR games was discovered meaning the domination of the single-player game designs.

It is widely accepted that human activities largely contribute to global emissions and thus, greatly impact climate change. Awareness promotion and adoption of green transportation mode could make a difference in the long term. To achieve behavioural change, we investigate the use of a persuasive game utilising online transportation mode recognition to afford bonuses and penalties to users based on their daily choices of transportation mode. To facilitate an easy identification of transportation mode, classification predictive models are built based on accelerometer and gyroscope historical data. Preliminary results show that the classification true-positive rate for recognising 10 different transportation classes can reach up to 95% when using a historical set (66% without). Results also reveal that the random tree classification model is a viable choice compared to random forest in terms of sustainability. Qualitative studies of the trained classifiers and measurements of Android-...

This paper presents a human action recognition system that runs in real-time and uses a depth camera and an inertial sensor simultaneously based on a previously developed sensor fusion method. Computationally efficient depth image features and inertial signals features are fed into two computationally efficient collaborative representative classifiers. A decision-level fusion is then performed. The developed real-time system is evaluated using a publicly available multimodal human action recognition dataset by considering a comprehensive set of human actions. The overall classification rate of the developed real-time system is shown to be more than 97% which is at least 9% higher than when each sensing modality is used individually. The results from both offline and real-time experimentations demonstrate the effectiveness of the system and its real-time throughputs.

Vehicles play a major role in this globalization era and their increasing use in everyday life comes with greater risks for accidents. On the other hand, people are not really aware of the necessity for safe driving, as evident in the number of people breaking traffic regulation. This research is aimed at building a system of vehicle velocity and acceleration monitoring using an accelerometer with ADXL345protocol and Raspberry Pi minimum system. The program in Raspberry Pi is written using Python. Programming is initialized with SMB (system management bus) function, followed by setting for data transfer velocity and command program to read the accelerometer, as well as a command to display data output. For validation purposes, output from the accelerometer sensor system is compared to that of accelerometer sensor embedded in cellular telephone using accelerometer analyzer software. Sensor readings are kept in files of text format. I. INTRODUCTION Vehicles play major roles in this globalization era, starting from their core function as transportation means, distribution of goods, down to vehicle modification for specific purposes such vehicles for heavy duty work, firefighter trucks, and even to race cars that become a sport. Increasing number of vehicle use in everyday life lead to more accidents. This is made worse by the fact that people are not yet really aware of driving safety, with many of them still breaking traffic regulation. Traffic accidents are caused by the arrogance and ego of drivers (human error), lack of road infrastructure and vehicle conditions, among other things. One of the ways to prevent accidents is by making a vehicle velocity and acceleration monitoring instrument. This is meant to know vehicle condition on the road and whenever an accident takes place. Vehicle velocity and acceleration can be detected to determine exactly when an accident may take place and the possible extent of the resulting impact.

Resumen: El presente trabajo logró cumplir con su objetivo general sobre mejorar la técnica de tenis de mesa del club BTY PERÚ-TRUJILLO al implementar «SporTTec», habiendo estudiado el movimiento TopSpin de 5 tenimesistas para las pruebas de Pre y Post TEST (investigación pre-experimental) en 1 mes por cada fase, se tomaron como población y muestra (24 en total) a los promedios de evaluaciones semanales y a los promedios de temperaturas corporales de 3 intercalados días a la semana, los cuales fueron obtenidos mediante fichas de recolección de datos; se usaron 3 pruebas paramétricas (T de Student) y 1 no paramétrica (Mc Nemar) de muestras relacionadas / estudio longitudinal (dos medidas) tras aplicar pruebas de normalidad de datos. «SporTTec» fue construido con la placa Arduino UNO, a la cual se le instalaron un sensor de temperatura y un módulo giroscopio adaptados (con costuras) a dos accesorios deportivos; se utilizó "Modelo en V" como método de desarrollo debido al uso principal de hardware; finalmente, los datos en tiempo real son mostrados en el "Monitor Serie" que brinda el software de arduino. Los resultados se muestran favorables al haber mejorado el giro de cintura (+13.82% y +29.90%) y la inclinación del tren superior (+51.54%), así como al haber disminuido la probabilidad de lesiones (-58.33%). Descriptores: sistema inteligente, técnica topspin, arduino, giroscopio, temperatura.

Abstract: This study achieved its main objective, which was to improve the technique in table-tennis at the BTY PERU-TRUJILLO club with the use of «SporTTec». After studying the TopSpin technique of 5 table-tennis players for pre and post-testing (pre-experimental research) of one month for each phase, the average results of weekly evaluations and average body temperatures for three alternate days a week were taken as samples (24 total). Said results were obtained through data collection forms. The tests used consisted of 3 parametric tests (Student's t-test) and a non-parametric (Mc Nemar) of paired-samples / longitudinal study (two measures) after applying data normality tests. «SporTTec» was built with the Arduino UNO board, to which a temperature sensor and gyroscope module were installed, adapted (with seams) to two accessories. "V Model" was used as the development method, given the primary hardware use. Finally, real-time data is shown in the "Monitor Series" which is provided by the Arduino software. Results were positive, since they improved hip-torque (+13.82% and +29.90%) and upper-body inclination (+ 51.54%) as well as decreasing the likelihood of injury (-58.33%). Keywords: intelligent system, topspin technique, Arduino, gyroscope, temperature.

Precision swine production can benefit from autonomous, noninvasive, and affordable devices that conduct frequent checks on the well-being status of pigs. Here, we present a remote monitoring tool for the objective measurement of some behavioral indicators that may help in assessing the health and welfare status—namely, posture, gait, vocalization, and external temperature. The multiparameter electronic sensor board is characterized by laboratory measurements and by animal tests. Relevant behavioral health indicators are discussed for implementing machine learning algorithms and decision support tools to detect animal lameness, lethargy, pain, injury, and distress. The roadmap for technology adoption is also discussed, along with challenges and the path forward. The presented technology can potentially lead to efficient management of farm animals, targeted focus on sick animals, medical cost savings, and less use of antibiotics.

Inertial measurement units (IMUs) are fundamental for attitude control of drones. With the advancements in micro-electro-mechanical systems (MEMS) fabrication processes, size, power consumption, and price of these sensors have reduced significantly and attracted many new applications. However, this came at the expense of sensors requiring frequent recalibration, as they are highly contaminated with systematic errors. This paper presents a novel method to jointly calibrate the accelerometer, gyroscope, and magnetometer triad in a MEMS IMU without additional equipment. Opportunistic zero change in velocity and position updates, and inclination updates were used in conjunction with relative orientation updates from magnetometers in a robust batch least-squares adjustment. Solutions from the proposed self-calibration were compared to existing calibration methods. Empirical results suggest that the new method is robust against magnetic distortions and can achieve performance similar to a specialized calibration that uses a more accurate (and expensive) IMU as reference. The jointly estimated accelerometer and gyroscope calibration parameters can deliver a more accurate dead-reckoning solution than the popular multi-position calibration method (i.e., 54% improvement in orientation accuracy) by recovering the gyroscope scale error and other systematic errors. In addition, it can improve parameter observability as well as reduce calibration time by incorporating dynamic data with static orientations. The proposed calibration was also applied on-site pre-mission by simply waving the sensor by hand and was able to improve the orientation tracking accuracy by 73%.

Sound spatialisation is an important component in interactive per- formances as well as in game audio and virtual or mixed reality systems. HCI practices are increasingly focused on creating a natural user experience and embodied interaction through gestu- ral control. Body movements that coincide with sounds consist of both performed ‘sound producing‘ gestures, and ancillary and communicative movements. Thus, different gestural typologies may relate to the same audio source. Furthermore, gestures may depend on the context in which they have been expressed; in other words, they can carry different semantic or semiotic meanings in relationship to the situation and environment or reality in which they have been enacted. In order to explore these research themes, we are developing gSPAT: a software and hardware system able to drive live sound spatialisation for interactive audio performance using gestural control based on human-meaningful gesture-sound relationships. The ultimate aim is to provide a highly natural and musically expressive sound spatialisation experience for the per- former. Here we describe three experiments conducted to explore the possible directions for the future of gSPAT’s development. The tests employ a range of practice-based and ethnographic research methods to establish applicability, naturalness and usability across a range of approaches to the interaction design of the system.

This paper presents a fusion approach for improving human action recognition based on two differing modality sensors consisting of a depth camera and an inertial body sensor. Computationally efficient action features are extracted from depth images provided by the depth camera and from accelerometer signals provided by the inertial body sensor. These features consist of depth motion maps and statistical signal attributes. For action recognition, both feature-level fusion and decision-level fusion are examined by using a collaborative representation classifier. In the feature-level fusion, features generated from the two differing modality sensors are merged before classification while in the decision-level fusion, the Dempster-Shafer theory is used to combine the classification outcomes from two classifiers, each corresponding to one sensor. The introduced fusion framework is evaluated using the Berkeley Multimodal Human Action Database. The results indicate that due to the complementary aspect of the data from these sensors, the introduced fusion approaches lead to from 2% to 23% recognition rate improvements depending on the action over the situations when each sensor is used individually.

This paper examines the use of sensor devices in sports biomechanics, focusing on current frequency of use of Electromyography (EMG) device preferences. Researchers in the International Society of Biomechanics in Sports were invited to participate in an online survey. Responses on multiple sensor devices highlighting frequency of use, device features and improvements researchers sought in acquisition and analysis methods were obtained via an online questionnaire. Results of the investigation showed that the force platform is the most frequently used device, with inertial measurement units and EMG devices growing in popularity. Wireless functionality and ease of use for both the participant and the practitioner proved to be important features. The main findings of the survey demonstrated need for a simple, low power, multi-channel device which incorporates the various sensors into one single device. Biomechanists showed they were looking for more availability of wireless sensor devic...

This paper performs determination of a mathematical model of the balancing robot, its linearization and algorithm for calculation of stabilization of the deflection angle from the vertical modal method. This algorithm also makes determination of the angle of deviation from the use of either alpha-beta filter and the angle of rotation of the wheels by means of encoders.

A method for human activity recognition using mobile phones is introduced. Using the accelerometer and gyroscope typically found in modern smartphones, a system that uses the proposed method is able to recognize low level activities, including athletic exercises, with high accuracy. A Hebbian learning preprocessing stage is used to render accelerometer and gyroscope signals independent to the orientation of the smartphone inside the user's pocket. After preprocessing, a selected set of features are obtained and used for classification by a k-nearest neighbor or a multilayer perceptron. The trained algorithm achieves an accuracy of 95.3 percent when using the multilayer perceptron and tested on unknown users who are asked to perform the exercises after placing the mobile device in their pocket without any constraints on the orientation. Comparison of performance with respect to other popular methods is provided.

A practical solution for ELIMINATING All Parkinson's Tremors

New solution to Parkinson's Tremors, with an impressive video on an old man.

I started USING various versions of this device I invented in 2007, because I have fairly serious Parkinson's Tremors, and have had for many years. The two devices which I have used most are my two "gloves" which are each standard sturdy workman's (welder's) gloves which each have a battery-powered, motorized gyroscope on the back of it. The results have been amazing!

A method for human activity recognition using mobile phones is introduced. Using the accelerometer and gyroscope typically found in modern smartphones, a system that uses the proposed method is able to recognize low level activities, including athletic exercises, with high accuracy. A Hebbian learning preprocessing stage is used to render accelerometer and gyroscope signals independent to the orientation of the smartphone inside the user's pocket. After preprocessing, a selected set of features are obtained and used for classification by a k-nearest neighbor or a multilayer perceptron. The trained algorithm achieves an accuracy of 95.3 percent when using the multilayer perceptron and tested on unknown users who are asked to perform the exercises after placing the mobile device in their pocket without any constraints on the orientation. Comparison of performance with respect to other popular methods is provided.