Obesity is a growing problem that has life-threatening health consequences. One way to combat obesity is by increasing physical activity levels, which has been a focus of recent applied behavioral research. The purpose of this study was to evaluate the effectiveness of goal setting and textual feedback without social support to increase physical activity, specifically weekly running distance. A multiple-baseline across participants design was employed with four participants using a Fitbit Flex accelerometer to collect two physical activity measures, intense steps and distance. Results showed a significant increase in weekly running distance for two out of four participants following the intervention.

No description available.

When it comes to monitoring human health, accuracy is not a choice. Accuracy in blood pressure (BP) estimation is essential for proper diagnosis and management of hypertension. An error of 5 mmHg is so serious, it can be responsible for doubling or halving number of patients diagnosed with hypertension. Motion artifacts are external sources of inaccuracy and can be due to sudden arm motion, muscle tremor, shivering, and transport vehicle vibration. Medium term drift, due to changing environmental factors, such as ambient temperature, can also contribute to the inaccuracy. Long term drift (ageing), can reach 9 mmHg during the first three months of usage. In this thesis, a new stage is added to current cuff based BP devices. This stage is responsible for adjusting the pressure reading before displaying it to end users. The proposed stage is provided with a 3-axis accelerometer, which makes the detection of motion artifacts during measurement possible. Moreover, it monitors changes in the ambient temperature and sensor ageing, so that it will adaptively compensate for these inaccuracies. These sources of inaccuracy are suppressed using algorithms based on Empirical Mode Decomposition (EMD), which has the feature of removing unwanted noise components little effect on the phase or the frequency distribution of the measured signal. With motion artifacts, measurements show that the proposed algorithms considerably improved the accuracy of the blood pressure estimates in comparison with the commonly-used conventional oscillometric algorithm that does not include a stage for artifact suppression, and allowed the estimates to consistent with the international ANSI/AAMI/ISO standard. Moreover, simulations based on experimental results show that the system is able to compensate for drift due to temperature changes and ageing with excellent performance. Results show promise towards building a robust BP monitor, with very low errors due to motion artifacts, environmental changes, and ageing.

As the population ages, prediction of falls risk is becoming an increasingly important research area. Due to built-in inertial sensors and ubiquity, smartphones provide an at- tractive data collection and computing platform for falls risk prediction and continuous gait monitoring. One challenge in continuous gait monitoring is that signi cant signal variability exists between individuals with a high falls risk and those with low-risk. This variability increases the di cultly in building a universal system which segments and labels changes in signal state. This paper presents a method which uses unsu- pervised learning techniques to automatically segment a gait signal by computing the dissimilarity between two consecutive windows of data, applying an adaptive threshold algorithm to detect changes in signal state, and using a rule-based gait recognition al- gorithm to label the data. Using inertial data,the segmentation algorithm is compared against manually segmented data and is capable of achieving recognition rates greater than 71.8%.

Este trabalho tem o objetivo de modelar um sistema composto por alimentador industrial vibratório, comando eletrônico e sensor acelerômetro Microelectromechanical systems - MEMS. O alimentador vibratório é um equipamento industrial utilizado para alimentação automática de peças diversas em plantas ou processos industriais semiautomáticos ou totalmente automáticos. Separa e organiza a sequência de inserção de peças em processo automático de fabricação e montagem. O modelo proposto neste trabalho reproduz o comportamento ondulatório e periódico da vibração, gerada pelo movimento descrito pela bacia do alimentador. Apresenta a análise dos resultados obtidos por meio de simulação computacional do modelo proposto, confrontados com os resultados obtidos experimentalmente. O experimento proposto tem um comando eletrônico para o acionamento do alimentador industrial vibratório. A tensão de entrada (setpoint) define a potência elétrica entregue a uma bobina eletromagnética, parte integrante do alimentador vibratório, responsável por gerar as vibrações mecânicas. Os resultados obtidos indicam que o modelo é viável. / This paper presents proposed model for a system composed of industrial vibratory feeder, electronic control and accelerometer sensor Microelectromechanical systems - MEMS. The vibrating feeder is an industrial equipment used for automatic feeding of parts in various plants or industrial processes semiautomatic or fully automatic. Separates and organizes the insertion sequence parts in automatic fabrication and assembly. The proposed model reproduces the wave behavior and periodic vibration generated by the movement described by bowl feeder. Presents the analysis of the results obtained by computer simulation of the proposed model, compared to the results obtained experimentally. The proposed experiment has an electronic command to the firing of industrial vibratory feeder. The input voltage (setpoint) defines the electrical power delivered to an electromagnetic coil, part of vibratory feeder, responsible for generating mechanical vibrations. The results indicate that the model is feasible.

This work deals with design and construction of unstable double wheeled Segway-like vehicle built for human personnel transportation and its smaller scaled clone developed for control algorithms testing. The smaller machine is controlled via Joystick and PC. This work was conducted in team consisting of three students. Individual goals are described in chapter „Stanovení cílů práce“. The beginning of the work deals with researching any similar projects concerned with this topic, especially with sensors and control algorithms used. Further, the work describes the process of choosing used electronics and its parameters. One of the problems faced during the work was the pitch angle of the vehicle base calculation - algorithm of the angle calculation had been designed by students of several world universities. The principle of how it works was studied and then tested by simulations and practically in the following chapters. Further on, the work deals with platform‘s parameter estimation, at first the testing platform made of wood, followed by the final platform made of aluminium. Parameter estimation was realized by using the multifunctional I/O card Humusoft MF 624 for PC. Part of the work deals with the final control algorithm on the dsPIC microcontroller implementation, sensor‘s outputs calculation and calibration algorithm design. Since the vehicle is built for human personnel transportation, implementation of certain safety algorithms was necessary. These algorithms should be able to detect possible fail states and prevent the driver from losing control over the vehicle in order to prevent any injuries.

The purpose of this project was to explore the possibility of using the accelerometer in a mobile device to measure acceleration instead of professional laboratory equipment. Five subjects performed ten vertical jumps each. The equipment used for the purpose of this project was a mobile device, force plate and a motion capture system, of which the last one mentioned was used as a reference for comparison against the others. The data from the mobile device was split into two groups of datasets, where the first one was the nominal acceleration from the raw data and the second one the acceleration when the phones orientation was taken into consideration. The height was calculated by using the double summation, take-off velocity and flight-time. All the data was compiled where the mean deviation, standard deviation, R-value and R-Squared was calculated for each method. The take-off velocity was later used as the final method to give it a fair and equal comparison. The result showed that the force plate was significantly better and more reliable than the mobile device. When comparing the different datasets from the phone, the orientation adapted data performed better than the raw data. The conclusion made from this project was that the force plate is still significantly better than the mobile device regardless of the type of data, but the orientation adapted data demonstrates a result in the right direction.

Smartphone's are extremely popular and in high demand nowadays. They are easy to handle and very intuitive compared with old phones for end users. Approximately two billion people use Smartphones all over the world, so it is clear that these phones are very popular. One of the major issues of these smart phones is theft. What happens if someone steals your phone? Why should we try to secure our phones? The reason is that, even if the phone is stolen, the thief should not be able to open and use it through unlocking easily. People are generally careless while typing their password/pin code or drawing a pattern while others are watching. Maybe someone can see it just by standing next to or behind the person who is typing the pin or drawing the pattern. This scenario of getting the information is called shoulder surfing. Another scenario is to use a hidden camera, so-called Record monitoring. Shoulder surfing can be used by an attacker/observer to get passwords or PINs. Shoulder surfing is very easy to perform by just looking over the shoulder when a user is typing the PIN or drawing the unlock pattern. Record monitoring needs more preparation, but is not much more complicated to perform. Sometimes it also happens that the phone gets stolen and by seeing fingerprints or smudge patterns on the phone, the attacker can unlock it. These above two are general security threats for smart phone users. This thesis introduces some different approaches to overcome the above mentioned security threats in Smartphones. The basic aim is to make it more difficult to perform shoulder surfing or record monitoring, and these will not be easy to perform by the observer after switching to the new techniques introduced in the thesis. In this thesis, the usability of each method developed will be described and also future use of these approaches. There are a number of techniques by which a user can protect the phone from observation attacks. Some of these will be considered, and a user interface evaluation will be performed in the later phase of development. I will also consider some important aspects while developing the methods such as -user friendliness, Good UI concepts etc. I will also evaluate the actual security added by the methods, and the overall user impression. Two separate user studies have been performed, first one with students from the Computer Science department, and then one with students from other departments. The results indicate that students from Computer Science are more attracted to the new security solution than students from other departments.

Work in demanding postures is a known risk factor for work-related musculoskeletal disorders (MSDs), specifically work with elevated arms may cause neck/shoulder disorders. Such a disorder is a tragedy for the individual, and costly for society. Technical measurements are more precise in estimating the work exposure, than observation and self-reports, and there is a need for uncomplicated methods for risk assessments. The aim of this project was to develop and validate an iOS application for measuring arm elevation angle. Such an application was developed, based on the built-in accelerometer and gyroscope of the iPhone/iPod Touch. The application was designed to be self-exploratory. Directly after a measurement, 10th, 50th and 90th percentiles of angular distribution and median angular velocity, and percentage of time above 30°, 60°, and 90° are presented. The focused user group, ergonomists, was consulted during the user interface design phase. Complete angular datasets may be exported via email as text files for further analyses. The application was validated by comparison to the output of an optical motion capture system for four subjects. The two methods correlated above 0.99, with absolute error below 4.8° in arm flexion and abduction positions. During arm swing movements, the average root-mean-square differences (RMSDs) were 3.7°, 4.6° and 6.5° for slow (0.1 Hz), medium (0.4 Hz) and fast (0.8 Hz) arm swings, respectively. For simulated painting, the mean RMSDs was 5.5°. Since the accuracy was similar to other tested field research methods, this convenient and “low-cost” application should be useful for ergonomists, for risk assessments or educational use. The plan is to publish this iOS application on Apple Store (Apple Inc.) for free. New user feedback may further improve the user interface.

Sedentary behavior (SB) contributes to many negative health-related outcomes. Motivation to reduce SB and increase physical activity (PA) are necessary to reduce co-morbidities. Tracking SB and PA provides objective data to help promote wellness. The purposes of this quasi-experimental study were to 1) determine the accuracy of three commercially available activity trackers compared to research-grade accelerometers, and 2) explore whether using these activity trackers led to a change in activity level one week after gathering baseline data. Activity trackers used in this study were Apple Watch, Fitbit Surge, and Microsoft Band 2. A convenience sample of college-age students and community members wore the research-grade ActiGraph 3GTX+ accelerometer on the non-dominant wrist for one week. Participants returned and the activity tracker was added to the non-dominant wrist with the ActiGraph 3GTX+ for another week. All activity trackers significantly differed from the ActiGraph accelerometers. Fitbit Surge had a significant regression equation that could adjust for this difference, but not Apple Watch or Microsoft Band 2. Participants had below average sedentary time, exhibiting 288.4 min/day (SD 100.7) of SB. They also surpassed United States PA standards, averaging 130.3 (SD 48.8) min/day of moderate-to-vigorous physical activity. Few significant changes in activity level transpired between week 1 and week 2. In a group that already has low SB and high PA, activity trackers do not seem to make an impact on activity levels. Further testing is required to determine if activity trackers are motivating to reduce SB and increase PA in groups with different activity profiles.