Among the elderly, falling represents a major threat to the individual health, and is considered as a major source of morbidity and mortality. In Sweden alone, three elderly are lost each day in accidents related to falling. The elderly who survive the fall are likely to be suffering from decreased quality of life. As the percentage of elderly increase in the population worldwide, the need for preventive methods and tools will grow drastically in order to deal with the increasing health-care costs. This report is the result of a conceptual study where an algorithm for individualized motion monitoring and pre-impact fall detection is developed. The algorithm learns the normal state of the wearer in order to detect anomalous events such as a fall. Furthermore, this report presents the requirements and issues related to the implementation of such a system. The result of the study is presented as a comparison between the individualized system and a more generalized fall detection system. The conclusion is that the presented type of algorithm is capable of learning the user behaviour and is able to detect a fall before the user impacts the ground, with a mean lead time of 301ms. / Bland äldre är risken för att drabbas av fallrelaterade skador överhängande, ofta med svåra fysiska skador och psykiska effekter som följd. Med en ökande andel äldre i befolkningsmängden beräknas även samhällets kostnad för vård att stiga. Genom aktiva samt preventiva åtgärder kan graden av personligt lidande och fallre- laterade samhällskostnader reduceras. Denna rapport är resultatet av en konceptuell studie där en algoritm för aktiv, individanpassad falldetektion utvecklats. Algoritmen lär sig användarens normala rörelsemönster och skall därefter särskilja dessa från onormala rörelsemönster. Rapporten beskriver de krav och frågeställningar som är relevanta för utvecklingen av ett sådant system. Vidare presenteras resultatet av studien i form av en jämförelse mellan ett individanpassat och generellt system. Resultatet av studien visar att algoritmen kan lära sig användarens vanliga rörelsemönster och därefer särskilja dessa från ett fall, i medelvärde 301ms innan användaren träffar marken.

No description available.

Over the past two decades, Micro-Electro-Mechanical System (MEMS) based accelerometers, benefiting from relatively simple structure, low-power consumption, high sensitivity, and easy integration, have been widely used in many industrial and consumer electronics applications. For the high precision accelerometers, a significant technical challenge is to design a low-noise readout circuit to guarantee the required high resolution of the entire integrated system. There are three main approaches for improvement of the noise and offset of the readout circuit, namely auto-zero (AZ) and correlated double sampling (CDS) for the switched- capacitor (SC) circuit and chopper stabilization (CHS) for the continuous-time circuit. This thesis investigates the merits and drawbacks of all three techniques for reading the capacitance of a low noise MEMS accelerometer developed in our group. After that, we compare the different effects of the three technologies on noise, offset, output range, linearity, dynamic range, and gain. Next, we present the design of the most suitable structure for our sensor to achieve low noise, low offset, and high precision within the working frequency. In this thesis, the design and post-layout simulation of the circuit is proposed, and the fabrication is currently in progress. The readout circuit has reached the noise floor of the sub-μg, which meets the strict requirements of low noise MEMS capacitance-to-voltage converter. A high-performance accelerometer system is regarded as the core of a low-noise, high-resolution geophone. We show that together with the MEMS accelerometer sensor, the readout circuit provides competitive overall system noise and guarantees the required resolution.

It is very common in everyday life for people to use data generated by sensors like accelerometers and gyroscopes, whether they are on the mobile phone, smartwatch or other smart devices, for analysis of their movement or tracking their habits. This study is focused on boxing, and proposes a test where the generated data are put through machine learning algorithms in order to output information on the type of punch thrown by the user. Furthermore, the possibility of implementing ML on Android is examined. This thesis was performed by conducting a literature study, and an experimental study. For the literature study, researches similar to this were examined to gather information and insight on what the most common practices are, regarding the setup of the device used to collect the data, both in terms of sensor placement on the body and sensor setup like the optimal data output rates. The experimental part was conducted using custom hardware implementing an accelerometer and a gyroscope in which the wearer of this device would proceed to throw 6 types of punches (jab, cross, left & right uppercut, and left & right hook) to generate the data to be analyzed. It was technically possible to use Android for ML, but it was the least optimum way to execute the algorithms, so a PC was used instead. After putting the data through multiple ML algorithms, the results show that with our current hardware set up it was not possible for the ML algorithms to adequately classify the type of punches with mediocre accuracy scores ranging from 37.37% - 59.16% depending on the algorithm.

Vid undersökandet av röstkällan, och då framförallt röstkvalitet ur ett lingvistiskt perspektiv, är inspelning med accelerometer, eller strupmikrofon, en lovande metod som dock är i behov av förbättring. En ännu okänd aspekt är om vokalkvalitet påverkar korrelationen mellan sig- nalerna från strupe och mun, vilket denna studie ämnade klargöra. I studien ingick 8 friska, vuxna försöksdeltagare som fick yttra sekvenser av /pa/, /pi/, /pu/ och /pæ/, med olika intensi- tet, 3 olika tonhöjder och röstkvaliteterna modal, läckande och pressad. Resultaten visade att en regressionsmodell som tar hänsyn till vokalkvalitet har lägre MAE (en förbättring med i genom- snitt 0,34 dB SPL) och högre R2 än en baselinemodell utan någon interaktionsvariabel. Slutna vokaler tenderade att ha en flackare regressionskurva än öppna vokaler. Motsvarande modell gjordes också för röstkvalitet, och även den visade sig vara något bättre än baselinemodellen, dock utan särskilt tydliga tendenser. Vokalkvalitet har således en signifikant, men liten, effekt på förhållandet mellan accelerometer- och mikrofonsignal.

OBJECTIVE: Excess body weight in children has become a serious public health concern worldwide. The Centers for Disease Control and Prevention (CDC) demonstrated that childhood obesity has tripled since the 1970s in the United States. To prevent childhood obesity, the CDC recommends that children achieve 60 minutes of moderate to vigorous physical activity each day. A variety of wearable monitors are available for objectively assessing activity but these methods are complicated by the lack of established values for the activity intensity and comparability across devices. The purpose of this study is to establish activity intensity cutpoins for the MotionWatch8 (MW8) accelerometer in children by comparison with the gold standard cutpoints established for the Actigraph GT1M accelerometer. MATERIAL/METHODS: 40 children (ages 9-13 years) from Syracuse, NY were enrolled in this study. All participants were required to wear the two different monitors on the dominant wrist as they performed a resting activity (4-minute sitting), a 4-minute slow-paced walk, a 4-minute faster-paced walk, and a 2-minute vigorous running game to mimic the different intensities a child might perform in a free-living environment. Linear regression and receiver operating characteristic (ROC) curves were performed to assess sensitivity and specificity of MW8 intensity cutpoints. RESULT: Mean value for each activity were positively correlated between MW8 and the Actigraph (r=0.85, p<0.001). The optimal cutpoints for differentiating sedentary from light physical activity, light from MVPA, and moderate from vigorous activity were (≤32 counts, ≥ 371.5 counts, and ≥ 859.5 counts per 30 second interval, respectively). CONCLUSION: The MW8 is a simple and objective instrument for measuring physical activity in children. This study provides usable cutoff values for further testing the validity of the MW8 for measuring physical activity patterns among children.

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The Thesis of the dissertation work present results of the accelerometer test operation on board of the Russian small spacecraft Universat – 2. This technical experiment was performed in the frame of the project TEASER (Technological Experiment And Space Environmental Resistance) financed by Ministry of Industry and Trade of the Czech Republic. The principal aim of the project was development and application of end-to-end tests procedures for MAC04TS. Generally, End-to-end Testing is intended for verification of the new product comprehensive operational sequence. In the context of the accelerometer this process includes following aspects: On-Ground Qualification of the instrument flight model and its integration with space platform, operation in orbit to verify the device functionality and performances. The MAC04TS is the new modification of the triaxial electrostatic high sensitive microaccelerometer MAC (or MACEK) designed for measurement of non-gravitational accelerations acting to orbiting spacecraft. Mentioned non-conservative perturbations are primary constraint for theoretical predictions of the spacecrafts orbit evolution. Problem of the precise orbit determination is important not only for ballistic mission analysis and support but at planning and realization of the science researches in Geophysics, Geodesy or in Space Physics branches… Presented text to examination is devoted to partial task of the MAC04TS end-to-end testing, especially, measurement and analysis of the micro-gravitational accelerations in the instrument position on board of the spacecraft. Solution of the problem is based on two methods. The first method was based on the calculation of the accelerations by telemetry information about satellite attitude motion. The second method consisted in direct measuring the accelerations by the triaxial low frequency accelerometer MAC04TS and subsequent smoothing measurement data. Comparison of the acceleration values, obtained by different ways, was carried out as a result of constructing the approximation of the measured acceleration values by their calculated values. The approximation was constructed by the least squares method. Both methods gave similar results. The received estimations of the accelerometer measurements can be used for the analysis of the accelerometer verification.

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University students experience negative emotions due to their study situation, which adversely affects their academic performance. Previous between-group and between-individual studies have demonstrated that cognition, physical activity, and sleep serve as predictors of academic performance. The objective of this pilot study was to examine the potential relationship between physical activity, sleep, and cognition within days, as well as to explore whether physical activity and sleep have a bidirectional relationship within days. This observational study consisted of five N-of-1 studies involving university students aged 22-29. Physical activity levels were measured using accelerometers, while cognition was assessed using the memory span test within the m-Path app. Sleep quality and duration were self-assessed through the ecological momentary assessment approach within the m-Path app. The study did not yield statistically significant findings. It is important to note that the study's methodological choices may have hindered the detection of potentially significant associations and effects. Future studies should employ more objective measurement methods, increase the number of measurement occasions, and include a more diverse sample group in order to further investigate these relationships.