On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

sEMG Classication with Convolutional Neural Networks: A Multi-Label Approach for Prosthetic Hand Control

In myoelectric prosthesis design, there is often a trade-off between control robustness and range of executable movements. As a low movement error rate is necessary in any real application, this often results in a quite severe limitation on the dexterity of the user. One possible remedy for this could come from the use of multi-label machine learning methods, where complex hand movements can be expressed as the sum of several simple movements. I investigate how effective state of the art deep learning methods are at classifying HD-sEMG signals. Notable weight is put on extracting multilabel information from both the spatial and temporal signal domain of EMG signals by use of convolutional neural networks (CNN). In addition, to investigate the feasibility of reducing the number of necessary electrodes, a novel method for quantifying channel importance is proposed. I show that multi-label classication performance can rival that of classical single-label methods, even with a large set of labels. Despite the general stochasticity of sEMG signals, no manual feature engineering is necessary and a very short time window is suficient for accurate classication.Klassificering av Elektromyogram med Faltningsneuronnätverk På senaste tiden har metoder inom maskininlärning fått stort genomslag i en mängd akademiska discipliner och kommersiella tillämpningar. Speciellt intressanta är så kallade artificiella neuronnätverk; en klass av självlärande algoritmer som efterliknar informationsbehandling i biologiska system. I detta arbete nyttjades en specifik typ av sådant nätverk som kallas faltningsneuronnätverk (Convolutional Neural Network, eller CNN), känt från bildanalys, för att behandla elektromyografisk data med syftet att kunna styra handproteser. Till skillnad från tidigare, besläktade metoder användes en multilabel-model, där komplicerade rörelser kan representeras som en kombination av enklare handtillstånd. Bland de muskler som är ansvariga för att styra den mänskliga handen och handleden, särskilt vad gäller böjning och utsträckning av fingrarna, är majoriteten belägna i underarmen. Även vid amputation eller annan handberövande händelse är det alltså sannolikt att en stor del av denna struktur bevaras. I den långa och mycket komplexa processkedja som styr handen, från att tanken uppstår till att själva rörelsen sker, är det i själva verket endast det absolut sista steget, själva manöverorganet, som skulle saknas. En typ av protes som nyttjar detta kvarvarande system för styrning är myoelektriska proteser. När en muskel brukas genereras elektriska potentialskillnader i och kring sagda muskel, detta en direkt konsekvens av stimuleringen från nervsystemet. Resultatet av att mäta dessa spänningar kallas ett elektromyogram, eller EMG, och är mycket användbart i många kliniska och diagnostiska situationer. Myoelektriska handproteser använder sig utav EMG från återstoden av armen för att styra själva handen och/eller handleden. Trots att sådana anordningar funnits sedan 1950-talet så lider de fortfarande av flera väsentliga nackdelar som hindrar utbredd användning; i synnerhet svårighet med förutsägbarhet under användning och begränsning av antal möjliga handrörelser. I detta arbete föreslogs en utveckling av det myoelektriska proteskonceptet som förhoppningsvis skulle minska denna nackdel med hjälp av moderna metoder från maskininlärning. Tanken är att det ska vara möjligt att automatiskt extrahera information om rörelser från EMG, så att användningen av protesen blir helt likvärdigt med att använda en riktig hand. Mätningar gjordes med ett rutnät av elektroder placerade på huden längs armen på en försöksperson. Totalt bidrog 3 försökspersoner med mätdata medan de utförde handrörelser. Därefter förbehandlades dessa mätningar till en serie med bilder, där varje bild representerar det elektriska tillståndet i armen vid ett givet ögonblick. Varje sådan bild paras också med ett så kallat label set som beskriver handens och handledens tillstånd i mätögonblicket. Dessa bilder kunde sedan användas för att träna och utvärdera en bildklassificeringsalgoritm; i detta fall ett faltningsneuronnätverk, som försöker gissa vilket label set som passar för en given bild. Förhoppningen är då alltså att nätverket ska kunna generalisera och berätta vilken rörelse som sker i helt nya observationer. Resultaten från metodiken ovan visade sig vara mycket lovande, med precision och känslighet på samma nivå som tidigare metoder trots den mycket stora mängden möjliga rörelser. Bäst resultat uppnåddes då nätverket tränades till att känna igen vilken rörelse som sker över en sekvens av bilder motsvarande ett tidsintervall och inte bara ett enskilt ögonblick. Det upptäcktes att det fanns stora skillnader i resultat för olika försökspersoner, något som antas bero på själva datainsamlingsmetoden som var känslig för felaktigt utföra rörelser

Architectural design methods used in engineering Master\u27s thesis projects

By letting structural engineering thesis students explore questions using architectural design\ua0methods, they creatively and systematically addressed\ua0 holistic questions while maintaining\ua0a technical depth. The approach may serve as a model to increase engineering students\u27\ua0ability to insightfully contribute to solutions for complex societal problems

Should Torroja’s prestressed concrete Alloz aqueduct be thought of as a beam or a shell?

This paper examines the structural action of Eduardo Torroja’s Alloz aqueduct, completed in 1939, to see whether we should think of it as acting as a beam or a shell. This is of interest regarding the Alloz aqueduct itself, but also in the design of similar structures in the future, where we must have a simple conceptual understanding of how we want it to work.We apply two alternative approaches available at that time, before computers. Firstly, the membrane theory of shells, effectively assuming the aqueduct walls are infinitely flexible in bending, and secondly, the Euler–Bernoulli ‘plane sections remain plane’ elementary beam theory. We also review Torroja’s calculations which were based on an elaboration of the Euler–Bernoulli beam theory know as the Griffith–Taylor theory for the bending of cantilevers, although we are uncertain as to why he decided to use the Griffith–Taylor theory for a thin walled structure.Both the membrane shell and Euler–Bernoulli beam theory require a prestress to be applied along the longitudinal edges of the channel. However, the level of prestress in the Alloz aqueduct is consistent with the beam theory, which seams the most appropriate approach.Whether or not a structure of this type acts as a shell depends upon the thickness of the wall. The thinner the wall, the more it act as a shell. The wall thickness of the Alloz aqueduct is sufficient for it to act mainly as a beam

Controllern i svensk praktik – En kvantitativ studie av en profession i förändring

Syfte: Att kartlägga samt analysera förändringar inom ekonomistyrningsområdet avseende controllerns roll, arbetsområde och styrmedel i svensk praktik Metod: En webb-baserad enkät skickades ut till 1000 controller i Sverige. Enkäten var utformad som en replikat från studien av Scapens et al (2002). Kvantitativa och statistiska metoder användes vid bearbetning av materialet Teoretiska perspektiv: Vi utgår ifrån att externa strukturella processer, s.k. förändringsdrivare, utvecklade av Lukka och Granlund (1998), som genom ekonomiska, normativa och tvingande tryck, samt efterliknelseprocesser, skapar homogeniserande praktiker i controllerns arbete. Slutsats: Några signifikanta skillnader mellan service- och industrisektorn i Sverige avseende controllerrollen, arbetsområdet och styrmedel kunde inte verifieras. Controllern i Sverige idag arbetar till stor del med de traditionella arbetsuppgifterna med ett visst inslag av strategiska arbetsuppgifter. I ett internationellt perspektiv kunde inga stora skillnader mellan Sverige och Storbritannien konstateras. Indikationer på en förhållandevis långsam förändring avseende controller rollen, arbetsområdet och styrmedel kunde noteras, till stor del oavsett nations- och sektortillhörighet samt organisationsegenskaper

Brazing development and interfacial metallurgy study of tungsten and copper joints with eutectic gold copper brazing alloy

Current proposals for the divertor component of a thermonuclear fusion reactor include tungsten and copper as potentially suitable materials. This paper presents the procedures developed for the successful brazing of tungsten to oxygen free high conductivity (OFHC) copper using a fusion appropriate gold based brazing alloy, Orobraze 890 (Au80Cu20). The objectives were to develop preparation techniques and brazing procedures in order to produce a repeatable, defect free butt joint for tungsten to copper. Multiple brazing methods were utilised and brazing parameters altered to achieve the best joint possible. Successful and unsuccessful brazed specimens were sectioned and analysed using optical and scanning electron microscopy, EDX analysis and ultrasonic evaluation. It has been determined that brazing with Au80Cu20 has the potential to be a suitable joining method for a tungsten to copper joint

Uppköp - En strategi för expansion

Hur arbetar svenska hotellkedjor med företagsförvärv som strategi för expansion? Uppsatsen undersöker denna fråga och bryter ner processen till motiven bakom, val av objekt, integrationsfasen och dess problem samt uppföljning

Solar cells for Uppsala’s sports facilities

The aim of this project was to evaluate the possibilities for solar panels on Uppsala’s sporting facilities and to suggest an expansion strategy. The project was based on a literature study concerning solar panels in general, an investigation of the most established solar panel manufacturers and an evaluation of their panels on a variety of factors. Additionally, an evaluation of Uppsala Sport- och Rekreationsfastigheters sporting facilities was made and combined with a field trip to gain more information about the buildings. The results from these investigations were used in the simulation programme PV*SOL, where five different facilities where simulated in two different scenarios with three different solar modules. The main difference between the two simulated scenarios was the amount of electricity sold to the grid. The simulation results show that facilities with large roof areas and high electricity consumption are most suited for installation. PV-modules of the manufacturer Sonnenstrom are recommended and a dimensioning of the installation according to scenario 2, where some electricity is sold to the grid, is proposed

Radiometric Correction of Multispectral UAS Images: Evaluating the Accuracy of the Parrot Sequoia Camera and Sunshine Sensor

Unmanned aerial systems (UAS) carrying commercially sold multispectral sensors equipped with a sunshine sensor, such as Parrot Sequoia, enable mapping of vegetation at high spatial resolution with a large degree of flexibility in planning data collection. It is, however, a challenge to perform radiometric correction of the images to create reflectance maps (orthomosaics with surface reflectance) and to compute vegetation indices with sufficient accuracy to enable comparisons between data collected at different times and locations. Studies have compared different radiometric correction methods applied to the Sequoia camera, but there is no consensus about a standard method that provides consistent results for all spectral bands and for different flight conditions. In this study, we perform experiments to assess the accuracy of the Parrot Sequoia camera and sunshine sensor to get an indication if the quality of the data collected is sufficient to create accurate reflectance maps. In addition, we study if there is an influence of the atmosphere on the images and suggest a workflow to collect and process images to create a reflectance map. The main findings are that the sensitivity of the camera is influenced by camera temperature and that the atmosphere influences the images. Hence, we suggest letting the camera warm up before image collection and capturing images of reflectance calibration panels at an elevation close to the maximum flying height to compensate for influence from the atmosphere. The results also show that there is a strong influence of the orientation of the sunshine sensor. This introduces noise and limits the use of the raw sunshine sensor data to compensate for differences in light conditions. To handle this noise, we fit smoothing functions to the sunshine sensor data before we perform irradiance normalization of the images. The developed workflow is evaluated against data from a handheld spectroradiometer, giving the highest correlation (R-2 = 0.99) for the normalized difference vegetation index (NDVI). For the individual wavelength bands, R-2 was 0.80-0.97 for the red-edge, near-infrared, and red bands