Classifier Optimized for Resource-constrained Pervasive Systems and Energy-efficiency

Computational intelligence is often used in smart environment applications in order to determine a user’s context. Many computational intelligence algorithms are complex and resource-consuming which can be problematic for implementation devices such as FPGA:s, ASIC:s and low-level microcontrollers. These types of devices are, however, highly useful in pervasive and mobile computing due to their small size, energy-efficiency and ability to provide fast real-time responses. In this paper, we propose a classifier, CORPSE, specifically targeted for implementation in FPGA:s, ASIC:s or low-level microcontrollers. CORPSE has a small memory footprint, is computationally inexpensive, and is suitable for parallel processing. The classifier was evaluated on eight different datasets of various types. Our results show that CORPSE, despite its simplistic design, has comparable performance to some common machine learning algorithms. This makes the classifier a viable choice for use in pervasive systems that have limited resources, requires energy-efficiency, or have the need for fast real-time responses.publishedVersionnivå

Kaasasündinud N-glükosüülimise haigused Eestis

Väitekirja elektrooniline versioon ei sisalda publikatsiooneKaasasündinud glükosüülimise haigused (KGH) moodustavad kiirelt areneva ainevahetushaiguste grupi ning on põhjustatud valkude ja lipiididega seotud glükaanide häirunud sünteesist. Erinevad valkude N-glükosüülimise haigused on enim diagnoositavad KGH-d ja PMM2-CDG on kõige sagedasem N-glükosüülimise haigus. KGH sümptomid on mittespetsiifilised ja multisüsteemsed. Valikmeetod KGH skriinimiseks on seerumi transferriini isoelektriline fokuseerimine (IEF). Käesoleva uuringu eesmärk oli juurutada Eestis KGH diagnostikaks transferriini IEF ja hinnata kolme aasta jooksul N-glükosüülimise haiguste esinemist meie patsientide hulgas. Kuuel patsiendil 1230-st esines KGH skriiningul positiivne tulemus, mis leidis molekulaarse kinnituse. Esmalt näitasime, et kõige sagedasem KGH Eestis on PMM2-CDG, mida diagnoositi neljal patsiendil kahest perekonnast. Ühe pere lastel väljendub haigus kerge neuroloogilise vormina, kuid normaalse kognitiivse arenguga, mida PMM2-CDG patsientide hulgas esineb harva. Eesti PMM2-CDG patsientidel oli kõige sagedasem variant PMM2 geenis p.Val131Met. Teiseks, esitasime tulemused PMM2-CDG eeldatava sageduse kohta, kasutades Tartu Ülikooli Eesti Geenivaramu andmeid. Leidsime viis erinevat PMM2 heterosügootset mutatsiooni. Kõige sagedasem geenivariant on p.Arg141His kandlussagedusega 1/224. p.Val131Met kandlussagedus on 1/449. Eeldatav PMM2-CDG sagedus Eestis on 1/77,000. Kolmandaks, kirjeldasime patsienti KGH alatüübiga SLC35A2-CDG ning võrdlesime tema fenotüüpi ja genotüüpi 14 rahvusvahelise patsiendi kliiniliste andmetega. Patsientidele on iseloomulik mittespetsiifiline neuroloogiline haigus üldise arengu hilistumise, lihashüpotoonia, krampide ning epileptilise entsefalopaatiaga, düsmorfsed tunnused ja lühike kasv. Lisaks võib transferriini IEF olla vale-negatiivne. Neljandaks, kirjeldasime multisüsteemsete kliiniliste sümptomitega ning uue, seni kirjeldamata KGH alatüübiga patsienti, kellel on KGH alatüübi põhjuseks tõenäoliselt haiguspõhjuslik homosügootne muutus STX5 geenis. Käesolev uuring näitas, et Eesti patsientide puhul on transferriini IEF on tulemuslik meetod KGH diagnostikas. Skriiningu rakendamine võimaldas lisada uusi kliinilisi ja epidemioloogilisi andmeid erinevate teadaolevate ning uue KGH alatüübi kohta.Congenital disorders of glycosylation (CDG) are an expanding group of inherited metabolic diseases caused by impaired synthesis and attachment of glycans on proteins and lipids. Disorders affecting the N-glycosylation pathway form the most common CDG subgroup, and the most common N-glycosylation disorder is PMM2-CDG. The symptoms of different CDG are often non-specific and multisystem. Serum transferrin isoelectric focusing (Tf IEF) is a routine method to screen CDG. The aim of this study was to implement Tf IEF in Estonian clinical practice and to study the presence of N-glycosylation defects among Estonian patients in a three-year screening period. Altogether, positive CDG screening with subsequent molecular confirmation was detected in six patients among 1230 subjects screened. First, the most frequent CDG in Estonia is PMM2-CDG as we diagnosed this disorder in four patients from two families. In one family, the siblings show a mild neurological phenotype with normal-borderline cognitive development, which has previously been seldom described. Among PMM2-CDG patients, the most common variant in PMM2 gene is p.Val131Met. Second, we reported the expected frequency of PMM2-CDG based on the Estonian population data. In this cohort, we identified five different heterozygous variants in PMM2 gene. The most frequent variant is p.Arg141His with carrier frequency 1/224. The carrier frequency for p.Val131Met based on the Estonian population data is 1/449. The expected frequency of PMM2-CDG is 1/77,000. Third, we described a patient with SLC35A2-CDG and compared his phenotype-genotype with 14 international SLC35A2-CDG patients. This type of CDG presents as a non-specific neurological syndrome with global developmental delay, hypotonia, seizures and epileptic encephalopathy, together with dysmorphic features and short stature. In addition, Tf IEF can show a normal profile. Fourth, we presented a patient with multisystem clinical CDG features and a novel type II CDG likely caused by homozygous variant in STX5. In conclusion, Tf IEF proved to be an effective method to detect CDG among Estonian patients. Our results led to many findings, which have helped to add new clinical and epidemiological data about different known types of CDG, but also to expand the group of CDG by the discovery of a new type of CDG

Diskret Aktivitetsigenkänning i Resursbegränsade Miljöer

This thesis discusses activity recognition from a perspective of unobtrusiveness, where devices are worn or placed in the environment without being stigmatising or in the way. The research focuses on performing unobtrusive activity recognition when computational and sensing resources are scarce. This includes investigating unobtrusive ways to gather data, as well as adapting data modelling and classification to small, resource-constrained, devices. The work presents different aspects of data collection and data modelling when only using unobtrusive sensing. This is achieved by considering how different sensor placements affects prediction performance and how activity models can be created when using a single sensor, or when using a number of simple binary sensors, to perform movement analysis, recognise everyday activities, and perform stress detection. The work also investigates how classification can be performed on resource-constrained devices, resulting in a novel computation-efficient classifier and an efficient hand-made classification model. The work finally sets unobtrusive activity recognition into real-life contexts where it can be used for interventions to reduce stress, sedentary behaviour and symptoms of dementia. The results indicate that activities can be recognised unobtrusively and that classification can be performed even on resource-constrained devices. This allows for monitoring a user’s activities over extensive periods, which could be used for creating highly personal digital interventions and in-time advice that help users make positive behaviour changes. Such digital health interventions based on unobtrusive activity recognition for resource-constrained environments are important for addressing societal challenges of today, such as sedentary behaviour, stress, obesity, and chronic diseases. The final conclusion is that unobtrusive activity recognition is a cornerstone necessary for bringing many digital health interventions into a wider use.Denna avhandling diskuterar aktivitetsigenkänning ur ett diskret perspektiv, där enheter bärs eller placeras i miljön utan att vara stigmatiserande eller i vägen. Forskningen fokuserar på att utföra diskret aktivitetsigenkänning när beräknings- och sensor-resurser är knappa. Detta inkluderar att undersöka diskreta sätt att samla in data, samt att anpassa datamodellering och klassificering till små, resursbegränsade enheter. Arbetet presenterar olika aspekter av datainsamling och datamodellering när man bara använder diskreta sensorer. Detta uppnås genom att överväga hur olika sensorplaceringar påverkar prediktionsprestanda och hur aktivitetsmodeller kan skapas vid användning av en enda sensor eller vid användning av ett antal enkla binära sensorer, för att utföra rörelsesanalys, känna igen vardagliga aktiviteter och utföra stressdetektering. Arbetet undersöker också hur klassificering kan utföras på resursbegränsade enheter, vilket resulterar i en ny beräkningseffektiv klassificeringsalgoritm och en effektiv handgjord klassificeringsmodell. Slutligen sätter arbetet in diskret aktivitetsigenkänning i verkliga sammanhang där det kan användas för interventioner för att minska stress, stillasittande  beteende och symptom på demens. Resultaten visar att diskret aktivitetsigenkänning är möjligt och att klassificeringen kan utföras även på resursbegränsade enheter. Detta möjliggör övervakning av användarens aktiviteter under längre  perioder, vilket kan användas för att skapa personliga digitala interventioner och tidsanpassad rådgivning som hjälper användarna att göra positiva beteendeförändringar. Sådana digitala hälsointerventioner baserade på diskret aktivitetsigenkänning i resursbegränsade miljöer är viktiga för att ta itu med dagens samhällsutmaningar, såsom stillasittande beteende, stress, fetma och kroniska sjukdomar. En slutsats av arbetet är att diskret aktivitetsigenkänning är en hörnsten som är nödvändig för att få en större användning av digitala hälsointerventioner

Spelprototyp från grunden med Unreal Engine 4

I detta examensarbete går jag genom en grundläggande del för skapandet av en spelprototyp med spelmotorn Unreal Engine 4. Arbetet behandlar spelmotorns användargränssnitt och grundläggande terminologi som användaren kan stöta på. Spelmotorns utvecklare, Epic Games presenteras också samt affärsstrategin som företaget valt för att locka användare och företag att välja deras spelmotor. Syftet med examensarbetet är att visa hur spelmotorn fungerar samt hur man utvecklar spel då jag för tillfället, enligt egen åsikt endast besitter grundläggande programmeringskunskaper, och jag vill lära mig mera om spelutveckling. Prototypen jag skapar ska också gå att utveckla i framtiden. Arbetet består av tre olika delar -en teoretisk, en praktisk samt en avslutande del. I den teoretiska delen går jag igenom spelmotorn och dess utvecklare samt uppbyggandet av själva spelprototypen. I den praktiska delen utformas och färdigställs denna och i den avslutande delen bedömmer jag mitt examensarbete

Activity recognition in resource-constrained pervasive systems

There is an increasing need for personalised and context-aware services in our everyday lives and we rely on mobile and wearable devices to provide such services. Data collected from these devices includes important information about users’ movements, locations, physiological status, and environment. This data can be analysed in order to recognise users’ activities and thus provide contextual information for services. Such activity recognition is an important tool for personalising and adapting assistive services and thereby increasing the usefulness of them.This licentiate thesis focuses on three important aspects for activity recognition usingwearable, resource constrained, devices in pervasive services. Firstly, it is investigated how to perform activity recognition unobtrusively by using a single tri-axial accelerometer. This involves finding the best combination of sensor placement and machine learning algorithm for the activities to be recognized. The best overall placement was found to be on the wrist using the random forest algorithm for detecting Strong-Light, Free-Bound and Sudden-Sustained movement activities belonging to the Laban Effort Framework.Secondly, this thesis proposes a novel machine learning algorithm suitable for resource-constrained devices commonly found in wearable and pervasive systems. The proposed algorithm is computationally inexpensive, parallelizable, has a small memory footprint, and is suitable for implementation in hardware. Due to this, it can reduce battery usage, increase responsiveness, and also make it possible to distribute the machine learning task, which enables balancing computational costs against data traffic costs. The proposed algorithm is shown to have a comparable accuracy to that of more advanced machine learning algorithms mainly for datasets with two classes.Thirdly, activity recognition is applied in a personalised and pervasive service for im-proving health and wellbeing. Two monitoring prototypes and one coaching prototype were proposed for achieving positive behaviour change. The three prototypes were evaluated in a user workshop with 12 users aging between 20 and 60. Participants of the workshop believed that the proposed health and wellbeing app is something people are likely to use on a permanent basis.By applying results from this thesis, systems can be made more energy efficient andless obtrusive while still maintaining a high activity recognition accuracy. It also shows that pervasive and wearable systems using activity recognition have the potential of relieving some problems in health and wellbeing that society face today.Godkänd; 2015; 20151021 (nikkar); Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Niklas Karvonen Ämne: Distribuerade datorsystem/Pervasive Mobile Computing Uppsats: Activity Recognition in Resource-Constrained Pervasive Systems Examinator: Professor Kåre Synnes, Institutionen för system- och rymdteknik Avdelning: Datavetenskap, Luleå tekniska universitet Diskutant: Professor Chris Nugent, University of Ulster, Northern Ireland Tid: Tisdag 15 december 2015 kl 15.00 Plats: D770, Luleå tekniska universite

Time-efficient algorithms for laser guided autonomous driving

Robust navigation systems are of great importance in the field of mobile robotics. In order for a mobile robot to be useful, it must know its surroundings at all times to be able to perform its task. The complexity of creating navigation is highly dependent on the environment the robot is to work in and whether or not geographical data of this environment is known beforehand. The key focus in this thesis was to find fast and time efficient robust algorithms and models for driving autonomously along an unknown road using minimal equipment. It also strives to find a trade off with the least amount of parameters necessary for doing so. A range finding laser scanner (lidar from SICK) was used as a single sensor for interpreting the surroundings of the robot. The scanning laser measures distances in a single plane in front of the car and these data are then interpreted by a road finding algorithm. The goal is to distinguish the road from the terrain. When the system has found what is interpreted as the road it will pass that information to the driver model which is responsible for controlling the car. After the development of a steering model and road finding algorithms, the car successfully managed to navigate autonomously over 1 kilometre along a curved and hilly forest road. While remaining in constant low speed (<20 km/h) the system was robust. higher speeds and acceleration showed weaknesses in the system. including a rate gyro could increase the stability and speed.Validerat; 20101217 (root

A Domain Knowledge-Based Solution for Human Activity Recognition: The UJA Dataset Analysis

Detecting activities of daily living (ADL) allows for rich inference about user behavior, which can be of use in the care of for example, elderly people, chronic diseases, and psychological conditions. This paper proposes a domain knowledge-based solution for detecting 24 different ADLs in the UJA dataset. The solution is inspired by a Finite State Machine and performs activity recognition unobtrusively using only binary sensors. Each day in the dataset is segmented into: morning, day, evening in order to facilitate the inference from the sensors. The model performs the ADL recognition in two steps. The first step is to detect the sequence of activities in a given event stream of binary sensors, and the second step is to assign a starting and ending times for each of detected activities. Our proposed model achieved an accuracy of 81.3% using only a very small amount of operations, making it an interesting approach for resource-constrained devices that are common in smart environments. It should be noted, however, that the model can end up in faulty states which could cause a series of mis-classifications before the model is returned to the true state

NxBias. : Design av normmedveten tjänst för inkluderande kommunikation med hjälp av AI/Maskininlärning

NoBia

Analyzing Body Movements within the Laban Effort Framework Using a Single Accelerometer

This article presents a study on analyzing body movements by using a single accelerometer sensor. The investigated categories of body movements belong to the Laban Effort Framework: Strong—Light, Free—Bound and Sudden—Sustained. All body movements were represented by a set of activities used for data collection. The calculated accuracy of detecting the body movements was based on collecting data from a single wireless tri-axial accelerometer sensor. Ten healthy subjects collected data from three body locations (chest, wrist and thigh) simultaneously in order to analyze the locations comparatively. The data was then processed and analyzed using Machine Learning techniques. The wrist placement was found to be the best single location to record data for detecting Strong—Light body movements using the Random Forest classifier. The wrist placement was also the best location for classifying Bound—Free body movements using the SVM classifier. However, the data collected from the chest placement yielded the best results for detecting Sudden—Sustained body movements using the Random Forest classifier. The study shows that the choice of the accelerometer placement should depend on the targeted type of movement. In addition, the choice of the classifier when processing data should also depend on the chosen location and the target movement