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AIMS +	The state of the art in autonomous robotics has advanced sufficiently that open implementations of many core technologies are now readily available. Consequently, there is growing research on the design and development of innovative solutions that leverage insights from several specialist domains. The AIMS project lies in this category. Its goal is to develop a system that seamlessly combines inventory management with autonomous forklift trucks in intelligent warehouses. Information compatible with human operators, management systems, as well as mobile robots is of particular importance here. A rich and "live" map combining metric and semantic information is a crucial ingredient for effective management of logistics and inventory, especially for autonomous fleets working in the same space as humans and human-operated devices.
ARISE +	FFI Vinnova project
AccelGait +	The ability to judge "good" gait from "bad" gait is valuable when assessing the outcome of an orthopedic surgery, the rehabilitation of a stroke patient, or the ability of an older person to live independently, among others. Mobility assessment is commonly performed based on observations of the patient performing a few tasks. The patient performance is scored by an experienced observer with the help of questionnaires. A fundamental problem with this assessment is that it is very subjective to the observer. Several researchers have investigated the use of 3D motion capture (mocap) systems to extract objective measures of gait parameters, and derive a quality of gait index (QoGI). Unfortunately, these systems are complicated to use and very expensive. As a result, they are only used for research purposes and not used in most clinical settings. Accelerometers are small and cheap sensors which can be used to measure a number of gait parameters anytime, anywhere. The goal of this project is to collaborate with medical personnel in order to devise a cheap and easy to use measuring system using accelerometers; and to create a QoGI which will facilitate the assessment and treatment of mobility challenged patients at the clinic and at home. The project is funded by Stiftelsen Promobilia.
Active at Work +	The aim of Active@Work is to explore if mobile technology including a personalized decision support system, can have any effect on physical activity level, health, work ability, quality of life, work productivity or sick leave among individuals with osteoarthritis (OA). We also aim to study if there is any difference in effect between using mobile technology and activity monitoring alone or when continuous feedback concerning physical activity is added. Participants will be allocated through a patient education program for OA and randomized into either (A) Patient education program and physical activity monitoring, (B) Intervention A plus continuous feedback concerning physical activity or (C) Patient education program (control). The intervention will be performed during three months, with measurements at baseline, and follow-ups after 3, 6 and 12 months. Patient-reported outcomes, outcomes from technical devices and register data will be evaluated. Lund University is responsible for the randomized controlled trial and Halmstad University is responsible for developing the mobile technology and activity support in the project. Inclusion of patients in the project is expected to start in spring 2017 and to continue until 2019. Analyses and manuscript writing will be performed during 2019. This project will show how technological solutions can be used to develop evidence-based treatment models to improve health and work ability which can be effective as first line treatment of OA.
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BIDAF +	BIDAF is a five-year research project financed by the KK-stiftelsen. The project is carried out by researchers at Halmstad University, SICS Swedish ICT AB, and University of Skövde, Sweden. The overall aim of the BIDAF project is to significantly further the research within massive data analysis, by means of machine learning, in response to the increasing demand of retrieving value from data in all of society. This will be done by creating a strong distributed research environment for big data analytics.
BIO-DISTANCE +	Despite the practical importance and the advantages of biometric solutions to the task of verifying personal identity, their adoption has proved to be slower that predicted. Biometrics ''on the move'' is a hottest research topic aimed to acquire biometric data ''at a distance'' as a person walks by detection equipment. This drastically reduces the need of user’s cooperation, achieving low intrusiveness and thus, high acceptance and transparency. With these ideas in mind, the main objective of this project is to investigate a number of activities aimed to make biometric technologies applicable to data acquired at a distance and/or on the move. We propose the use of face and iris as the reference modalities, being the two traits that are attracting more efforts thanks to the possibility of their simultaneous acquisition. This project is an integrated approach that covers the whole structure of a biometric system, including basic research and algorithm development for the different stages of the system, as well as practical results through case studies implementation and evaluation. To accomplish the overall objectives, a number of challenges need to be overcome, which constitute the specific research objectives.
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CAISR Intelligent Environment +	The CAISR Intelligent Environment is platform for demonstrating and showcasing CAISR research; implementing and testing new technologies in a realistic environment; supporting data collection and validation of research hypotheses; as well as providing a functional infrastructure for student projects.
Cargo ANTs +	The EC-funded Cargo-ANTs project involves five partners from three countries, to create smart Automated Guided Vehicles and Highly Automated Trucks than can cooperate in shared workspaces for efficient and safe freight transportation in main ports and freight terminals.
Centre for Health Technology Halland - HCH +	The Centre for Health Technology in Halland is a meeting place for businesses, consumers, the municipalities of Halland County, the County Council of Halland and Halmstad University and provides a joint platform for the pursuit of development in the area of health technology. Through co-operative projects and work in innovative environments leading to new products and services, the project seeks to contribute to increasing the pace of innovation and to sharpening the competitive edge of the companies involved.
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EIS-IGS Smart Grids +	Current advancements in embedded systems, communications and sensing technologies, have made it possible to acquire and process large amounts of real-time data from distributed sensor networks. These networks can therefore be used to monitor and manage large-spread utility services such as the power grid, commonly known as the smart grid. However, this concept can also be applied to create other smart utilities such as district heating, and public transportation. The driving force behind smart utilities are aware intelligent systems that can process large amounts of data, extract relevant information, plan according to a given situation, and then act in a timely manner in order to optimize the efficiency and sustainability of these services.
EVE +	The EVE project aims to develop general lifetime models for all vital components in the electrical driveline of heavy duty vehicles. Hybrid and fully electric buses must take advantage of predictive maintenance services based on Machine Learning (ML) in order to remain competitive in the market despite their increased cost. Those new technical solutions are enabled by the possibility of collecting more precise and extensive data. Motivating example is the main battery, a component that is responsible for large fraction of the vehicle cost, and is expected to survive over 10 years. In the project we will create a data-driven model for battery health degradation, based on Transfer Learning (TL) paradigm utilising recent advancements in the field of deep learning, namely Generative Adversarial Networks (GANs). Historical data on failures of electric driveline components is not available due to its technological novelty, thus the right support for drivers, fleet operators and OEMs requires studies and development of new ML techniques. Our goal is to enable predictive and prescriptive maintenance solutions that are capable of adapting to rapid changes in technology and continuous arrival of new observational data. This must be bootstrapped by leveraging available expert knowledge in the form of adaptive models for the evolving technology through AI solutions.
Emotion-based automation of intelligent environments using brain-computer interface +	Through a combination of biosignals and environmental sensors, we aim to help the user relax and sleep, for exemple, depending on the time of the day and if he/she is laying on the bed. If the user is in this "wanting to sleep" context and his/hers biosignals detect anxiety and alertness, the environment would adapt maybe turning off the lights and using sounds from the brain drive theories in order to help him/her achieve a relaxed and sleepy mental state. With this project studies: - Ways to detect different mental states through biosignals, - Ways to use a combinations of environmental and bio-sensors to speculate what the user wishes to do at the environment, - How the environment can influence the individual's mental state, - New ways of passive interaction between the user and the environment, - How to put the "human in the loop" at controlling smart environments,
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FFI NG-TEST +	The project is funded by Vinnova under the Strategic Vehicle Research Partnership (FFI). It will run for three years and involves Volvo AB, SP Technical Research Institute, Volvo Car Corporation, Autoliv, VTI, Chalmers, and Halmstad. Our contributions will be in the domain of requirement modeling as well as validation and verification of the entire testing framework.
FuelFEET +	Analysis of factors influencing fuel consumption is a very important task both for automotive industry. There is a lot of knowledge already available concerning this topic, but it is poorly organized and often more anecdotal than rigorously verified. Nowadays, however, enough rich datasets from actual vehicle usage are available and a data-mining based approach can be used to not only validate earlier hypotheses, but also to potentially discover unexpected influencing factors.
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GoDIS +	The future of health promotion in the form of e-health applications and interactive tools developed at breakneck speed, but despite this and a plethora of information about the health benefits of exercise, it has proved difficult to promote sustainable exercise behavior and motivation to exercise. The goal of this project is to develop and test an interactive tool based on the current motivational and behavioral research combined with modern expertise from the Information Technology, Innovation Sciences and based on ease of use and the needs of the e-health industry. In addition to potentially health-promoting effects at the individual and societal level through the promotion of sustainable exercise behaviors the project is expected to generate innovative digital solutions for e- health industry through cross-disciplinary application of behavioral theory, information technology and business model development.
H
HEALTH +	FFI Vinnova project
HMC2 +	The objective is to design wearable instruments/devices that can characterize and classify human motion. The devices shall be small and “non-intrusive”, similar to a step counter. We are looking at two sensor modalities: inertial sensors (e.g. accelerometers and gyros) and electrical sensors for detecting, e.g., electromyography (EMG) signals.
I
IMedA +	The iMedA project will improve medication adherence for hypertensive patients through an AI agent that supports doctor and patient in collaboratively understanding key individual adherence risk factors and designing an appropriate intervention plan. iMedA will deliver the selected intervention through a mobile App and follow-up on its effectiveness improving the system over time. The combination of person-centered care and self-management interventions will lead to significantly improved health outcomes and reduced healthcare costs. iMedA empowers hypertensive patients to take responsibility for their health through self-management, and provides doctors with information they need for person-centered care. To identify risks and intervention strategies, iMedA uses health records as well as self-reported input. The AI agent understands how both medical and personal factors interact with respect to medication adherence, and display this information on a "dashboard" that guides patient-doctor conversation. The AI monitors the effectiveness of interventions in order to improve over time. The iMedA agent will be built by combining three important AI techniques. First is to create a meaningful and comprehensive representation of each patient based on information fusion and representation learning. Second is to use peer group analysis and interpretable supervised machine learning methods to predict non-adherence for concrete patients. Finally, intervention strategies that are the most appropriate for a particular patient we will selected by combining data-driven and knowledge-driven approaches.
In4Uptime +	The goal of the project is to keep commercial vehicles in good operational condition, both from a financial and safety point of view. Haulers and transporters require OEMs to provide vehicles having close to 100% uptime. That means no stops, unless planned, as well as guarantees on optimal performance of all components ensuring acceptable levels of CO2 emission and fuel consumption. Utilizing data that comes from sources with different origin, such as on-board, off-board, structured, unstructured, private and public, and by combining information and finding common patterns will allow us to better adapt the service contracts and maintenance plans to the needs of individual customers and individual vehicles. Volvo Technology will coordinate the project and is overall responsible. The other partners of the project are: Volvo Information Technology, Högskolan i Halmstad, Svenska Innovationsinstitutet and Recorded Future.
InnoMerge +	The InnoMerge project addresses the challenges related to the major growth opportunities expected to be found in emerging markets such as India and East Asia. The main objective of the project as a whole is to build knowledge on how advanced technologies and business models can be transferred to, and from, an emerging market context. This should lead to speed up in the adoption of more sustainable truck solutions, including environment and traffic safety.
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Malta +	The purpose of the MALTA project is to develop a demonstrator platform for a fully autonomous fork-lift truck that handles heavy products in an industrial setting. The truck should be able to work safely together with other autonomous trucks and with manually driven trucks. The autonomous trucks should be able to move around safely in an environment with other trucks and with people. They should be able to pick up (load) products, unload them, store them in containers or train wagons and possibly even stack them. The trucks should be able to do this with a speed that is comparable to the speed of trucks driven by humans. The project is a collaboration between the AASS research center at Örebro University, the Intelligent Systems Lab at Halmstad University, Danaher Motion, Stora Enso Logistics and Linde Material Handling.
MoveApp +	The MoveApp project aims to develop new tools to support self-management of chronic conditions which are characterized by motor symptoms and loss of motor coordination. The initial application targets Parkinson's Disease patients. The project will be undertaken in two phases, one related to the development of a self-monitoring and visualization tool for patients; and another related to the development of models to support the decision making process for patients and doctors.
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NHCT +	xxx
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OCULAR +	This is a four-years project financed the Swedish Research Council. The project is concerned with ocular biometrics in unconstrained sensing environments. Attention will be paid to the periocular modality (the part of the face surrounding the eye), which has shown a surprisingly high discrimination ability, and is the facial-ocular modality requiring the least constrained acquisition. One goal is to contribute with methods for efficient ocular detection and segmentation. This is still a challenge, with most works relying on manual image annotation, or on detecting the full face, which may not be reliable for example under occlusion. We will continue initiated work with symmetry filters, and will explore deep learning algorithms too, which are giving promising results in many computer vision tasks. Low resolution is another limitation. Thus, another goal will be super-resolution (SR) reconstruction of ocular images. With few works focused on iris, and none on periocular, adaptation of the many available SR methods to the particularities of ocular images is a promising avenue yet to be explored. Ubiquitous biometrics has emerged as critical not only in light of current security threats (e.g. identifying terrorists in surveillance videos), but also due to the proliferation of consumer electronics (e.g. smartphones) in need of continuous personal authentication for a wide variety of applications. By our contributions, we expect to be able to handle a wide range of variations in biometric imaging from these scenarios.
P
PRIME +	The goal of this project is predicting failures in a fleet of sterilizers deployed in hospitals all over the world. The characteristics of this problem are general to the field of predictive maintenance for different application fields. Companies are interested in predictive maintenance to reduce the down time of their machines. In general the list of critical components, whose unexpected breakdowns would result in stopping the machine, is long. Therefore, the scope of a predictive maintenance system should be predicting failures in a big number of different components. For several years, systems such as cars, sterilizers or industrial equipment have been equipped with a significant amount of sensors. Which signals to record is in general not decided based on the predictive maintenance needs, but on the requirements of security or controllers among other reasons. The sensors mounted usually don’t describe the particular behavior of the components of interest, but measure physical quantities that can be influenced by the different behavior of several components. Predicting what component will fail when requires historic data about the operation of the machines, but also needs to be linked to the occurrence of failures, so that we can label the recorded data. In general, companies have access and store data coming from their machines, but don’t necessary have access to the whole history of repairs. The owner of the machines can decide whether to perform maintenance and repairs with the official service or any other unofficial service. The main research goal of this project is to build a framework that allows predicting all type of failures that can happen in a machine.

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