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Application area:

Health technology

We envision a future where wearable sensors and smart environments are commonplace, where we can gather information about our activities, sleep patterns, social interactions, medical care, and many other sources of health-related data. The monitoring, analysis and use of such information require new aware intelligent systems that can - based on the available data - assess a given situation, learn and adapt overtime, and provide relevant and timely information.

 Research

Our mission is to support healthy and active lifestyles, safe and independent aging,  as well as effective care services by developing intelligent systems that are aware of a person’s situation, health, and well-being using affordable, unobtrusive, and ubiquitous sensors. At CAISR, we develop technologies that support the acquisition and analysis of health-related data for monitoring and decision-support. We work with both mobile technologies and intelligent environments, and focus on movement analysis, behavior modeling and deviation detection techniques.

Activities

Two research projects funded by the KK-foundation as part of CAISR are currently ongoing: Situation Awareness for Ambient Assisted Living (SA3L), in collaboration with Neat Electronics; and Human Motion Classification and Characterization (HMC2), in collaboration with Tappa Service and Swedish Adrenaline.

The HMC2 project focuses on narrowing the gap between research and innovations in order to bringing research based products to the market. Efforts in this direction have led to a new data collection platform - named MAREA - that can simultaneously collect data from multiple wearable sensors such as accelerometers, store it on a smartphone, and stream it to the Cloud where high-level processing can be done in real-time. MAREA enables long-term gait or movement analysis in our normal daily environment. To this end, a novel algorithm was also developed and evaluated, which can accurately detect gait events using accelerometers placed anywhere on the body and thus moving beyond conventional gait lab analysis. A large gait data set was collected and is publicly available. The MAREA databaseexternal link is gathering a lot of attention and is being constantly downloaded by researchers from across the world.

Other activities within HMC2 include the development of novel methods to predict fatigue non-invasively in order to adjust the training regimen of athletes. This helps them improve the quality of their training for building muscle power, improving maximal oxygen consumption, raising the lactate threshold, and avoiding the risk of overuse injuries. In our study, an athlete runs on a treadmill starting with a low speed warm-up of five minutes. The speed of the treadmill is increased every 3 minutes depending on the perceived exertion of the runner. Blood samples are collected between the intervals to determine the lactate concentration. At the maximum level of perceived exertion, the treadmill speed is kept constant until the runner is totally exhausted. EMG signals are obtained using surface electrodes that are taped on the legs of the runner. Statistical measures and signal processing are used to evaluate the relationship between the EMG responses from the leg muscles and the blood lactate concentration and oxygen consumption.

In the SA3L project the problem of detecting potentially dangerous situations is explored by machine learning methods for learning normal activity patterns in the home. These patterns are thereafter used for interpretation and comparison of forthcoming activity patterns (which could indicate potential dangerous situations), this is an automatic approach which decrease the need for manually specified if-then-rules (e.g. manually program a system to alert caregivers if the resident has not return to bed within 10 minutes). During 2014 and 2015 a data collection was conducted using sensors installed in the homes of elderly residents in the municipality of Halmstad. This data was used for further development of the intelligent home simulator (IE Sim) together with researchers from Ulster University. Currently researchers in the SA3L project are using the acquired data to investigating methods for activity pattern representation, modelling normal behaviour and detecting deviations in living patterns of residents.

Additional activities supported by CAISR include the development of the Halmstad Intelligent Home, a fully functional research apartment equipped with various sensors, actuators and robots. The environment supports research and innovation related to ambient assisted living, human-robot interaction, understanding human activity and human behavior, as well as how to control or adapt an intelligent home in order to improve the health and wellbeing of the resident. The Halmstad Intelligent Home was inaugurated in September 2015.

Health Innovation Strategy

The Health Innovation Theme at Halmstad University aims to contribute to the development of sustainable solutions for societal challenges related to healthcare and wellbeing by coordinating several multidisciplinary initiatives. The Health Innovation Theme supports research, education, and collaborations with industry and the public sector.

One important way to contribute to the development of innovative health solutions in the region is to equip our students with the necessary tools to implement their own ideas. Several courses at undergraduate, graduate and research level are available at the University from Autumn 2017. These courses bring together students from different areas in order to foster creative thinking and cross-disciplinary health innovations.

In order to innovate in health we must also collaborate with society, e.g. local municipalities, and industry. The Centre for Health Technology Hallandexternal link (HCH) is an arena at Halmstad University where academia, society and industry – commonly referred to as the triple helix – come together.

CAISR activities are always attuned to Halmstad University’s health innovation strategy. We strive to collaborate with researchers from different fields, and our research directions are guided by the needs of society, and in an effort to co-produce with our industrial partners.

Related Projects

The CAISR health technology group is involved in other projects not directly funded by CAISR. These include:

  • The SIDUS AIR project – Activity and intention recognition in human interaction with autonomous systems – in collaboration with Skövde University, Örebro University, and Viktoria Swedish ICT.
  • Emotion-based automation of intelligent environments using brain-computer interface. This is a doctoral project funded by the Brazilian program Science Without Borders.
  • REMIND The use of computational techniques to Improve compliance to reminders within smart environments. This is a Marie Skłodowska-Curie Research and Innovation Staff Exchange project funded by the European Union. The project focuses on multidisciplinary knowledge transfer between academia and industry.
  • Active@work optimizing physical activity at work with personalized decision support among individuals with osteoarthritis. This project is a collaboration with Lund University funded by VR.

At CAISR, we develop technologies that support the acquisition and analysis of health-related data for monitoring and decision-support.

Test person on treadmil in the HMC2-project

Test person on treadmil in the HMC2-project

Updated 2018-02-27