Matti Linnavuo

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We present a new fall-detection method using a floor sensor based on near-field imaging. The test floor had a resolution of 9×16. The shape, size, and magnitude of the patterns are used for classification. A test including 650 events and ten people yielded a sensitivity of 91% and a specificity of 91%.
The stroke is one of the most prevalent health and well being risks world-wide. In stroke patient rehabilitation, retrieving the ability to walk is an important goal. In addition to that, the gait should be sufficient for independent mobility in the community, thus eliminating the risk of more severe immobilization, falls and health deterioration. In this(More)
Markets are waiting for new, innovative game control techniques from the game industry. At the same time people suffering from stroke or other neurologic problems could benefit from new rehabilitation systems. Education and exhibition can make use of new learning environments. This poster presents a proactive space concept to meet these needs. Proactive(More)
In healthcare environment, different kinds of automatic solutions have been created to monitor and track patients, for example near-field imaging and low-frequency RFID. The problem has been how to use the context-based data these systems produce and how to show the related information to the nursing staff. This paper shows how hospital data can be(More)
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