In addition to this, a key question is how humans can meaningfully cooperate with machines to ensure that tasks are carried out better overall. Both humans and machines can contribute to fi nd a solution. Good illustrations of this can be found in medicine. When carrying out diffi cult operations, for example on the human brain, virtual assistance systems can be used to provide the surgeon with important additional information – where exactly is the tumor; where are critical constrictions that require particular care?
In the intensive care, machines handle monitoring of patients’ vital signs, with medical personnel reacting accordingly. Empirical studies have, however, shown that numerous visual and acoustic alarm signals can be problematic since it is rarely necessary for personnel to actually intervene. In such cases the flow of information in not helpful but rather can be dangerous because it causes over-familiarization. A mere parallel existence of humans and machines can thus lead to a negative outcome. Human-machine cooperation is a better option, with the machines condensing information to generate a useful overall picture from many single parameters. Intuitive displays could then enable nursing personnel to quickly recognize whether patients are doing well or not.
Interactive systems are already being used in many diff erent environments today. In transportation modes such as cars, planes, or ships; in devices such as smartphones, tablets, or wearables; and in control rooms and voice-controlled devices. Conventional input and output modalities such as keyboards or monitors are increasingly being replaced by so-called »multimodal user interfaces« that incorporate multiple sensory channels. Intelligent assistants facilitate contextsensitive use and interaction with real objects in our daily environment. Ambient lighting can, for example, today be controlled using a smart device; acoustic signal, or voice command.
Depending on the application context, human-machine interaction and cooperation is subject to specifi c requirements regarding fitness for purpose; user-friendliness; acceptance; robustness; and security. Correspondingly, the Human Machine Cooperation competence cluster uses a variety of analysis, development, and evaluation methods to design and validate interactive or cooperative systems. Application focuses are, for example, assistive systems in vehicles, ships, healthcare, medicine, and personal health.
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