Haptic feedback based on the sense of touch and movement is a promising area of human-computer interaction in the car context. Most user studies on haptic feedback in the car have been controlled experiments of specific types of haptic stimuli. For the study presented in this paper, twelve participants tried novel haptic feedback prototypes and evaluated communication scenarios in the physical car context. Our aim was to understand user experiences and usage potential of haptic feedback in the car. The qualitative results show that haptic feedback may offer support for safety and social communication, but can be hard to interpret. We propose design considerations for in-car haptics such as simplicity, subtleness and directionality.

The spreading of mobile devices to all areas of everyday life impacts many contexts of use, including cars. Even though driving itself has remained relatively unchanged, there are now a wide variety of new in-car tasks, which people perform with both integrated infotainment systems and their mobile devices. To gain insights into this new task context and how it could be improved, we conducted a qualitative, contextual study in which we observed real-life car journeys with eight participants. The focus was on user interaction with touchscreen mobile devices, due to their wide range of functions and services. The findings show that the car is an extension of other contexts and it contains a rich set of infotainment tasks, including use of social media. Drivers emphasized gesture interaction and the use of non-visual modalities, for replacing visual information and notifying of changes in the driving context. Based on the findings, we present design implications for future in-car infotainment systems.

Home environment is an exciting application domain for multimodal mobile interfaces. Instead of multiple remote controls, personal mobile devices could be used to operate home entertainment systems. This paper reports a subjective evaluation of multimodal inputs and outputs for controlling a home media center using a mobile phone. A within-subject evaluation with 26 participants revealed significant differences on user expectations on and experiences with different modalities. Speech input was received extremely well, even surpassing expectations in some cases, while gestures and haptic feedback were almost failing to meet the lowest expectations. The results can be applied for designing similar multimodal applications in home environments.

We present a systematic study on gender classification with automatically detected and aligned faces. We experimented with 120 combinations of automatic face detection, face alignment and gender classification. One of the findings was that the automatic face alignment methods did not increase the gender classification rates. However, manual alignment increased classification rates a little, which suggests that automatic alignment would be useful when the alignment methods are further improved. We also found that the gender classification methods performed almost equally well with different input image sizes. In any case, the best classification rate was achieved with a support vector machine. A neural network and Adaboost achieved almost as good classification rates as the support vector machine and could be used in applications where classification speed is considered more important than the best possible classification accuracy.

There has been much research on computer vision in last three decades. Computer vision methods have been developed for different situations. One example is a detection of human face. For computers face detection is hard. Faces look different from different viewing directions. Facial expressions affect to the look of the face. Each individual person has a unique face. The lightning conditions can vary and so on.