In this thesis the automatic multimodal detection of social and task cohesion in meetings is studied. The presence of social and task cohesion has positive benefits on employee well-being, creativity and productiveness, and can therefore be used to assess meeting quality. Conversational partners imitate each other's body language and speech characteristics to have smoother interactions, to increase liking, and it can cause a coordination of expectations. We hypothesize that social and task cohesion are therefore positively related to the imitation of both body language and speech characteristics. As the group-level alignment of non-verbal speech behaviour has been previously linked to social and task cohesion in meetings, this thesis investigates the relationship between cohesion in meetings and both motion and posture mimicry from accelerometer and video data. Motion mimicry is described using accelerometer features previously used for the detection of friendly and romantic attraction in pairs. We propose a method to convert these features to group-level descriptors of mimicry. The same quantifications of mimicry are also applied to video-based motion quantifiers that have been previously used to detect team cohesion. Appearance is described using HOG descriptors from densely-sampled feature points that are tracked over time. To our knowledge this is the first-time appearance similarity is used to estimate cohesion. We also test if a multimodal mimicry model performs better than a unimodal mimicry model to investigate if the different forms of mimicry contain complementary information. Our group-level movement mimicry features detect social cohesion with average an area under the ROC-curve of 0.64 for social cohesion, and the accelerometer-based movement mimicry features specifically detect task cohesion with an average AUC of 0.63. The multimodal combination of the different features does improve over the unimodal models with an area under the ROC-curve of 0.68 for social cohesion and 0.65 for task cohesion. This shows both that movement mimicry is an indicator of verbal expressions of cohesion, and that measuring mimicry in different modalities can better model cohesion than a unimodal model. Further experiments are recommended for general appearance feature mimicry, specifically in the method used to measure mimicry, to confirm or deny if these are related to cohesion.

As of this moment there is a lack of data about rainfall in cities. To collect such data, IBM has started the Sense Umbrella Connection and Desensitisation project. For this project an umbrella was equipped with a piezoelectric sensor and a Bluetooth device to record the rain that falls on the surface of this umbrella. One downside of this umbrella is that, besides rain, it will also record other sounds which include recognisable human speech. Because IBM values privacy, one of the tasks was to make sure that no recording containing recognisable human speech would be uploaded to a server.

This bachelor project focuses on creating a mobile application to connect to the umbrella via Bluetooth and save the audio recordings of rain together with GPS data. After saving this data it would originally be analysed for presence of rain and it was also required to remove all human speech before it was sent from the phone to the server. Over the course of the project it became clear that because of a scarce set of sample data and the limited availability of audio processing libraries on Android, it would be difficult to process audio on Android devices. This is why the decision was made to upload all data, and the processing and analysing was moved to an external device supporting Java.
The raw data will now be uploaded to a server which will make a database entry which includes GPS data, and saves the audio file. In the end an app has been developed that is able to gather data from the umbrella and send it to an external server. Additionally an implementation has been made to analyse the audio data gathered to classify which parts may contain rain.

This project focused on developing an Android app and not on other operating systems due to time constraints.