We have completed the collection of one of the richest accurately annotated mobile dataset of modes of transportation and locomotion. To do this, we developed a highly reliable Android application called DataLogger capable of recording multisensor data from multiple synchronized smartphones simultaneously. The application allows real-time data annotation. We explain how we designed the app to achieve high reliability and ease of use. We also present an evaluation of the application in a big-data collection (750 hours, 950 GB of data, 17 different sensor modalities), analysing the data loss (less than 0.4%) and battery consumption (≈ 6% on average per hour). The application is available as open source.

We explain how to obtain a highly versatile and precisely annotated dataset for the multimodal locomotion of mobile users. After presenting the experimental setup, data management challenges and potential applications, we conclude with the best practices for assuring data quality and reducing loss. The dataset currently comprises 7 months of measurements, collected by smartphone's sensors and a body-worn camera, while the 3 participants used 8 different modes of transportation. It comprises 950 GB of sensor data, which corresponds to 750 hours of labelled data. The obtained data will be useful for a wide range of research questions related to activity recognition, and will be made available to the community1.

We present a way to annotate cystoscopy finding on Google Glass in a reproducible and hands free manner for use by surgeons during operations in the sterile environment inspired by the current practice of hand-drawn sketches. We developed three data entry variants based on speech and head movements. We assessed the feasibility, benefits and drawbacks of the system with 8 surgeons and Foundation Doctors having up to 30 years' cystoscopy experience at a UK hospital in laboratory trials. We report data entry speed and error rate of input modalities and contrast it with the participants' feedback on their perception of usability, acceptance, and suitability for deployment. The results are supportive of new data entry technologies and point out directions for future improvement of eyewear computers. The findings can be generalised to other endoscopic procedures (e.g. OGD/laryngoscopy) and could be included within hospital IT in the future.