Sussex-Huawei Locomotion and Transportation Dataset

This dataset is a highly versatile and precisely annotated large-scale dataset of smartphone sensor data for multimodal locomotion and transportation analytics of mobile users.

The dataset comprises 7 months of measurements, collected from all sensors of 4 smartphones carried at typical body locations, including the images of a body-worn camera, while 3 participants used 8 different modes of transportation in the southeast of the United Kingdom, including in London.

In total 28 context labels were annotated, including transportation mode, participant’s posture, inside/outside location, road conditions, traffic conditions, presence in tunnels, social interactions, and having meals.

The total amount of collected data exceed 950 GB of sensor data, which corresponds to 2812 hours of labelled data and 17562 km of traveled distance.
The potential applications arising from this dataset include:

• Machine-learning systems to automatically recognize modes of transportations from mobile phone data
• Road condition analysis and recognition
• Traffic conditions analysis and recognition.
• Assessment of Google’s activity and transportation recognition API in comparison to custom algorithms
• Probabilistic mobility modelling
• Activity recognition (e.g. automatic detection of eating and drinking)
• Novel localization techniques using dynamic fusion of sensors
• Radio signal propagation analsis
• Image-based activity and transportation mode recognition

The current recommended publication regarding the dataset is [1]. The current recommended publication regarding the application which was used to collect the dataset is [2].

• [1] H. Gjoreski, M. Ciliberto, L. Wang, F. J. Ordoñez Morales, S.Mekki, S.Valentin, D. Roggen, “The University of Sussex-Huawei Locomotion and Transportation Dataset for Multimodal Analytics with Mobile Devices”, In IEEE Access, 2018
• [2] M. Ciliberto, F. J. Ordoñez Morales, H. Gjoreski, D. Roggen, S.Mekki, S.Valentin. “High reliability Android application for multidevice multimodal mobile data acquisition and annotation.” In ACM Conference on Embedded Networked Sensor Systems. ACM, 2017.

We recommend to refer to the dataset as follows in your publications:

• Use at least once the complete name: “The University of Sussex-Huawei Locomotion and Transportation Dataset” or “The Sussex-Huawei Locomotion and Transportation Dataset“. You may introduce the acronym of the dataset as well: “The University of Sussex-Huawei Locomotion and Transportation (SHL) Dataset“.
• Subsequently, you may refer to the dataset with its acronym: “The SHL Dataset“.