Enabling Reproducible Research in Sensor-Based Transportation Mode Recognition With the Sussex-Huawei Dataset

Abstract

This work was supported by Huawei Technologies through the project "Activity Sensing Technologies for Mobile Users."ABSTRACT Transportation and locomotion mode recognition from multimodal smartphone sensors is useful for providing just-in-time context-aware assistance. However, the field is currently held back by the lack of standardized datasets, recognition tasks, and evaluation criteria. Currently, the recognition methods are often tested on the ad hoc datasets acquired for one-off recognition problems and with different choices of sensors. This prevents a systematic comparative evaluation of methods within and across research groups. Our goal is to address these issues by: 1) introducing a publicly available, largescale dataset for transportation and locomotion mode recognition from multimodal smartphone sensors; 2) suggesting 12 reference recognition scenarios, which are a superset of the tasks we identified in the related work; 3) suggesting relevant combinations of sensors to use based on energy considerations among accelerometer, gyroscope, magnetometer, and global positioning system sensors; and 4) defining precise evaluation criteria, including training and testing sets, evaluation measures, and user-independent and sensorplacement independent evaluations. Based on this, we report a systematic study of the relevance of statistical and frequency features based on the information theoretical criteria to inform recognition systems. We then systematically report the reference performance obtained on all the identified recognition scenarios using a machine-learning recognition pipeline. The extent of this analysis and the clear definition of the recognition tasks enable future researchers to evaluate their own methods in a comparable manner, thus contributing to further advances in the field. The dataset and the code are available online. 1

Transportation mode recognition fusing wearable motion, sound and vision sensors

“…In principle a recurrent network could also be applied to video streams, however the images in the SHL dataset come from a timelapse camera which took a picture every 30 seconds. • Second, over-fitting is a crucial issue in activity recognition, such as a classifier trained on one user with specific senor placement tends to show degraded performance for other users and sensor placement [10]. In this paper, the mono-modal classifiers simply employ a generic technique, e.g.…”

“…The Sussex-Huawei Locomotion-Transportation (SHL) dataset is one of the biggest multimodal dataset for transportation and locomotion mode recognition from mobile devices [10], [32]. The dataset was recorded over 7 months by 3 users engaging in 8 different transportation modes: Still, Walk, Run, Bike, Car, Bus, Train and Subway.…”

“…These devices are embedded with multimodal sensors including motion sensors, GPS (global positioning system), microphones and cameras. There have been many studies on analyzing the mode of transportation from the data captured by the sensors of these wearable devices with machine learning techniques [10]. Motion and GPS sensors are widely used for transportation mode detection.…”

“…In this paper we conduct the first work that combines the motion, sound and vision modalities for transportation mode recognition. The state-of-the-art Sussex-Huawei Locomotion-Transportation dataset [10], [32] contains rich sensor modalities (including the three above mentioned sensors), which enable us to carry out this research, in order to recognize eight modes of transportation: being still, walking, running, cycling, being in a car, being on a bus, train or subway (Sec. II).…”

Sound-based Transportation Mode Recognition with Smartphones

“…The SHL dataset is a major outcome of a large-scale longitudinal data collection campaign, which collected 2812 hours of labeled data over a period of 7 months corresponding to 17,562 km in the south-east of the UK including London [17,18]. The SHL dataset was recorded by three participants engaging in eight transportation and locomotion activities in real-life settings: Still, Walk, Run, Bike, Car, Bus, Train and Subway.…”