Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge

In this paper we summarize the contributions of participants to the Sussex-Huawei Transportation-Locomotion (SHL) Recognition Challenge organized at the HASCA Workshop of UbiComp 2018. The SHL challenge is a machine learning and data science competition, which aims to recognize eight transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the inertial and pressure sensor data of a smartphone. We introduce the dataset used in the challenge and the protocol for the competition. We present a meta-analysis of the contributions from 19 submissions, their approaches, the software tools used, computational cost and the achieved results. Overall, two entries achieved F1 scores above 90%, eight with F1 scores between 80% and 90%, and nine between 50% and 80%

Benchmarking the SHL Recognition Challenge with classical and deep-learning pipelines

In this paper we, as part of the Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge organizing team, present reference recognition performance obtained by applying various classical and deep-learning classifiers to the testing dataset. We aim to recognize eight modes of transportation (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from smartphone inertial sensors: accelerometer, gyroscope and magnetometer. The classical classifiers include naive Bayesian, decision tree, random forest, K-nearest neighbour and support vector machine, while the deep-learning classifiers include fully-connected and convolutional deep neural networks. We feed different types of input to the classifier, including hand-crafted features, raw sensor data in the time domain, and in the frequency domain. We employ a post-processing scheme to improve the recognition performance. Results show that convolutional neural network operating on frequency domain raw data achieves the best performance among all the classifiers

A case study for human gesture recognition from poorly annotated data

In this paper we present a case study on drinking gesture recognition from a dataset annotated by Experience Sampling (ES). The dataset contains 8825 "sensor events" and users reported 1808 "drink events" through experience sampling. We first show that the annotations obtained through ES do not reflect accurately true drinking events. We present then how we maximise the value of this dataset through two approaches aiming at improving the quality of the annotations post-hoc. First, we use template-matching (Warping Longest Common Subsequence) to spot a subset of events which are highly likely to be drinking gestures. We then propose an unsupervised approach which can perform drinking gesture recognition by combining K-Means clustering with WLCSS. Experimental results verify the effectiveness of the proposed method