Locomotion and Transportation Mode Recognition from GPS and Radio Signals: Summary of SHL Challenge 2021

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Abstract
In this paper we summarize the contributions of participants to the fourth Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge organized at the HASCA Workshop of UbiComp/ISWC 2021. The goal of this machine learning/data science challenge is to recognize eight locomotion and transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the radio sensor data (GPS location, GPS reception, WiFi reception and Cell reception) of a smartphone in a user-independent manner. The training and testing data are collected by different users with a smartphone placed at the Hips position. We introduce the dataset used in the challenge and the protocol of the competition. We present a meta-analysis of the contributions from 15 submissions, their approaches, the software tools used, computational cost and the achieved results. The challenge evaluates the recognition performance by comparing predicted to ground-truth labels at every second, but puts no constraints on the maximum decision window length. Overall, two submissions achieved F1 scores between 70% and 80%, one between 60% and 70%, five between 50% and 60%, and seven below 50%. Due to the technical challenges of data synchronization, sensor unavailability and sensor diversity, the overall performance based on GPS and radio sensors is lower than the performance achieved by motion sensors in previous challenges (SHL 2018-2020). Finally, we present a baseline implementation to help understand the contribution of each sensor modality to the recognition task.

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