Tackling the SHL challenge 2020 with person-specific classifiers and semi-supervised learning

The SHL recognition challenge 2020 was an open competition in activity recognition where the participants were tasked with recognizing eight different modes of locomotion and transportation with smartphone sensors. The main challenges were that the training data was recorded by a different person than the validation and test data, and that the smartphone location in the test data was unknown to the participants. We, team "Third time's a charm", tackled the first challenge by attempting to identify the persons with clustering, and then performed cluster/person-specific feature selection to build a separate classifier for each person. The smartphone location appears not to make much difference. We also used semi-supervised learning to classify the test data. Internal tests using this methodology yielded an accuracy of 81.01%.