Summary of SHL Challenge 2025: Locomotion and Transportation Mode Recognition Using Foundation Models

The paper summarizes the contributions of participants to the seventh Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge organized at the HASCA Workshop of UbiComp/ISWC 2025. Motivated by the growing interest in foundation models, particularly large language models, this year's edition will explore their application to transportation mode recognition. The goal is to recognize eight locomotion and transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the motion (accelerometer, gyroscope, magnetometer) sensor data of a smartphone in a way which is user-independent and smartphone position-independent. The training data of a ''train'' user is available from smartphones placed at four body positions (Hand, Torso, Bag and Hips). The testing data originates from ''test'' users with a smartphone placed at three body positions (Torso, Bag or Hips). Participants will be required to develop a solution that leverages a recognized and well-established foundation model. We introduce the dataset used in the challenge and the protocol of the competition. We present a meta-analysis of the contributions from 7 submissions, their approaches, the software tools used, computational cost and the achieved results. Overall, three submissions achieved an F1 score between 80% and 90%, two between 70% and 80%, and two below 60%, with a latency of maximum of 5 seconds. The top performance is comparable to the results achieved with non-foundation models in previous challenges.