How ChatGPT Assists Novices in Human Activity Recognition

It is evident that large language models are not only capable of generating text and images but also of facilitating discussions on the latest research in the field of human activity recognition due to their extensive knowledge. The objective of this paper is to observe how novices in machine learning in 2024 can rapidly develop their skills with the assistance of ChatGPT. As an initial introduction, we started with a dialogue-based consultation with ChatGPT and executed a classification task using the Iris dataset on Google Colaboratory. This initial task served to deepen the novices' basic understanding of machine learning. Subsequently, the authors, as part of Team Shonan-Blue, participated in the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge (hereafter SHL recognition challenge). In this challenge, the authors developed a simple human activity recognition system without expert advice and relying solely on interactions with ChatGPT. The system was constructed by integrating four classical methods: k-Nearest Neighbor algorithm (kNN), Neural Network (NN), Random Forest (RF), and Support Vector Machine (SVM). The models were selected for their differing approaches, which were anticipated to enhance prediction accuracy when combined. For feature extraction, we utilized only the primary four features (maximum, minimum, mean, standard deviation) derived from 5-second sensor data collected by accelerometers, gyroscopes, and magnetometers. The outputs of the four models were then processed ensemble to obtain the final prediction results. This yielded an F1 score of 0.936. Although the limited challenge period may not have permitted the optimal performance, the observed growth of novices who, with the assistance of ChatGPT and without the use of GPU power, managed to develop a functional system, is encouraging for future newcomers in the field of activity recognition.