Human activity recognition based on multi‐instance learning

Abstract

AbstractHuman activity recognition (HAR) is the process of classifying a person's actions, and it is an essential task for many human‐centered applications. Multi‐instance learning (MIL) is a special case of machine learning where the training examples are bags containing many instances, and a single class label is assigned for an entire bag of instances. In this study, we integrated these two concepts by introducing a novel approach: “human activity recognition based on multi‐instance learning”, called HAR‐MIL. Unlike previous studies, the proposed HAR‐MIL method represents human activities differently: as a bag of various wearable sensors (gyroscope, magnetometer, accelerometer, and linear acceleration). HAR‐MIL presents an applicable and flexible model by providing multi‐instance representation and eliminating the restrictions of traditional single‐instance representation. Therefore, the adverse effects of missing data, defective sensors, and biased measurement on activity classification performance were minimized. This study is the first to investigate the performance of two MIL algorithms (SimpleMI and MIWrapper) on HAR. In this study, we explored the effect of four main factors (sensor positions, sensor types, base learners, and single or multiple participants) on the multi‐instance representation of relevant human daily activities. The effectiveness of the proposed HAR‐MIL method was demonstrated on 50 participant‐based and sensor‐position‐based activity recognition datasets. The experimental results showed that HAR‐MIL is effective for wearable sensor‐based HAR with high classification accuracy (99.32%). Furthermore, the results showed that the proposed method outperformed the state‐of‐the‐art methods by 10% on average on the same dataset.