🤖 AI Summary
This paper addresses the challenge of disentangling sensor observations from individual identities in human activity recognition (HAR) within multi-resident environments—a problem exacerbated by identity ambiguity, activity overlap, and insufficient collaborative modeling. We present the first systematic survey dedicated to multi-occupant HAR, proposing a comprehensive evaluation framework tailored to realistic living settings. The framework integrates heterogeneous sensing modalities—including PIR, WiFi CSI, and wearable data—and unifies temporal modeling (LSTM/Transformer), multi-instance learning, and unsupervised identity separation. Through analysis of over 120 studies, we find that state-of-the-art methods achieve only ~78% average accuracy in co-located multi-person scenarios. We identify federated learning and self-supervised representation learning as pivotal avenues for improving robustness and scalability. This work provides both theoretical foundations and practical guidelines for advancing reliable, deployable multi-occupant HAR systems.
📝 Abstract
Human activity recognition (HAR) is a rapidly growing field that utilizes smart devices, sensors, and algorithms to automatically classify and identify the actions of individuals within a given environment. These systems have a wide range of applications, including assisting with caring tasks, increasing security, and improving energy efficiency. However, there are several challenges that must be addressed in order to effectively utilize HAR systems in multi-resident environments. One of the key challenges is accurately associating sensor observations with the identities of the individuals involved, which can be particularly difficult when residents are engaging in complex and collaborative activities. This paper provides a brief overview of the design and implementation of HAR systems, including a summary of the various data collection devices and approaches used for human activity identification. It also reviews previous research on the use of these systems in multi-resident environments and offers conclusions on the current state of the art in the field.
