Human Activity Recognition (HAR) is a well-studied field with research dating
back to the 1980s. Over time, HAR technologies have evolved significantly from
manual feature extraction, rule-based algorithms, and simple machine learning
models to powerful deep learning models, from one sensor type to a diverse
array of sensing modalities. The scope has also expanded from recognising a
limited set of activities to encompassing a larger variety of both simple and
complex activities. However, there still exist many challenges that hinder
advancement in complex activity recognition using modern deep learning methods.
In this paper, we comprehensively systematise factors leading to inaccuracy in
complex HAR, such as data variety and model capacity. Among many sensor types,
we give more attention to wearable and camera due to their prevalence. Through
this Systematisation of Knowledge (SoK) paper, readers can gain a solid
understanding of the development history and existing challenges of HAR,
different categorisations of activities, obstacles in deep learning-based
complex HAR that impact accuracy, and potential research directions.

人类活动识别（HAR）是一个经过研究的领域，研究始于 20 世纪 80 年代。本文通过对数据的多样性和模型的容量等因素进行系统研究，全面梳理现代深度学习方法在识别复杂 HAR 时存在的准确性问题，并提出了潜在的研究方向。