  The combination of increased life expectancy and falling birth rates is
resulting in an aging population. Wearable Sensor-based Human Activity
Recognition (WSHAR) emerges as a promising assistive technology to support the
daily lives of older individuals, unlocking vast potential for human-centric
applications. However, recent surveys in WSHAR have been limited, focusing
either solely on deep learning approaches or on a single sensor modality. In
real life, our human interact with the world in a multi-sensory way, where
diverse information sources are intricately processed and interpreted to
accomplish a complex and unified sensing system. To give machines similar
intelligence, multimodal machine learning, which merges data from various
sources, has become a popular research area with recent advancements. In this
study, we present a comprehensive survey from a novel perspective on how to
leverage multimodal learning to WSHAR domain for newcomers and researchers. We
begin by presenting the recent sensor modalities as well as deep learning
approaches in HAR. Subsequently, we explore the techniques used in present
multimodal systems for WSHAR. This includes inter-multimodal systems which
utilize sensor modalities from both visual and non-visual systems and
intra-multimodal systems that simply take modalities from non-visual systems.
After that, we focus on current multimodal learning approaches that have
applied to solve some of the challenges existing in WSHAR. Specifically, we
make extra efforts by connecting the existing multimodal literature from other
domains, such as computer vision and natural language processing, with current
WSHAR area. Finally, we identify the corresponding challenges and potential
research direction in current WSHAR area for further improvement.
