Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …

Recognition of human movements with radar for ambient activity monitoring is a developed
area of research that yet presents outstanding challenges to address. In real environments …

This paper presents a framework based on multilayer bi-LSTM network (bidirectional Long
Short-Term Memory) for multimodal sensor fusion to sense and classify daily activities' …

This article covers radar signal processing for sensing in the context of assisted living (AL).
This is presented through three example applications: human activity recognition (HAR) for …

Fall detection is a major challenge for researchers. Indeed, a fall can cause injuries such as
femoral neck fracture, brain hemorrhage, or skin burns, leading to significant pain. However …

Indoor human activity recognition is actively studied as part of creating various intelligent
systems with applications in smart home and office, smart health, internet of things, etc …

Fall is a challenging task that poses a great danger to the elderly person's health as they
carry out their daily routines and activities and could lead to serious injuries, long …

Inertial sensing suites now permeate all forms of smart automation, yet a plateau exists in
the real-world derivation of global orientation. Magnetic field fluctuations and inefficient …

This paper presents different information fusion approaches to classify human gait patterns
and falls in a radar sensors network. The human gaits classified in this work are both …

This article concerns the issue of how to combine the multidomainradar information,
including range–Doppler, time–Doppler, and time–range, for human activity recognition …