  Neurological disorders, including stroke, spinal cord injuries, multiple
sclerosis, and Parkinson's disease, generally lead to diminished upper
extremity (UE) function, impacting individuals' independence and quality of
life. Traditional assessments predominantly focus on standardized clinical
tasks, offering limited insights into real-life UE performance. In this
context, this review focuses on wearable technologies as a promising solution
to monitor UE function in neurologically impaired individuals during daily life
activities. Our primary objective is to categorize the different sensors, data
collection and data processing approaches employed. What comes to light is that
the majority of studies involved stroke survivors, and predominantly employed
inertial measurement units and accelerometers to collect kinematics. Most
analyses in these studies were performed offline, focusing on activity duration
and frequency as key metrics. Although wearable technology shows potential in
monitoring UE function in real-life scenarios, an ideal solution that combines
non-intrusiveness, lightweight design, detailed hand and finger movement
capture, contextual information, extended recording duration, ease of use, and
privacy protection remains an elusive goal. Furthermore, it stands out a
growing necessity for a multimodal approach in capturing comprehensive data on
UE function during real-life activities to enhance the personalization of
rehabilitation strategies and ultimately improve outcomes for these
individuals.

Signal Processing