A systematic review of healthcare cyber–physical systems with associated innovative technologies for Alzheimer’s and Parkinson’s Diseases

The emergence of cyber–physical systems (CPS) and new healthcare technologies provides innovative ways to diagnose and monitor diseases. This paper presents a systematic review of studies related to healthcare CPS for Alzheimer’s disease (AD) and Parkinson’s disease (PD), two of the most prevalent neurodegenerative diseases worldwide. The studies reviewed are classified into four main application domains: (i) assistance with diagnosis, (ii) symptom monitoring, (iii) prediction of treatment response, and (iv) support for therapy and rehabilitation. Methodological approaches are grouped into descriptive, predictive, and prescriptive analytics, evaluating their contributions to patient care and CPS applications. Most PD studies focus on diagnosis and symptom monitoring, using devices such as wearable sensors, EEG/EMG systems, and video tracking combined with machine learning, deep learning, and optimization algorithms. Most AD studies focus on treatment response and rehabilitation support, employing force or pressure sensors, optical motion trackers, and clinical decision support systems (CDSS), along with artificial intelligence techniques and digital twin models. However, healthcare management along the patient care pathway remains underexplored for both diseases. This review also identifies challenges for real-world use, such as integrating data from multiple sources, making pathway-level decisions, and creating CPS frameworks that are more personalized, explainable, and patient-centered.