Design and Engineering of a Medical Wearable Device for Parkinson’s Disease Management

The emergence of Internet of Things (IoT)-based wearable devices has ushered in new possibilities for the detection, diagnosis, and quantification of Parkinson’s Disease (PD). These devices predominantly rely on inertial sensors and computational algorithms, offering promising advancements. However, they also introduce fresh challenges, including concerns related to security, privacy, connectivity, and power efficiency. From a clinical perspective, effective monitoring of patients’ motor function is crucial for adjusting L-dopa doses, avoiding adverse effects, and mitigating motor activity deterioration. Adapting to variations in motor functions observed between different appointments poses a significant hurdle for clinicians, potentially leading to incorrect decisions. The principal objective of this chapter is to establish a comprehensive ecosystem that streamlines enhanced evaluation of PD stages and disease progression, especially concerning tremor and bradykinesia. This chapter endeavors to craft a holistic ecosystem capable of capturing motion data associated with PD and securely transmitting it to the cloud for storage, data processing, and severity estimation, all facilitated by specially developed learning algorithms.