IoT-Based Accelerometer Sensors for Early Detection and Continuous Monitoring of Parkinson's Disease Symptoms

1. Introduction

Parkinson's complaint (PD) stands as a redoubtable and progressive neurological complaint that exacts a profound risk on the lives of those affected. Named after the visionary croaker.Dr. James Parkinson, who first described its symptoms in 1817, PD has become one of the most current neurodegenerative conditions encyclopedically. Characterized by a range of motor andnon-motor symptoms, PD presents a complex clinical geography that challenges both cases and healthcare providers. The graveness of Parkinson's complaint lies not only in its frequence but also in its insidious onset and gradational progression. Tsipouras et al. (2011) Motor symptoms similar to temblors, bradykinesia(slowness of movement), muscular severity, and postural insecurity crop gradually, frequently obscuring the original signs. Still, PD isn't solely defined by these motor impairments; it encompasses a wide diapason of non-motor symptoms, including cognitive decline, mood diseases, and autonomic dysfunction, further complicating opinion and operation. [ Samà et al., 2018] Beforehand discovery and nonstop monitoring of PD symptoms have surfaced as critical imperatives in the hunt to alleviate the complaint's impact. Timely recognition of motor and non-motor symptoms is consummate for easing early intervention and substantiated treatment strategies. This is where the confluence of healthcare and technology, specifically the Internet of effects(IoT) and wearable accelerometer detectors, offers a promising result. In this paper, we embark on a trip through the innovative geography of IoT- grounded wearable accelerometer detectors, with a singular focus on their vital part in PD symptom discovery and nonstop monitoring is shown in Figure 1. These detectors, compact and invisible, are designed to quantify movement, furnishing a rich source of data that transcends traditional clinical assessments. By using the power of IoT technology, these accelerometers enable non-invasive and real- time data collection, offering the eventuality for early symptom identification and substantiated care. The posterior sections of this paper claw into the specific symptoms of PD that can be effectively detected and tracked using accelerometers. We punctuate the advantages of early discovery, illustrating how timely interventions can mainly enhance patient issues while reducing the burden on healthcare systems. Likewise, we explore exemplifications of being IoT- grounded results, showcasing their effectiveness in symptom discovery and operation. As technology advances, the integration of machine literacy algorithms for data analysis enriches the capabilities of these detectors, allowing for the identification of subtle changes in motor function over time. In conclusion, this paper underscores the transformative eventuality of IoT- grounded accelerometer detectors in reconsidering the geography of Parkinson's complaint monitoring. The new openings will be created because of the integration of the IoT predicted results and enhance the operation and diagnosis of neurological condition. Figure 1 represents the symptoms of Parkinson Disease.

Figure 1.

Symptoms of Parkinson’s disease