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

Abstract

Accelerometer-based IoT wearable sensors for PD symptom detection and assessment are discussed in this chapter. Accelerometers measure PD-related movement patterns and tremors in the IoT system. These discrete body sensors collect non-invasive, real-time data for early symptom detection and continuous monitoring. Accelerometers can track symptoms such tremors, bradykinesia, and postural instability. It also emphasizes early PD detection and how it might improve patient outcomes and lower healthcare expenditures. The integration of machine learning algorithms for data analysis further enriches the capabilities of these wearable sensors, enabling the identification of subtle changes in motor function over time. This chapter concludes that IoT-based accelerometer sensors can transform Parkinson's disease monitoring. By detecting, analyzing, and personalizing care, these sensors may enhance PD patients' lives. IoT accelerometers provide early intervention and better management of this complex neurological disorder.