Aplikasi Assistive Technology Parkinson dengan FSM dan Risk Stratification Berbasis Mobile
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Abstract
The development of mobile application-based health technology provided opportunities to support the care of patients with Parkinson’s disease who experienced progressive motor disorders. Within the family context, common challenges included medication non-compliance, a high risk of falls associated with freezing of gait without early monitoring, and limited caregiver involvement in continuous supervision. Therefore, mobile-based assistive technology was required to support medication management, fall risk monitoring, and patient–caregiver coordination. This study applied assistive technology for Parkinson’s management by implementing a Finite State Machine as a multi-stage model for detecting movement conditions, combined with risk stratification to translate sensor signals into interpretable risk levels. The system also involved caregivers through monitoring and notification mechanisms to enable early intervention. The system was developed iteratively using Extreme Programming, including planning, design, coding, and testing phases. Implementation was conducted on Android using Kotlin and Jetpack Compose, utilizing accelerometer and gyroscope sensors, as well as Firebase for authentication, data storage, synchronization, and notifications. Functional testing was performed using black-box testing. The results indicated that the application provided medication management and fall risk monitoring, while improving coordination between patients and caregivers. The system demonstrated potential as an assistive technology to support Parkinson’s care within the family environment.
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