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Rehabilitation of hand function following neurological injuries is critical for restoring
independence in daily living activities. Traditional rehabilitation assessment tools, such
as the Barthel Index, rely heavily on subjective clinical observation, which may lack
precision in detecting subtle neuromuscular impairments. This study presents the
development and evaluation of a SMART MONITORING ADAPTIVE SPOON (SMAS),
an Internet of Things (IoT)-enabled assistive device designed to monitor hand tremors
and grip strength while providing rehabilitative feedback. The device integrates an
ESP32 microcontroller, MPU6050 accelerometer/gyroscope sensors, Force-Sensitive
Resistor (FSR) pressure sensors and haptic feedback mechanisms within an
ergonomic spoon handle. Data collected during feeding activities are transmitted
wirelessly via the Dashboard IoT platform, enabling real-time remote monitoring and objective
quantification of motor control improvements. Testing with a C5 tetraplegia patient
demonstrated 20-25% improvements in tremor and grip metrics over 2 weeks. The
device successfully combines assistive and rehabilitative functionality while maintaining
comfort and ease of use. Results demonstrate that IoT-integrated rehabilitation devices
offer a practical, data-driven alternative to traditional subjective assessment methods,
improving clinical decision-making and patient motivation. The SMART MONITORING
ADAPTIVE SPOON represents a viable, low-cost (RM45-70) solution for personalized
hand rehabilitation in both clinical and home-based settings.
Keywords: Assistive Technology, Hand Rehabilitation, IoT, Accelerometer, Force
Sensing, Tremor Detection, Activities of Daily Living (ADL), Barthel Index,
Neuroplasticity.