Enhanced Online Segmentation and Performance Evaluation Method for Real- Time Activity Recognition in Smart Homes
Résumé
This paper presents a robust method for realtime
recognition of Activities of Daily Living (ADLs) in smart
home environments using IoT data. Our approach improves
the segmentation of sensor data streams into distinct activities
by leveraging IoT sensor spatiotemporal features and applies
the Needleman-Wunsch method to align predicted and actual
activities. Testing on the Aruba dataset achieved 83.2% accuracy,
demonstrating superior performance in segmentation and activity
recognition compared to existing dynamic methods. Future work
will focus on developing a sensor installation simulator to enhance
accuracy and reliability.