Abstract
The proven approach successfully recognizes the activity of daily living is a classifier training on feature vectors created from streamed sensor data. However, there is still room to improve feature extraction techniques in that the activity of daily living data are often nominal or ordinal. The ordinal data can be likely less discriminative due to the great uncertainty in level of measurement. This article provides a framework with novel activity of daily living primitive that introduces an enhanced feature selector with linear time complexity. The extension to traditional approaches is that the present framework considers the following: (1) defining activity of daily living primitives and constructing a primitive vocabulary, (2) reducing data when representing raw activity data, and (3) selecting an appropriate primitive set for each testing activity. The empirical results reveal that a pre-trained portable primitive vocabulary not only outperforms the existing baseline frameworks but also greatly faci...
Highlights
Activity recognition systems deployed in smart homes are characterized by the abilities of detecting human actions and their goals and providing them with the decent assistance
In order to validate the effectiveness of activity recognition framework based on ADL primitive (AR-ADLP) framework, we introduce two evaluation metrics, namely, the total activity recognition accuracy rate and the standard deviation of activity recognition srate as follows: lr rate =
We propose a novel AR-ADLP
Summary
Activity recognition systems deployed in smart homes are characterized by the abilities of detecting human actions and their goals and providing them with the decent assistance. Such assistive technologies have been adopted nowadays by smart homes and healthcare applications in practice[1,2] and have delivered promising results for offering either more caring services to an elderly resident[3,4,5] or responsive assistance in an emergency situation.[6]. There are various sensors to collect raw activity data stream in the field of daily activity recognition, and a huge amount of sensor data are certainly generated.
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More From: International Journal of Distributed Sensor Networks
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