A multilabel classification approach for complex human activities using a combination of emerging patterns and fuzzy sets
| Autor: | Nehal A. Sakr, Mervat Abu-Elkheir, Ahmed Atwan, Hassan Soliman |
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| Rok vydání: | 2019 |
| Předmět: |
Fuzzy sets
General Computer Science business.industry Computer science media_common.quotation_subject Fuzzy set Complex activity recognition Machine learning computer.software_genre Fuzzy logic Activity recognition ComputingMethodologies_PATTERNRECOGNITION Discriminative model Simple (abstract algebra) Pattern mining Multilabel classification Pervasive computing Artificial intelligence Electrical and Electronic Engineering Function (engineering) business computer media_common |
| Popis: | In our daily lives, humans perform different Activities of Daily Living (ADL), such as cooking, and studying. According to the nature of humans, they perform these activities in a sequential/simple or an overlapping/complex scenario. Many research attempts addressed simple activity recognition, but complex activity recognition is still a challenging issue. Recognition of complex activities is a multilabel classification problem, such that a test instance is assigned to a multiple overlapping activities. Existing data-driven techniques for complex activity recognition can recognize a maximum number of two overlapping activities and require a training dataset of complex (i.e. multilabel) activities. In this paper, we propose a multilabel classification approach for complex activity recognition using a combination of Emerging Patterns and Fuzzy Sets. In our approach, we require a training dataset of only simple (i.e. single-label) activities. First, we use a pattern mining technique to extract discriminative features called Strong Jumping Emerging Patterns (SJEPs) that exclusively represent each activity. Then, our scoring function takes SJEPs and fuzzy membership values of incoming sensor data and outputs the activity label(s). We validate our approach using two different dataset. Experimental results demonstrate the efficiency and superiority of our approach against other approaches. |
| Databáze: | OpenAIRE |
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