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Smart Devices Based multisensory Approach for Complex Human Activity Recognition

Computers, Materials & Continua 2022年第2期70卷 3221-3234页

作者： Muhammad Atif Hanif Tallha Akram Aamir Shahzad Muhammad Attique Khan Usman Tariq Jung-In Choi Yunyoung Nam Zanib Zulfiqar Department of Electrical and Computer Engineering COMSATS University IslamabadWah Campus47040Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadAbbottabad Campus22040Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Applied Artificial Intelligence Ajou UniversitySuwonKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Electrical and Computer Engineering CUISahiwalPakistan

Sensors based Human Activity Recognition(HAR)have numerous applications in eHeath,sports,fitness assessments,ambient assisted living(AAL),human-computer interaction and many *** human physical activity can be monitored by using wearable sensors or external *** usage of external devices has disadvantages in terms of cost,hardware installation,storage,computational time and lighting conditions ***,most of the researchers used smart devices like smart phones,smart bands and watches which contain various sensors like accelerometer,gyroscope,GPS etc.,and adequate processing *** the task of recognition,human activities can be broadly categorized as basic and complex human *** of complex activities have received very less attention of researchers due to difficulty of problem by using either smart phones or smart *** reasons include lack of sensor-based labeled dataset having several complex human daily life *** of the researchers have worked on the smart phone’s inertial sensors to perform human activity recognition,whereas a few of them used both pocket and wrist *** this research,we have proposed a novel framework which is capable to recognize both basic and complex human activities using builtin-sensors of smart phone and smart *** have considered 25 physical activities,including 20 complex ones,using smart device’s built-in *** the best of our knowledge,the existing literature consider only up to 15 activities of daily life.

关键词： Complex human activities human daily life activities features extraction data fusion multi-sensory smartwatch smartphone

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Intelligent Biometric Information Management

引用

Intelligent Information Management 2010年第9期2卷 499-511页

作者： Harry Wechsler 不详

We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation, and functionality, and robustness refers to the ability to handle incomplete and/or corrupt adversarial information, on one side, and image and or device variability, on the other side. The proposed methodology is model-free and non-parametric. It draws support from discriminative methods using likelihood ratios to link at the conceptual level biometrics and forensics. It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised lea- rning, and Information Theory (IY) using mutual information. The key concepts supporting the proposed methodology are a) local estimation to facilitate learning and prediction using both labeled and unlabeled data;b) similarity metrics using regularity of patterns, randomness deficiency, and Kolmogorov complexity (similar to MDL) using strangeness/typicality and ranking p-values;and c) the Cover – Hart theorem on the asymptotical performance of k-nearest neighbors approaching the optimal Bayes error. Several topics on biometric inference and prediction related to 1) multi-level and multi-layer data fusion including quality and multi-modal biometrics;2) score normalization and revision theory;3) face selection and tracking;and 4) identity management, are described here using an integrated approach that includes transduction and boosting for ranking and sequential fusion/aggregation, respectively, on one side, and active learning and change/ outlier/intrusion detection realized using information gain and martingale, respectively, on the other side. The methodology proposed can be mapped to additional types of information beyond biometrics.

关键词： Authentication Biometrics Boosting Change Detection Complexity Cross-Matching Data Fusion Ensemble Methods Forensics Identity Management Imposters Inference Intelligent Information Management Margin gain MDL multi-sensory Integration Outlier Detection P-Values Quality Randomness Ranking Score Normalization Semi-Supervised Learning Spectral Clustering Strangeness Surveillance Tracking Typicality Transduction

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