Real-time activity recognition in mobile phones based on its accelerometer data

Ayu, Media Anugerah; Ismail, Siti Aisyah; Mantoro, Teddy; Matin, Ahmad Faridi Abdul

doi:10.1109/iac.2016.7905732

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…For example point 6 (the palm of the right hand) have three shapes which means some of the studies were used in this position for classification human activities by using three devices that are described in the following: a) A red circle contains (A): Smartphone was used for sensing the accelerometer data in the reference [26] and [27]. The reference [26] used NB and KNN for classification human activities but the reference [27] used logitBoost classifier. b) A red circle contains (A and G): Smartphone was used to collect Accelerometer and Gyroscope data for classifying human activities.…”

Section: B Online Vs Offline Classificationmentioning

confidence: 99%

“…By investigating 48 different research, it is found 56.25% of researches focused on using traditional algorithms for classification, some of those researches adapting the traditional algorithm for achieving high accuracy, 33.33% using deep learning, and 10.4% using algorithms (deep learning or/and traditional) for examining their proposed system but they didn't focus on the algorithm such as in [33] [26]. Maekawa et al [33] compared two traditional algorithms, AdaBoost and DT, for examining the efficiency of classifier when collecting the data from heterogeneous sensors.…”

Section: Traditional and Deep Learning Techniquesmentioning

confidence: 99%

“…Hayashi et al as well as Hammerla et al [41] proposed a new method that called the empirical cumulative distribution function (ECDF) to representing sensor data for improving the efficiency of feature extraction phase. Ayu et al [26] proposed a system for realtime activity recognition and compared two traditional classifiers (NB and KNN) for exploring the influence of training data size on recognition accuracy. Nhac et al [28] comparing four traditional classifiers (RF, NB, KNN, SVM) for examining their proposed Mobile Online Activity Recognition System (MOARS) to automatically recognize several activities of smartphone users.…”

Section: Traditional and Deep Learning Techniquesmentioning

confidence: 99%

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Survey on Human Activity Recognition based on Acceleration Data

Slim¹,

Atia²,

Elfattah³

et al. 2019

ijacsa

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Human activity recognition is an important area of machine learning research as it has many utilization in different areas such as sports training, security, entertainment, ambientassisted living, and health monitoring and management. Studying human activity recognition shows that researchers are interested mostly in the daily activities of the human. Therefore, the general architecture of HAR system is presented in this paper, along with the description of its main components. The state of the art in human activity recognition based on accelerometer is surveyed. According to this survey, Most of the researches recently used deep learning for recognizing HAR, but they focused on CNN even though there are other deep learning types achieved a satisfied accuracy. The paper displays a two-level taxonomy in accordance with machine learning approach (either traditional or deep learning) and the processing mode (either online or offline). Forty eight studies are compared in terms of recognition accuracy, classifier, activities types, and used devices. Finally, the paper concludes different challenges and issues online versus offline also using deep learning versus traditional machine learning for human activity recognition based on accelerometer sensors.

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Section: B Online Vs Offline Classificationmentioning

confidence: 99%

Section: Traditional and Deep Learning Techniquesmentioning

confidence: 99%

Section: Traditional and Deep Learning Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Survey on Human Activity Recognition based on Acceleration Data

Slim¹,

Atia²,

Elfattah³

et al. 2019

ijacsa

View full text Add to dashboard Cite

show abstract

“…For the past several years, as mobile devices (MDs) such as smartphones, handheld game consoles, and vehicle multimedia computers have become virtually ubiquitous, an increasing number of new mobile applications such as augmented reality, image processing, natural language processing, face recognition, and interactive gaming have emerged and become the focus of considerable attention [1,2]. ese types of mobile applications are typically latency-sensitive, demand intensive computation, and have high-energy consumption characteristics.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Computation Offloading and Transmit Power Allocation Scheme for Mobile Edge Computing

Zhao

et al. 2019

Mobile Information Systems

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Mobile edge computing (MEC) is considered a promising technique that prolongs battery life and enhances the computation capacity of mobile devices (MDs) by offloading computation-intensive tasks to the resource-rich cloud located at the edges of mobile networks. In this study, the problem of energy-efficient computation offloading with guaranteed performance in multiuser MEC systems was investigated. Given that MDs typically seek lower energy consumption and improve the performance of computing tasks, we provide an energy-efficient computation offloading and transmit power allocation scheme that reduces energy consumption and completion time. We formulate the energy efficiency cost minimization problem, which satisfies the completion time deadline constraint of MDs in an MEC system. In addition, the corresponding Karush–Kuhn–Tucker conditions are applied to solve the optimization problem, and a new algorithm comprising the computation offloading policy and transmission power allocation is presented. Numerical results demonstrate that our proposed scheme, with the optimal computation offloading policy and adapted transmission power for MDs, outperforms local computing and full offloading methods in terms of energy consumption and completion delay. Consequently, our proposed system could help overcome the restrictions on computation resources and battery life of mobile devices to meet the requirements of new applications.

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“…Finally, the representations and their corresponding labels are used to train a classifier to separate various activities given the differences in the sensor data. Human activity recognition (HAR) is an active research domain along AAR, and the overlap is mainly on the type of sensors being considered and the underlying ML techniques [10]. In Section 2.3, we discuss the overlap and differences in more detail.…”

Section: Introductionmentioning

confidence: 99%

Hiding in the deep : online animal activity recognition using motion sensors and machine learning

Kamminga¹

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thesis may be reproduced, stored in a retrieval system or transmitted in any form or by any means without permission of the author. Alle rechten voorbehouden. Niets uit deze uitgave mag worden vermenigvuldigd, in enige vorm of op enige wijze, zonder voorafgaande schriftelijke toestemming van de auteur. GRADUATION COMMITTEE: Chairman/secretary prof. dr. J.N. Kok Supervisor prof. dr. P.J.M. Havinga Members prof. dr. ir. P.H. Veltink prof. dr. A.K. Skidmore prof. dr. B.J.A. Krose prof. dr. K. van Laerhoven No one can pass through life, any more than he can pass through a bit of country, without leaving tracks behind, and those tracks may often be helpful to those coming after him in finding their way.

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Real-time activity recognition in mobile phones based on its accelerometer data

Cited by 10 publications

References 13 publications

Survey on Human Activity Recognition based on Acceleration Data

Survey on Human Activity Recognition based on Acceleration Data

Energy-Efficient Computation Offloading and Transmit Power Allocation Scheme for Mobile Edge Computing

Hiding in the deep : online animal activity recognition using motion sensors and machine learning

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