Multi-Label Classification for Physical Activity Recognition From Various Accelerometer Sensor Positions

Mohamed, Raihani; Zainudin, Muhammad Noorazlan Shah; Sulaiman, Md. Nasir; Perumal, Thinagaran; Mustapha, Norwati

doi:10.32890/jict2018.17.2.3

Cited by 14 publications

(14 citation statements)

References 42 publications

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“…A vast number of datasets for HAR [39] are available for use (Table 1) such as: HAR [19,33,53,54], WISDM [53,55,56,57], UCI HAR [35,55,58], USCHAD [19], PAMAP2 [19,37,57], OPPORTUNITY [4,35,37], UniMiB-SHAR [4], MSR Action 3D [59], RGBD-HuDaAct [59], MSR Daily Activity 3D [59], MHEALTH [60], WHARF [22], KEH [61], etc. Determining which dataset to use in a HAR application and which techniques are the most appropriate for the HAR stages in a specific context is not a trivial task at all [39].…”

Section: Impact Of Human Activity Recognition (Har) Stages On Enermentioning

confidence: 99%

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Dinarevic

Husić

Baraković

2019

Sensors

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Human activity recognition (HAR) is a classification process that is used for recognizing human motions. A comprehensive review of currently considered approaches in each stage of HAR, as well as the influence of each HAR stage on energy consumption and latency is presented in this paper. It highlights various methods for the optimization of energy consumption and latency in each stage of HAR that has been used in literature and was analyzed in order to provide direction for the implementation of HAR in health and wellbeing applications. This paper analyses if and how each stage of the HAR process affects energy consumption and latency. It shows that data collection and filtering and data segmentation and classification stand out as key stages in achieving a balance between energy consumption and latency. Since latency is only critical for real-time HAR applications, the energy consumption of sensors and devices stands out as a key challenge for HAR implementation in health and wellbeing applications. Most of the approaches in overcoming challenges related to HAR implementation take place in the data collection, filtering and classification stages, while the data segmentation stage needs further exploration. Finally, this paper recommends a balance between energy consumption and latency for HAR in health and wellbeing applications, which takes into account the context and health of the target population.

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Section: Impact Of Human Activity Recognition (Har) Stages On Enermentioning

confidence: 99%

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Dinarevic

Husić

Baraković

2019

Sensors

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“…These activities are classified based on feature extraction schemes that are broadly categorized as time and frequency domains. In [19,20,21,22,23,24,25] researchers have implemented the time domain and frequency domain feature extraction as a combined approach. Other researchers in [26,27,28,29,30,31] have used feature extraction in the time domain only, whilst, in [32] researchers applied the frequency domain and time-frequency domain.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Out of these studies, Saha et al [19] found that the ensemble classifier performs best, with an overall accuracy rate of 94% using accelerometer and gyroscope sensor data. In the research carried out by Mohamed et al [20], a combination of accelerometer data from the arm, belt and pocket analysed using rotation forest with the base learner C4.5, was found to provide the best overall classification accuracy rate of 98.9% [20]. Researchers in [21,32], and [23,24,25] have analyzed the same dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors

Rahim

Elamvazuthi

Izhar

et al. 2018

Sensors

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Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier.

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“…The latter requires extra work but often preferred by most researchers. On the other hand, the former is much easier whereby pre-processing on data is often executed prior to complex analysis (Mohamed et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Variable Extractions With Lda for Classification of Mixed Variables, and Applications to Medical Data

Hamid

Mahat

Ibrahim

2021

Journal of Information and Communication Technology

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The strategy surrounding the extraction of a number of mixed variables is examined in this paper in building a model for Linear Discriminant Analysis (LDA). Two methods for extracting crucial variables from a dataset with categorical and continuous variables were employed, namely, multiple correspondence analysis (MCA) and principal component analysis (PCA). However, in this case, direct use of either MCA or PCA on mixed variables is impossible due to restrictions on the structure of data that each method could handle. Therefore, this paper executes some adjustments including a strategy for managing mixed variables so that those mixed variables are equivalent in values. With this, both MCA and PCA can be performed on mixed variables simultaneously. The variables following this strategy of extraction were then utilised in the construction of the LDA model before applying them to classify objects going forward. The suggested models, using three real sets of medical data were then tested, where the results indicated that using a combination of the two methods of MCA and PCA for extraction and LDA could reduce the model’s size, having a positive effect on classifying and better performance of the model since it leads towards minimising the leave-one-out error rate. Accordingly, the models proposed in this paper, including the strategy that was adapted was successful in presenting good results over the full LDA model. Regarding the indicators that were used to extract and to retain the variables in the model, cumulative variance explained (CVE), eigenvalue, and a non-significant shift in the CVE (constant change), could be considered a useful reference or guideline for practitioners experiencing similar issues in future.

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Multi-Label Classification for Physical Activity Recognition From Various Accelerometer Sensor Positions

Cited by 14 publications

References 42 publications

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors

Adaptive Variable Extractions With Lda for Classification of Mixed Variables, and Applications to Medical Data

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