Performance Evaluation of Classifiers on WISDM Dataset for Human Activity Recognition

Walse, K. H.; Dharaskar, Rajiv V.; Thakare, V. M.

doi:10.1145/2905055.2905232

Cited by 27 publications

(20 citation statements)

References 6 publications

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“…In the experiment, we perform feature selection with new feature metric and build two-layer ASG models with SVM [18,34], Random Forest [5,11], KNN [12,13], and RNN [24], respectively. e recognition performances of different methods on three datasets are showed as Tables 7-9, respectively.…”

Section: Comparison Of Different Classification Methods In Impersonalmentioning

confidence: 99%

“…Hossain et al [4] analyzed different active learning strategies to scale activity recognition and proposed a dynamic k-means clustering to solve the barrier of collecting the ground truth information. Walse et al [5] presented experimental work of various classifiers on the WISDM dataset and the performance of Random Forest was the best.…”

Section: Related Workmentioning

confidence: 99%

“…e dataset was carried out by a group of 510 volunteers (390 men and 120 women). HASC-PAC2016 is targeted for basic human activity, which includes 6 activities: (1) stay, (2) walk, (3) jog, (4) skip, (5) going upstairs, and (6) going downstairs. Activity data are collected using the embedded accelerometer and gyroscope in a different smartphone.…”

Section: Datasets and Experimental Settingsmentioning

confidence: 99%

“…However, these experimental results were obtained on small-scale datasets which only contain a few people's activity data. Moreover, most research work [3][4][5] used the personal mode for classification model training and testing. e training and testing datasets in personal mode have common users, but there is no common user between train and test datasets in the impersonal mode.…”

Section: Introductionmentioning

confidence: 99%

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TASG: An Augmented Classification Method for Impersonal HAR

Zheng

Sun

et al. 2018

Mobile Information Systems

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Human activity recognition (HAR) systems are widely used in our lives, such as healthcare, security, and entertainment. Most of the activity recognition models are tested in the personal mode, and the performance is quite good. However, HAR in the impersonal mode is still a great challenge. In this paper, we propose a two-layer activity sparse grouping (TASG) model, in which the first layer clusters the activities into 2–4 groups roughly and the second layer identifies the specific type of activities. A new feature selection metric inspired by the Fisher criterion is designed to measure the importance of the features. We perform the experiment using the TASG model with SVM, KNN, Random Forest, and RNN, respectively. The experiments are tested on HAPT, MobiAct, and HASC-PAC2016 datasets. The experimental results show that the performance of standard classifiers has been improved while combining the TASG method. The features selected by the proposed metric are more effective than other FS methods.

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Section: Comparison Of Different Classification Methods In Impersonalmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Datasets and Experimental Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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TASG: An Augmented Classification Method for Impersonal HAR

Zheng

Sun

et al. 2018

Mobile Information Systems

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show abstract

“…Thus, if the machine learning model trained with only a few subjects, classifying and differentiating activities for other people may be inaccurate [3]. Previous research also neglects to select the most relevant features to enhance the efficiency and accuracy of the results of the classification [4]. This is due to some of the features might irrelevant and less meaningful to describe the activity.…”

Section: Introductionmentioning

confidence: 99%

Recognizing the Activity Daily Living (ADL) for Subject Independent

Zainudin¹,

Kee²,

Idris³

et al. 2019

IJRTE

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Recently, Human Activity Recognition (HAR) has gained meaningful information for a human being. A wearable sensor like an accelerometer, small and simple to perform, has opened the room for scientists to explore an initial understanding of ubiquitous computing. The wearable sensor has begun to receive attention among researchers in some respects to conduct their studies in a wide area of recognition of human activity. Recent ADL discusses not only simple activities but also cater to the broad categories of complex activities. However, when involving enormous numbers of a subject, the accuracy of recognition tends to reduce. Although a different subject performed the same activity, the acceleration signal acquired considerably differs. This is due to the distinct pattern of action for each subject based on various factors such as subject age, gender, emotion and personality. Thus, by enhancing the accuracy of recognition of ADL, this article proposes the framework for addressing the subject independent matter. The signal acquired from an accelerometer sensor will undergo a segmentation process to extract important features. Some of the characteristics may be meaningless in some instances to determine the class. Therefore, proposing a variety of features to select the most relevant features that can lead to accuracy above 90%. Also, this article outlined a brief empirical evaluation of previous related work. Using several machine learning algorithms, this preliminary work will be examined and analyzed.

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Daily Routine Monitoring Using Deep Learning Models

Salman

Al-Chalabi

2023

Lecture Notes in Networks and Systems

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Performance Evaluation of Classifiers on WISDM Dataset for Human Activity Recognition

Cited by 27 publications

References 6 publications

TASG: An Augmented Classification Method for Impersonal HAR

TASG: An Augmented Classification Method for Impersonal HAR

Recognizing the Activity Daily Living (ADL) for Subject Independent

Daily Routine Monitoring Using Deep Learning Models

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