A Multi-featured Approach for Wearable Sensor-Based Human Activity Recognition

Yazdansepas, Delaram; Niazi, Anzah H.; Maier, Frederick; Ramaswamy, Lakshmish; Rasheed, Khaled; Buman, Matthew P.

doi:10.1109/ichi.2016.81

Cited by 30 publications

(22 citation statements)

References 18 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%

“…In the study of human activity recognition, some activities are easy to be classified, such as walking and standing, but some are confusing, such as walking upstairs and walking downstairs [11]. At present, most current work [12][13][14] does not consider differences in features among activities. ey always used the common features for all activities, which results in some awkward features, which are useless for distinguishing some activities although they have discriminative characteristics in other activities.…”

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: Introductionmentioning

confidence: 99%

TASG: An Augmented Classification Method for Impersonal HAR

Zheng

Sun

et al. 2018

Mobile Information Systems

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“…Clustering is one of the most used techniques in the recognition of Activities of Daily Living [2][3][4][5][6][7][8][9][10][11]. Clustering analysis is the formal study of algorithms and methods to group objects according to measurements, perceived attributes, intrinsic features or likelihoods [12].…”

Section: Clusteringmentioning

confidence: 99%

Clustering Techniques for Recognition of ADL Performed with Upper Limbs

Parra-Sánchez¹,

Gómez-González²,

Quintero-Ortega³

et al. 2019

RCS

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Activities of Daily Living (ADL) have a background of selfsufficiency and survival function. Upper limbs participate actively in many ADL; particularly, activities related to feeding, communication, and grooming. The performance of such activities is a parameter of independence. Various researchers have studied ADL in a free-living environment by using inertial sensors. However, functional-activity recognition with low recognition rate is a persistent result. This work proposes the use of well-known clustering techniques for ADL recognition by using as feeding signal the vertical trajectory of wrist relative to the shoulder.

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“…However, these systems are highly dependent on the on-body location of the smartphone. On the other hand, accelerometer-based HAR architectures present robust performance regardless the sensor location and allow to distinguish among a wider range of activities compared to smartphone-based systems [26,[33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Physical Workload Tracking Using Human Activity Recognition with Wearable Devices

Manjarrés

Narváez

Gasser

et al. 2019

Sensors

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In this work, authors address workload computation combining human activity recognition and heart rate measurements to establish a scalable framework for health at work and fitness-related applications. The proposed architecture consists of two wearable sensors: one for motion, and another for heart rate. The system employs machine learning algorithms to determine the activity performed by a user, and takes a concept from ergonomics, the Frimat's score, to compute the corresponding physical workload from measured heart rate values providing in addition a qualitative description of the workload. A random forest activity classifier is trained and validated with data from nine subjects, achieving an accuracy of 97.5%. Then, tests with 20 subjects show the reliability of the activity classifier, which keeps an accuracy up to 92% during real-time testing. Additionally, a single-subject twenty-day physical workload tracking case study evinces the system capabilities to detect body adaptation to a custom exercise routine. The proposed system enables remote and multi-user workload monitoring, which facilitates the job for experts in ergonomics and workplace health.

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A Multi-featured Approach for Wearable Sensor-Based Human Activity Recognition

Cited by 30 publications

References 18 publications

TASG: An Augmented Classification Method for Impersonal HAR

TASG: An Augmented Classification Method for Impersonal HAR

Clustering Techniques for Recognition of ADL Performed with Upper Limbs

Physical Workload Tracking Using Human Activity Recognition with Wearable Devices

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