Activity recognition using accelerometer sensor and machine learning classifiers

Sukor, Abdul Syafiq Abdull; Zakaria, A.; Rahim, Norasmadi Abdul

doi:10.1109/cspa.2018.8368718

Cited by 62 publications

(35 citation statements)

References 20 publications

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“…Besides our approach, authors in [52] used principal component analysis (PCA) for feature selection from fall detection datasets. This dataset contains 3 accelerometer Axis as features.…”

Section: Axis Analysis Of Activitiesmentioning

confidence: 99%

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Javed

Sarwar

Khan

et al. 2020

Sensors

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Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.2 of 18 emotional issues, and depression as well. Physical fitness can be tracked and analyzed by monitoring daily life physical activities.Physical activity recognition was initiated back in 2004 using on-body sensors. Researchers in [2] used the accelerometer's annotated data to recognize the physical activities. They made an Android-based system that collects raw data from the accelerometer and applied machine learning algorithms to predict physical activities. Authors in [3] recognized six basic activities, i.e., walking, jogging, sitting, standing, upstairs and downstairs. Authors in [4] used on-body sensors for activity recognition but found that it is very difficult to carry the sensors all the time. Many authors suggested that a smartphone is a non-obtrusive option for activity recognition [3,[5][6][7][8][9].The smartphone is playing a vital role in modern life. It provides services and applications such as health monitoring, early-stage disease detection, sports analysis, fitness tracking, and behavior analysis. Android-based smartphones have a built-in motion sensor that provides accurate and precise acceleration readings against physical activities. In early conditions, dedicated sensors were used for activity recognition. There exist several techniques for physical activity recognition such as on-body obtrusive and non-obtrusive sensors [10][11][12][13]. Non-obtrusive sensors are used in smart homes and smartphones. In smart homes, different motion and door sensors are installed at different locations and the primary objective is to recognize...

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“…Besides our approach, authors in [52] used principal component analysis (PCA) for feature selection from fall detection datasets. This dataset contains 3 accelerometer Axis as features.…”

Section: Axis Analysis Of Activitiesmentioning

confidence: 99%

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Javed

Sarwar

Khan

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Accuracy may produce misleading results if the data in each class of dataset is imbalanced, therefore, recall and precision is calculated to validate performance. FUSION METHODS [5] PCA [7] CNN+ TFFT [8] DBN [9] MC-SVM [11] LHN [13] Accuracy…”

Section: Resultsmentioning

confidence: 99%

A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices

Agarwal

Alam

2020

Procedia Computer Science

111

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Human Activity Recognition (HAR) using wearable and mobile sensors has gained momentum in last few years, in various fields, such as, healthcare, surveillance, education, entertainment. Nowadays, Edge Computing has emerged to reduce communication latency and network traffic. Edge devices are resource constrained devices and cannot support high computation.In literature, various models have been developed for HAR. In recent years, deep learning algorithms have shown high performance in HAR, but these algorithms require lot of computation making them inefficient to be deployed on edge devices. This paper, proposes a Lightweight Deep Learning Model for HAR requiring less computational power, making it suitable to be deployed on edge devices. The performance of proposed model is tested on the participant's six daily activities data. Results show that the proposed model outperforms many of the existing machine learning and deep learning techniques.

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“…Hsieh et al [19] proposed a fall detection algorithm that utilises both machine learning and threshold-based techniques for detection of falls from accelerometer signals with high accuracy, above 98%. Sukor et al [51] leveraged time and frequency space features including energy and power spectrum of accelerometer signals for fall detection. Principal Component Analysis (PCA) was performed on feature space to select the principal components and various machine learning classifiers including DT and SVM were used for fall detection.…”

Section: Related Workmentioning

confidence: 99%

A Novel Functional Link Network Stacking Ensemble with Fractal Features for Multichannel Fall Detection

2020

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Falls are a major health concern and result in high morbidity and mortality rates in older adults with high costs to health services. Automatic fall classification and detection systems can provide early detection of falls and timely medical aid. This paper proposes a novel Random Vector Functional Link (RVFL) stacking ensemble classifier with fractal features for classification of falls. The fractal Hurst exponent is used as a representative of fractal dimensionality for capturing irregularity of accelerometer signals for falls and other activities of daily life. The generalised Hurst exponents along with wavelet transform coefficients are leveraged as input feature space for a novel stacking ensemble of RVFLs composed with an RVFL neural network meta-learner. Novel fast selection criteria are presented for base classifiers founded on the proposed diversity indicator, obtained from the overall performance values during the training phase. The proposed features and the stacking ensemble provide the highest classification accuracy of 95.71% compared with other machine learning techniques, such as Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine. The proposed ensemble classifier is 2.3× faster than a single Decision Tree and achieves the highest speedup in training time of 317.7× and 198.56× compared with a highly optimised ANN and RF ensemble, respectively. The significant improvements in training times of the order of 100× and high accuracy demonstrate that the proposed RVFL ensemble is a prime candidate for real-time, embedded wearable device–based fall detection systems.

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Activity recognition using accelerometer sensor and machine learning classifiers

Cited by 62 publications

References 20 publications

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices

A Novel Functional Link Network Stacking Ensemble with Fractal Features for Multichannel Fall Detection

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