Subject variability in sensor-based activity recognition

Jimale, Ali; Noor, Mohd Halim Mohd

doi:10.1007/s12652-021-03465-6

Cited by 6 publications

(6 citation statements)

References 37 publications

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“…The elderly dataset was sampled in fixed-width sliding windows of 2 seconds and 50% overlap (100 readings/window). A full description of the used dataset can be found in section 4.1.1 of [48].…”

Section: Methodsmentioning

confidence: 99%

“…The third evaluation technique, usability evaluation, aims to investigate the quality of the synthetic data generated by the proposed architecture in improving the performance of activity recognition classification models. First, the synthetic data generated by both FCGAN architecture and Unified GAN is preprocessed to perform four experiments on it together with the real data using the best-performing deep learning classifiers by Jimale & Mohd Noor [48]. These experiments are experiments on 70% real data and 30% synthetic data, experiments on 50% real data and 50% synthetic data, experiments on 30% real data and 70% synthetic data, and experiments on 100% synthetic data.…”

Section: Usability Evaluationmentioning

confidence: 99%

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Fully Connected Generative Adversarial Network for Human Activity Recognition

Jimale

Noor

2022

IEEE Access

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Conditional Generative Adversarial Networks (CGAN) have shown great promise in generating synthetic data for sensor-based activity recognition. However, one key issue concerning existing CGAN is the design of the network architecture that affects sample quality. This study proposes an effective CGAN architecture that synthesizes higher quality samples than state-of-the-art CGAN architectures. This is achieved by combining convolutional layers with multiple fully connected networks in the generator's input and discriminator's output of the CGAN. We show the effectiveness of the proposed approach using elderly data for sensor-based activity recognition. Visual evaluation, similarity measure, and usability evaluation is used to assess the quality of generated samples by the proposed approach and validate its performance in activity recognition. In comparison to the state-of-the-art CGAN, the visual evaluation and similarity measure demonstrate that the proposed models' synthetic data more accurately represents actual data and creates more variations in each synthetic data than the state-of-the-art approach respectively. The experimental stages of the usability evaluation, on the other hand, show a performance gain of 2.5%, 2.5%, 3.1%, and 4.4% over the state-of-the-art CGAN when using synthetic samples by the proposed architecture.

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Section: Methodsmentioning

confidence: 99%

Section: Usability Evaluationmentioning

confidence: 99%

Fully Connected Generative Adversarial Network for Human Activity Recognition

Jimale

Noor

2022

IEEE Access

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“…Various academic studies on classifying daily physical behavior in older adults have made significant contributions to the field [28], [29]. Specific studies focusing on Classifying Daily Physical Behavior in Older Adults have contributed significantly to the field.…”

Section: Related Workmentioning

confidence: 99%

Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for Fine- Grained Heterogeneity Activity Recognition

Abbas,

Sampedro,

Alsubai

et al. 2024

IEEE Access

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This research explores how technology can be used to understand and identify activities among elderly individuals. By utilizing HAR70+ data and applying methods like Active Learning (AL), Machine Learning (ML), and Deep Learning (DL), this research aims to predict various activities performed by older adults. Moreover, the study leverages the HAR70+ dataset, providing insight into the daily activities of older individuals and AL-based ML and DL techniques to construct predictive models for these activities. The research experiments are presented systematically, summarizing the outcomes of various machinelearning models across three iterative experiments. This research explored a diverse array of ML algorithms, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGB) and DL methods such as Deep Neural Networks (DNN) and Long Short-Term Memory networks (LSTM) for experimentation. This research trained models on 7 activities: walking, shuffling, climbing stairs (up and down), standing, sitting, and lying down, and 4 activities separately: standing, sitting, walking, and lying down, using the same method. Results reveal that LSTM achieved the best accuracy of 0.98 for 7 activities and 0.95 using RF on 4 actives, showing the potential of DL and ML techniques, particularly when integrated with AL, to enhance activity recognition rate, patient care, optimize medication strategies and improve the well-being of elderly individuals.

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“…As a result, wearable sensors are better for recognizing human activities 5,6 . However, the recognition task is difficult due to the vast number of sensor modalities, noisy data, variances in the spatial and temporal dimensions of the feature space between people, 7 and also, the variability when a subject or different subjects perform the same task at various times, among other factors 8 . Examples of wearable sensors include accelerometers, gyroscopes, magnetometers, and others.…”

Section: Introductionmentioning

confidence: 99%

“…5,6 However, the recognition task is difficult due to the vast number of sensor modalities, noisy data, variances in the spatial and temporal dimensions of the feature space between people, 7 and also, the variability when a subject or different subjects perform the same task at various times, among other factors. 8 Examples of wearable sensors include accelerometers, gyroscopes, magnetometers, and others. Various machine learning models have been proposed to recognize the activities collected using these sensors; an example of such can be seen in Sani et al 9 where the authors used K-Nearest Neighbor for classification.…”

Section: Introductionmentioning

confidence: 99%

A lightweight deep learning with feature weighting for activity recognition

Ige

Noor

2022

Computational Intelligence

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With the development of deep learning, numerous models have been proposed for human activity recognition to achieve state-of-the-art recognition on wearable sensor data. Despite the improved accuracy achieved by previous deep learning models, activity recognition remains a challenge. This challenge is often attributed to the complexity of some specific activity patterns. Existing deep learning models proposed to address this have often recorded high overall recognition accuracy, while low recall and precision are often recorded on some individual activities due to the complexity of their patterns. Some existing models that have focused on tackling these issues are always bulky and complex. Since most embedded systems have resource constraints in terms of their processor, memory and battery capacity, it is paramount to propose efficient lightweight activity recognition models that require limited resources consumption, and still capable of achieving state-of-the-art recognition of activities, with high individual recall and precision. This research proposes a high performance, low footprint deep learning model with a squeeze and excitation block to address this challenge. The squeeze and excitation block consist of a global average-pooling layer and two fully connected layers, which were placed to extract the flattened features in the model, with best-fit reduction ratios in the squeeze and excitation block.

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Subject variability in sensor-based activity recognition

Cited by 6 publications

References 37 publications

Fully Connected Generative Adversarial Network for Human Activity Recognition

Fully Connected Generative Adversarial Network for Human Activity Recognition

Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for Fine- Grained Heterogeneity Activity Recognition

A lightweight deep learning with feature weighting for activity recognition

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