Acceleration Feature Extraction of Human Body Based on Wearable Devices

Huang, Zhenzhen; Niu, Qiang; You, Ilsun; Pau, Giovanni

doi:10.3390/en14040924

Cited by 6 publications

(5 citation statements)

References 28 publications

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“…Optimal device location: One of hypotheses to test in this study is that the accuracy of a DL model to classify human activities can vary based on the SWM device's locations on the body. During human activities, different body sites produce different motion data [61], so it may be possible that its accuracy trained using motion data collected from a body site is higher than one trained using motion data from other body sites. Therefore, it is necessary to understand the influence of the device's locations to achieve accurate and reliable fall detection and classification of other activities.…”

Section: Resultsmentioning

confidence: 99%

Experimental Study: Deep Learning-Based Fall Monitoring among Older Adults with Skin-Wearable Electronics

Lee

Pokharel

Muslim

et al. 2023

Sensors

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Older adults are more vulnerable to falling due to normal changes due to aging, and their falls are a serious medical risk with high healthcare and societal costs. However, there is a lack of automatic fall detection systems for older adults. This paper reports (1) a wireless, flexible, skin-wearable electronic device for both accurate motion sensing and user comfort, and (2) a deep learning-based classification algorithm for reliable fall detection of older adults. The cost-effective skin-wearable motion monitoring device is designed and fabricated using thin copper films. It includes a six-axis motion sensor and is directly laminated on the skin without adhesives for the collection of accurate motion data. To study accurate fall detection using the proposed device, different deep learning models, body locations for the device placement, and input datasets are investigated using motion data based on various human activities. Our results indicate the optimal location to place the device is the chest, achieving accuracy of more than 98% for falls with motion data from older adults. Moreover, our results suggest a large motion dataset directly collected from older adults is essential to improve the accuracy of fall detection for the older adult population.

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Section: Resultsmentioning

confidence: 99%

Experimental Study: Deep Learning-Based Fall Monitoring among Older Adults with Skin-Wearable Electronics

Lee

Pokharel

Muslim

et al. 2023

Sensors

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“…( 1), a x (t), a y (t), a z (t) are the acceleration values in the direction of the X, Y, and Z axes of the 3D coordinate system for the current sampling timestamp, respectively. Since the human body falls or performs some large movements, the SMV will appear at a peak at some point, which is usually a larger value, while the possibility of a fall event is small if the SMV is a small value [25]. Based on this understanding, the fall detection framework proposed in this paper determines whether further inference is needed by setting an SMV threshold.…”

Section: Methodsmentioning

confidence: 99%

A Novel Fall Detection Framework Using Skip-DSCGAN Based on Inertial Sensor Data

Fang,

Pan,

et al. 2024

CMC

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With the widespread use of Internet of Things (IoT) technology in daily life and the considerable safety risks of falls for elderly individuals, research on IoT-based fall detection systems has gained much attention. This paper proposes an IoT-based spatiotemporal data processing framework based on a depthwise separable convolution generative adversarial network using skip-connection (Skip-DSCGAN) for fall detection. The method uses spatiotemporal data from accelerometers and gyroscopes in inertial sensors as input data. A semisupervised learning approach is adopted to train the model using only activities of daily living (ADL) data, which can avoid data imbalance problems. Furthermore, a quantile-based approach is employed to determine the fall threshold, which makes the fall detection framework more robust. This proposed fall detection framework is evaluated against four other generative adversarial network (GAN) models with superior anomaly detection performance using two fall public datasets (SisFall & MobiAct). The test results show that the proposed method achieves better results, reaching 96.93% and 92.75% accuracy on the above two test datasets, respectively. At the same time, the proposed method also achieves satisfactory results in terms of model size and inference delay time, making it suitable for deployment on wearable devices with limited resources. In addition, this paper also compares GAN-based semisupervised learning methods with supervised learning methods commonly used in fall detection. It clarifies the advantages of GANbased semisupervised learning methods in fall detection.

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“…In this work, we compute a total of eleven hand-crafted features, as shown in Table 2. Eight of the selected features are proven accurate in previous works, especially in the general classification of human activities [15,33,34,[73][74][75]. These features include min, max, mean, median, SD, variance, kurtosis, and RMS.…”

Section: Feature Extractionmentioning

confidence: 99%

“…We believe that combining similarities measure and personalization can form a personalized model which has a better performance than the cross-subject model and consumes the test subject's data less than the subject-specific models. Moreover, we further enhance the models' performance by combining hand-crafted features with some of the suggested ones from the previous studies [15,33,34]. The hand-crafted features are often specific toward the activities of interest, which the models have to detect.…”

Section: Introductionmentioning

confidence: 99%

Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables’ Data from the Crowd

Elshafei

Costa

Shihab

2022

Sensors

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Nowadays, wearables-based Human Activity Recognition (HAR) systems represent a modern, robust, and lightweight solution to monitor athlete performance. However, user data variability is a problem that may hinder the performance of HAR systems, especially the cross-subject HAR models. Such a problem may have a lesser effect on the subject-specific model because it is a tailored model that serves a specific user; hence, data variability is usually low, and performance is often high. However, such a performance comes with a high cost in data collection and processing per user. Therefore, in this work, we present a personalized model that achieves higher performance than the cross-subject model while maintaining a lower data cost than the subject-specific model. Our personalization approach sources data from the crowd based on similarity scores computed between the test subject and the individuals in the crowd. Our dataset consists of 3750 concentration curl repetitions from 25 volunteers with ages and BMI ranging between 20–46 and 24–46, respectively. We compute 11 hand-crafted features and train 2 personalized AdaBoost models, Decision Tree (AdaBoost-DT) and Artificial Neural Networks (AdaBoost-ANN), using data from whom the test subject shares similar physical and single traits. Our findings show that the AdaBoost-DT model outperforms the cross-subject-DT model by 5.89%, while the AdaBoost-ANN model outperforms the cross-subject-ANN model by 3.38%. On the other hand, at 50.0% less of the test subject’s data consumption, our AdaBoost-DT model outperforms the subject-specific-DT model by 16%, while the AdaBoost-ANN model outperforms the subject-specific-ANN model by 10.33%. Yet, the subject-specific models achieve the best performances at 100% of the test subjects’ data consumption.

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Acceleration Feature Extraction of Human Body Based on Wearable Devices

Cited by 6 publications

References 28 publications

Experimental Study: Deep Learning-Based Fall Monitoring among Older Adults with Skin-Wearable Electronics

Experimental Study: Deep Learning-Based Fall Monitoring among Older Adults with Skin-Wearable Electronics

A Novel Fall Detection Framework Using Skip-DSCGAN Based on Inertial Sensor Data

Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables’ Data from the Crowd

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