Convolutional Neural Network-Based Human Activity Recognition for Edge Fitness and Context-Aware Health Monitoring Devices

Phukan, Nabasmita; Mohine, Shailesh; Mondal, Achinta; Manikandan, M. Sabarimalai; Pachori, Ram Bilas

doi:10.1109/jsen.2022.3206916

Cited by 29 publications

(4 citation statements)

References 46 publications

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“…ViT has to be pretrained using very large data sets since the representation of longer tokens requires a greater amount of computer resources. We need to perform the following to fix the issue of ViT pre-training time: In his research, Deit proposes a wide variety of instructional strategies for transformers (Agor et al 2021 ; Phukan et al 2022 ). ViT is able to do efficient pre-training on huge datasets and sample sets because to these approaches.…”

Section: Review Of Existing Multiorgan Disease Detection Techniquesmentioning

confidence: 99%

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

Ajani¹,

Mulla²,

Limkar³

et al. 2023

Soft Comput

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Progressive organ-level disorders in the human body are often correlated with diseases in other body parts. For instance, liver diseases can be linked with heart issues, while cancers can be linked with brain diseases (or psychological conditions). Defining such correlations is a complex task, and existing deep learning models that perform this task either showcase lower accuracy or are non-comprehensive when applied to real-time scenarios. To overcome these issues, this text proposes design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. The proposed model initially collects temporal and spatial data scans for different body parts and uses a multidomain feature extraction engine to convert these scans into vector sets. These vectors are processed by a Bacterial Foraging Optimizer (BFO), which assists in identification of highly variant feature sets, which are individually classified into different disease categories. A fusion of Inception Net, XCeption Net, and GoogLeNet Models is used to perform these classifications. The classified categories are linked with other disease types via temporal analysis of blood reports. The temporal analysis engine uses Modified Analytical Hierarchical Processing (MAHP) Model for calculating inter-organ disease dependency probabilities. Based on these probabilities, the model is able to generate a patient-level correlation map, which can be used by clinical experts to suggest remedial treatments, due to which the model was able to identify correlations between brain disorders and kidneys, heart diseases and lungs, heart diseases and liver, brain diseases and different types of cancers with high efficiency when evaluated under clinical scenarios. When validated on MITBIH, DEAP, CT Kidney, RIDER, and PLCO data samples, it was observed that the proposed model was capable of improving accuracy of correlation by 8.5%, while improving precision and recall by 3.2% when compared with existing correlation models under similar clinical scenarios.

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Section: Review Of Existing Multiorgan Disease Detection Techniquesmentioning

confidence: 99%

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

Ajani¹,

Mulla²,

Limkar³

et al. 2023

Soft Comput

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“…CNN-LSTM models showed improved performance and were used by other recent researchers [37,38]. The real-time feasibility of the IMU sensor and CNN-based PAR was introduced in [39]. A soft-voting and selflearning-based PAR method was proposed in [40], where the researcher enhanced accuracy by employing multiple machine learning models.…”

Section: Previous Studies and Our Contributionmentioning

confidence: 99%

A CNN Model for Physical Activity Recognition and Energy Expenditure Estimation from an Eyeglass-Mounted Wearable Sensor

Hossain,

LaMunion,

Crouter

et al. 2024

Sensors

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Metabolic syndrome poses a significant health challenge worldwide, prompting the need for comprehensive strategies integrating physical activity monitoring and energy expenditure. Wearable sensor devices have been used both for energy intake and energy expenditure (EE) estimation. Traditionally, sensors are attached to the hip or wrist. The primary aim of this research is to investigate the use of an eyeglass-mounted wearable energy intake sensor (Automatic Ingestion Monitor v2, AIM-2) for simultaneous recognition of physical activity (PAR) and estimation of steady-state EE as compared to a traditional hip-worn device. Study data were collected from six participants performing six structured activities, with the reference EE measured using indirect calorimetry (COSMED K5) and reported as metabolic equivalents of tasks (METs). Next, a novel deep convolutional neural network-based multitasking model (Multitasking-CNN) was developed for PAR and EE estimation. The Multitasking-CNN was trained with a two-step progressive training approach for higher accuracy, where in the first step the model for PAR was trained, and in the second step the model was fine-tuned for EE estimation. Finally, the performance of Multitasking-CNN on AIM-2 attached to eyeglasses was compared to the ActiGraph GT9X (AG) attached to the right hip. On the AIM-2 data, Multitasking-CNN achieved a maximum of 95% testing accuracy of PAR, a minimum of 0.59 METs mean square error (MSE), and 11% mean absolute percentage error (MAPE) in EE estimation. Conversely, on AG data, the Multitasking-CNN model achieved a maximum of 82% testing accuracy in PAR, a minimum of 0.73 METs MSE, and 13% MAPE in EE estimation. These results suggest the feasibility of using an eyeglass-mounted sensor for both PAR and EE estimation.

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“…The authors explored the impact of hyperparameters and evaluated the performance of CNN-based methods using acceleration signals from the University of California, Irvine (UCI) HAR benchmark database. They emphasized the significance of selecting appropriate activation functions and segment sizes to optimize accuracy and demonstrated the real-time feasibility of their approach using the Raspberry Pi 4 [16].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Sensor-Embedded Neural Network for Human Activity Recognition

Shakerian,

Douet,

Shoaraye Nejati

et al. 2023

Sensors

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This article introduces a novel approach to human activity recognition (HAR) by presenting a sensor that utilizes a real-time embedded neural network. The sensor incorporates a low-cost microcontroller and an inertial measurement unit (IMU), which is affixed to the subject’s chest to capture their movements. Through the implementation of a convolutional neural network (CNN) on the microcontroller, the sensor is capable of detecting and predicting the wearer’s activities in real-time, eliminating the need for external processing devices. The article provides a comprehensive description of the sensor and the methodology employed to achieve real-time prediction of subject behaviors. Experimental results demonstrate the accuracy and high inference performance of the proposed solution for real-time embedded activity recognition.

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Convolutional Neural Network-Based Human Activity Recognition for Edge Fitness and Context-Aware Health Monitoring Devices

Cited by 29 publications

References 46 publications

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

A CNN Model for Physical Activity Recognition and Energy Expenditure Estimation from an Eyeglass-Mounted Wearable Sensor

Real-Time Sensor-Embedded Neural Network for Human Activity Recognition

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