The vital signs can vary significantly depending on the daily physical activities, which may not be due to defects of the organs. Under remote human health-monitoring applications, for reliable disease diagnosis, recorded biomedical signals such as an electrocardiogram (ECG) and a photoplethysmogram (PPG) must be indexed with physical activity information, as it is unknown to the physicians and also to the computer-aided diagnostic system. Since deep-learning (DL) networks were explored for various vital signs extraction and disease classification, we present an effective convolutional neural network (CNN)-based human activity recognition (HAR) method by exploring suitable hyperparameters. CNN-based methods are evaluated using the acceleration signals taken from the standard HAR benchmark database, University of California, Irvine (UCI) with accuracy (ACC), model size (in kB), and processing time (PT), and also implemented on the Raspberry Pi 4 (R-Pi-4) to study real-time feasibility. Evaluation results showed that higher HAR accuracy can be achieved with the activation function of the exponential linear unit (ELU) for the 2-layer CNN with a segment size of 2.5 s (ACC of 89.05% and PT of 0.142 ms), the 4-layer CNN with a segment size of 1 s (ACC of 91.66% and PT of 0.541 ms), and the 6-layer CNN with a segment size of 2 s (ACC of 91.18% and PT of 1.672 ms). Results demonstrated that the selection of an optimal number of layers and hyperparameters plays a major role in achieving higher accuracy with lower computational time on both personal computer central processing unit (PC-CPU) and R-Pi computing platforms, which were not addressed in past studies. 

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

Abstract Pain is a complex and personal experience that presents diverse measurement challenges. Different sensing technologies can be used as a surrogate measure of pain to overcome these challenges. The objective of this review is to summarise and synthesise the published literature to: (a) identify relevant non-invasive physiological sensing technologies that can be used for the assessment of human pain, (b) describe the analytical tools used in artificial intelligence (AI) to decode pain data collected from sensing technologies, and (c) describe the main implications in the application of these technologies. A literature search was conducted in July 2022 to query PubMed, Web of Sciences, and Scopus. Papers published between January 2013 and July 2022 are considered. Forty-eight studies are included in this literature review. Two main sensing technologies (neurological and physiological) are identified in the literature. The sensing technologies and their modality (unimodal or multimodal) are presented. The literature provided numerous examples of how different analytical tools in AI have been applied to decode pain. This review identifies different non-invasive sensing technologies, their analytical tools, and the implications for their use. There are significant opportunities to leverage multimodal sensing and deep learning to improve accuracy of pain monitoring systems. This review also identifies the need for analyses and datasets that explore the inclusion of neural and physiological information together. Finally, challenges and opportunities for designing better systems for pain assessment are also presented. 

/pdf/a-systematic-review-of-neurophysiological-sensing-for-the-3m2uofit.pdf

A systematic review of neurophysiological sensing for the assessment of acute pain

Pain assessment is a challenging task encountered by clinicians. In clinical settings, patients’ self-report is considered the gold standard in pain assessment. However, patients who are unable to self-report pain are at a higher risk of undiagnosed pain. In the present study, we explore the use of multiple sensing technologies to monitor physiological changes that can be used as a proxy for objective measurement of acute pain. Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) signals were collected from 22 participants under two pain intensities (low and high) and on two different anatomical locations (forearm and hand). Three machine learning models were implemented, including support vector machines (SVM), decision trees (DT), and linear discriminant analysis (LDA) for the identification of pain. Various pain scenarios were investigated, identification of pain (no pain, pain), multiclass (no pain, low pain, high pain), and identification of pain location (forearm, hand). Reference classification results from individual sensors and from all sensors together were obtained. After feature selection, results showed that EDA was the most informative sensor in the three pain conditions, 93.2±8% in identification of pain, 68.9±10% in the multiclass problem, and 56.0±8% for the identification of pain location. These results identify EDA as the superior sensor in our experimental conditions. Future work is required to validate the obtained features to improve its feasibility in more realistic scenarios. Finally, this study proposes EDA as a candidate to design a tool that can assist clinicians in the assessment of acute pain of nonverbal patients.

/pdf/multimodal-physiological-sensing-for-the-assessment-of-acute-1qcyubei.pdf

Multimodal physiological sensing for the assessment of acute pain

HAR has attained major attention because of its significant use in real-life scenarios like activity and fitness monitoring, rehabilitation, gaming, prosthetic limbs, healthcare, smart surveillance systems, etc. HAR systems provide ways for monitoring human behaviors and detecting body movements and various activities by using sensor data. The collection of sensors available in the mobile and other wearable devices has made most of these HAR applications easily possible. Moreover, Deep Learning (DL) has further accelerated the research on HAR using the data obtained via wearable devices. In this paper, we have discussed the overview of HAR, its applications, and popular benchmark datasets available publicly. Further, we discussed various DL techniques applied for HAR applications. We have also presented the challenges associated with the field and the future directions for performing more vital research in HAR. 

A Survey on Human Activity Recognition Using Deep Learning Techniques and Wearable Sensor Data

With the popularity and development of Internet of Things (IoT) technology, human activity recognition using IoT devices such as wearable sensors can be implemented for various applications. Due to the complexity of activity recognition, multiple homogeneous or heterogeneous sensors are used to obtain excessive information in most wearable activity recognition systems. However, the increased number of sensors and the way of multichannel signal data bring huge challenges to human activity recognition tasks. How to select suitable sensor channels to balance the computational complexity and recognition accuracy has become a major issue. In this article, we extend the sparse group Lasso mechanism to human activity recognition tasks and propose a hybrid attention-based multi-sensor pruning and feature selection deep neural network, called HAP-DNN. This architecture is able to further perform feature selection on the basis of sensor pruning. HAP-DNN consists of three detachable modules: 1) a feature compression & reconstruction module for sensor feature information fusion and restoration; 2) a feature weight calculation module for calculating sensor channel weights and feature weights; and 3) a learning module for classification, which can be regarded as a filter feature selection method. Four public activity recognition data sets are used to verify our proposed architecture, and the experimental results show that HAP-DNN achieves the best classification performance with the least number of retained feature channels. 

A Hybrid Attention-Based Deep Neural Network for Simultaneous Multi-Sensor Pruning and Human Activity Recognition

This paper compares the three levels of data fusion with the goal of determining the optimal level of data fusion for multi-sensor human activity data. Using the data processing pipeline, gyroscope and accelerometer data was fused at the sensor-level, feature-level and decision-level. For each level of data fusion four different techniques were used with varying levels of success. This analysis was performed on four human activity publicly-available datasets along with four well-known machine learning classifiers to validate the results. The decision-level fusion (Acc = 0.7443±0.0850) outperformed the other two levels of fusion in regards to accuracy, sensor level (Acc = 0.5934 ± 0.1110) and feature level (Acc = 0.6742 ± 0.0053), but, the processing time and computational power required for training and classification were far greater than practical for a HAR system. However, Kalman filter appear to be the more efficient method, since it exhibited both good accuracy (Acc = 0.7536 ± 0.1566) and short processing time (time = 61.71ms ± 63.85); properties that play a large role in real-time applications using wearable devices. The results of this study also serve as baseline information in the HAR literature to compare future methods of data fusion.

Human Activity Recognition With Accelerometer and Gyroscope: A Data Fusion Approach

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Human Activity Recognition With Accelerometer and Gyroscope: A Data Fusion Approach