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Human activity recognition (HAR) employs a broad range of sensors that generate massive volumes of data. Traditional server-based and cloud computing methods require all sensor data to be sent to servers or clouds for processing, which leads to high latency and bandwidth costs. The long-term data transfer between servers and sensors maximizes the cost of latency and bandwidth. Real-time processing is, nevertheless, highly required for human action identification. By bringing processing and quick data storage to the sensors instead of depending on a central database, edge computing is rapidly emerging as a solution to this issue. Artificial intelligence is responsible for most HAR, which demands a lot of processing power and calculation. Artificial intelligence (AI) needs more computation which is not allowed by edge computing. So Edge intelligence, which allows AI to operate at the network edge for actual-time applications, has been made possible by the advent of binarized neural networks. To provide less latency and less memory for human activity identification at the edge network, we construct a hybrid deep learning-based binarized neural network (HDL-Binary Dilated DenseNet) in this research. Fractal HAR optimization algorithms could be applied to these algorithms. For example, fractal-HAR optimization techniques might be used to provide less latency and less memory human activity identification at the edge network. Using three sensors-based human activity detection datasets such as Radar HAR dataset, UCI HAR dataset and UniMib-SHAR dataset, we implemented the Hybrid Binary Dilated Dense Net. It is then assessed using four criteria. Comparatively, the Hybrid Binary Dilated DenseNet performs better with 99.6% radar HAR dataset which is highest than other models like CNN-BiLSTM and GoogLeNet.

Recognition of hand activities of daily living (hand-ADL) is useful in the areas of human–computer interactions, lifelogging, and healthcare applications. However, developing a reliable human activity recognition (HAR) system for hand-ADL with only a single wearable sensor is still a challenge due to hand movements that are typically transient and sporadic. Approaches based on deep learning methodologies to reduce noise and extract relevant features directly from raw data are becoming more promising for implementing such HAR systems. In this work, we present an ARMA-based deep autoencoder and a deep recurrent network (RNN) using Gated Recurrent Unit (GRU) for recognition of hand-ADL using signals from a single IMU wearable sensor. The integrated ARMA-based autoencoder denoises raw time-series signals of hand activities, such that better representation of human hand activities can be made. Then, our deep RNN-GRU recognizes seven hand-ADL based upon the output of the autoencoder: namely, Open Door, Close Door, Open Refrigerator, Close Refrigerator, Open Drawer, Close Drawer, and Drink from Cup. The proposed methodology using RNN-GRU with autoencoder achieves a mean accuracy of 84.94% and F1-score of 83.05% outperforming conventional classifiers such as RNN-LSTM, BRNN-LSTM, CNN, and Hybrid-RNNs by 4–10% higher in both accuracy and F1-score. The experimental results also showed the use of the autoencoder improves both the accuracy and F1-score of each conventional classifier by 12.8% in RNN-LSTM, 4.37% in BRNN-LSTM, 15.45% CNN, 14.6% Hybrid RNN, and 12.4% for the proposed RNN-GRU.

The proper management of patient data and their accessibility are still remaining issues that prevent the full deployment and usage of pervasive healthcare applications. This paper presents an integrated health monitoring system based on mobile pervasive technologies. The system utilizes Cloud Computing for providing robust and scalable resources for sensor data acquisition, management and communication with external applications like health information systems. A prototype has been developed using both mobile and wearable sensors for demonstrating the usability of the proposed platform. Initial results regarding the performance of the system, the efficiency in data management and user acceptability have been quite promising.

The modern Internet of Things (IoT) paradigm enables creating small devices that can provide sensor, process, and connect, facilitating sensors, embedded devices, and other “things” ready for understanding the environment. Physical education and physical activity can improve both the physical and mental health of practitioners. However, there are many issues or accidents occurred to the athletes during training and tournament. In addition, lack of awareness during a match or game could lead to injuries and dropping the competition or match. This study proposes an IoT-based Wearable Intelligent Health Monitoring system (IoTW-IHMS) for the physical teaching system and athletes’ health monitoring. This study intends to collect and track data on player rates, body temperature, and response time to stimulation while using compact wearable devices and microprocessors as the main device to relay them wirelessly. Multiple sensors tracked the heart rate, current body temperature, and response time of the athlete. The simulation analysis shows that the proposed IoTW-IHMS method enhances the accuracy (93.6%), response time ratio (95.4%), sensitivity ratio (97.5%), performance ratio (91.3%), and prediction ratio (96.8%) compared to other popular methods.

The following topics are under this section:

• Officials in China make progress in development of new drugs
• China conducts its first 5G-streamed robot-assisted surgery in Shanghai, China
• First Asian Scientist to be awarded the IBRO-Kemali Prize: Dr Hu Hailan
• Wuhan Healthgen Biotechnology gains approval for OsrHSA by U.S. FDA
• B&R tumor prevention, control training base to be built in Chongqing
• China-Thailand Joint Research Institute on Medicine Launched in Bangkok
• Porous Fibres Graphene Developed for Highly Sensitive Wearable Sensors
• New Cotton Fertilization Method Developed by CAS Researchers

The market for additive manufacturing of three-dimensional (3D) printed electronics has expanded significantly in recent years, and there is a growing need for diverse printing methods in electronics for a range of applications. This review paper presents an overview of the currently available 3D printing techniques that could be employed for printing electronics, exploring the strengths and weaknesses of each method. The paper goes on further to highlight a few of the significant applications of the technology in the domain of electronics, specifically related to 3D printing of wearable sensors and oximeters, strain sensors, 5G polymer antennas, and power electronic converters. For instance, 3D-printed wearable devices with integrated force and temperature sensors mimic human skin, while 3D-printed strain sensors enhance motion tracking in wearable electronics. The review also covers case studies, such as Nano Dimension’s DragonFly LDM system for rapid PCB prototyping and New Balance’s smart insoles with embedded electronics. Notable results include the significant volume reduction of 3D-printed inductors in power converters and the successful integration of sensors in smart insoles for real-time biomechanical monitoring. Finally, the paper ends with the technological difficulties as well as the potential applications for 3D-printed electronics in the near future. After reading this review paper, one will have a better knowledge of how the electronics industry may make use of the specialized technology of 3D printing for its applications.

With the increasing awareness of health, using wearable sensors to monitor individual activities and accurately estimate energy expenditure has become a current research focus. However, existing research encounters challenges including low estimation accuracy, a deficiency of frequency domain features, and difficulty in integrating time domain and frequency domain features. To address these issues, we propose an innovative framework called the Dual-Stream Fusion Network (DSFN). This framework combines the Time Domain Encoding (TDE) module, the Frequency Domain Hierarchical-Split Encoding (FDHSE) module, and a Two-Stage Feature Fusion (TSF) module. Specifically, the temporal stream of the framework employs the TDE module to capture deep temporal features that reflect the complex dynamic variations in time-series data. The frequency domain stream introduces the FDHSE module, which extracts frequency domain features using a multi-level, multi-scale approach, ensuring a comprehensive and diverse representation of frequency information. Through this dual-stream architecture, our model effectively learns both time and frequency domain features, addressing the limitations of frequency domain features observed in prior studies. Additionally, we propose the TSF module to fully integrate time and frequency domain features, effectively overcoming the challenge of fusing these two types of features. We conducted experiments on two public datasets, namely the GOTOV dataset (elderly people) and the JSI dataset (young people). Experimental results demonstrate that our method achieves excellent performance across different age groups. Compared to the baseline models, the proposed DSFN significantly improves the accuracy of human energy expenditure estimation.

Postoperative rehabilitation is led by physiotherapists and is a vital program that re-establishes joint motion and strengthens the muscles around the joint after an orthopedic surgery. Modern smart devices have affected every aspect of human life. Newly developed technologies have disrupted the way various industries operate, including the healthcare one. Extensive research has been carried out on how smartphone inertial sensors can be used for activity recognition. However, there are very few studies on systems that monitor patients and detect different gait patterns in order to assist the work of physiotherapists during the said rehabilitation phase, even outside the time-limited physiotherapy sessions. In this paper, we are presenting a gait recognition system that was developed to detect different gait patterns. The proposed system was trained, tested and validated with data of people who have undergone lower body orthopedic surgery, recorded by Hirslanden Clinique La Colline, an orthopedic clinic in Geneva, Switzerland. Nine different gait classes were labeled by professional physiotherapists. After extracting both time and frequency domain features from the time series data, several machine learning models were tested including a fully connected neural network. Raw time series data were also fed into a convolutional neural network.

Covid-19 invaded the world very quickly and caused the loss of many lives; maximum emergency was activated all over the world due to its rapid spread. Consequently, it became a huge burden on emergency and intensive care units due to the large number of infected individuals and the inability of the medical staff to deal with patients according to the degree of severity. Covid-19 can be diagnosed based on the artificial intelligence (AI) model. Based on AI, the CT images of the patient’s chest can be analyzed to identify the patient case whether it is normal or he/she has Covid-19. The possibility of employing physiological sensors such as heart rate, temperature, respiratory rate, and SpO2 sensors in diagnosing Covid-19 was investigated. In this paper, several articles which used intelligent techniques and vital signs for diagnosing Covid-19 have been reviewed, classified, and compared. The combination of AI and physiological sensors reading, called AI-PSR, can help the clinician in making the decisions and predicting the occurrence of respiratory failure in Covid-19 patients. The physiological parameters of the Covid-19 patients can be transmitted wirelessly based on a specific wireless technology such as Wi-Fi and Bluetooth to the clinician to avoid direct contact between the patient and the clinician or nursing staff. The outcome of the AI-PSR model leads to the probability of recording and linking data with what will happen later, to avoid respiratory failure, and to help the patient with one of the mechanical ventilation devices.

Consumer-grade heart rate (HR) sensors including chest straps, wrist-worn watches and rings have become very popular in recent years for tracking individual physiological state, training for sports and even measuring stress levels and emotional changes. While the majority of these consumer sensors are not medical devices, they can still offer insights for consumers and researchers if used correctly taking into account their limitations. Multiple previous studies have been done using a large variety of consumer sensors including Polar^® devices, Apple^® watches, and Fitbit^® wrist bands. The vast majority of prior studies have been done in laboratory settings where collecting data is relatively straightforward. However, using consumer sensors in naturalistic settings that present significant challenges, including noise artefacts and missing data, has not been as extensively investigated. Additionally, the majority of prior studies focused on wrist-worn optical HR sensors. Arm-worn sensors have not been extensively investigated either. In the present study, we validate HR measurements obtained with an arm-worn optical sensor (Polar OH1) against those obtained with a chest-strap electrical sensor (Polar H10) from 16 participants over a 2-week study period in naturalistic settings. We also investigated the impact of physical activity measured with 3-D accelerometers embedded in the H10 chest strap and OH1 armband sensors on the agreement between the two sensors. Overall, we find that the arm-worn optical Polar OH1 sensor provides a good estimate of HR (Pearson r = 0.90, p <0.01). Filtering the signal that corresponds to physical activity further improves the HR estimates but only slightly (Pearson r = 0.91, p <0.01). Based on these preliminary findings, we conclude that the arm-worn Polar OH1 sensor provides usable HR measurements in daily living conditions, with some caveats discussed in the paper.

The development of robust algorithms for human gait analysis are essential to evaluate the gait performance, and in many cases, crucial for diagnosing gait pathologies. This work proposes a new adaptive tool for human gait event detection in real-time, based on the angular velocity recorded from one gyroscope placed on the instep of the foot and in a finite state machine with adaptive decision rules. The signal was segmented to detect 6 events: Heel Strike (HS), Foot Flat (FF), Middle Mid-Stance (MMST), Heel-Off (HO), Toe-Off (TO), and Middle Mid-Swing (MMSW). The tool was validated with healthy subjects in ground-level walking using a treadmill, for different speeds (1.5 to 4.5 km/h) and slopes (0 to 10%). The results show that the tool is highly accurate and versatile for the detection of all events, as indicated by the values of accuracy, average delays and advances (HS: 99.96%, -7.95 ms, and 9.85 ms; FF: 99.48%, -4.95 ms, and 9.35 ms; MMST: 98.26%, - 36.54 ms, and 16.38 ms; HO: 98.87%, -22.71 ms, and 18.62 ms; TO: 95.95%, -6.80 ms, 14.38 ms; MMSW: 96.06%, -3.45 ms; 0.15 ms, respectively). These findings suggest that the proposed tool is suitable for the real-time gait analysis in real-life activities.