This research focuses on enhancing worker safety in environments with high levels of TVOC gases, such as toluene, a neurotoxin. Traditional cartridge replacement methods, based on fixed intervals, lack precision and pose risks. We propose a novel approach using the SGP30 sensor to monitor cartridge effectiveness, aligned with PEL-TWA regulations. Our system triggers alerts when sensor readings exceed 10 ppm for toluene, 35 ppm for carbon monoxide, and 5000 ppm for carbon dioxide. Additionally, we incorporate physiological monitoring via Photoplethysmography (PPG) using the AFE4404 sensor to assess Heart Rate (HR) and Heart Rate Variability (HRV), alongside respiration monitoring with the D6F-P0010AM2 airflow sensor. Data from these sensors is transmitted via low-power Bluetooth to a mobile APP, enabling real-time monitoring of the wearer's condition. In case of cartridge failure or abnormal physiological readings, the APP triggers vibrations on the mask and automatically sends an SOS to the employer's server via UDP protocol, ensuring immediate intervention. This system aims to enhance occupational safety by reducing the risk of accidents and safeguarding worker well-being.

Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of chronic diseases and deaths worldwide. When acute exacerbation of COPD (AECOPD) occurs, the frequency and severity of malignant attacks are highly correlated with the mortality rate. The purpose of this study is to use wearable devices to collect the physiological parameters of patients for early warning and prevention of complications of possible AECOPD attacks in the future. The subjects used wearable devices to measure Heart Rate Variability (HRV) at home. Physiological data and health assessment scales of 13 COPD patients were collected during the 6-month study period. According to the scale responses, the severity of the condition was classified into mild AE and no AE. If the subject needed emergency medical treatment due to COPD, it was classified as AE. With the scale classification method, a machine-learning Random Forest (RF) algorithm is used to predict the occurrence of AECOPD in the next 7 days, so as to prevent the deterioration of the disease in advance. The results of the study show that the accuracy of the model is more than 92% according to different classification methods, and using the mixed-parameter model as a feature for the prediction can improve the sensitivity of the original warning mechanism. In order to provide predictive results to the nursing staff at any time, the user interface of our system would transmit a warning message to remind the nursing staff to ensure early medical intervention for patients to avoid the occurrence of AECOPD.

This paper presents an Ethernet packet transformation and transmission architecture between Modbus transmission control protocol (Modbus/TCP) and universal serial bus (USB) 3.0 developed with a field-programmable gate array (FPGA) development board. The proposed architecture is used to complete packet transformation and transmission between Ethernet and USB 3.0 for application in plant automation. The Ethernet receiver receives and analyzes Modbus/TCP packets and sends the source address, destination address, IP header, and Modbus/TCP header to the register to verify the correctness of the packet. The Modbus/TCP packet is stored in static random access memory and awaits access by a USB 3.0 module. An FPGA development board (Intel DE10-Standard) is used for functional verification. The measured results show that the latency, throughput, and dynamic power are 18.845 ×s, 747.45 Mbps, and 142.17 mW, respectively, at a voltage of 1.8 V and operating frequency of 125 MHz.

This study aims to design and develop a virtual fitness-coach information system for barbell bench press based on deep-learning Long Short Term Memory (LSTM) mechanism and wearable devices. We utilizes a set of three-axis accelerometers, gyroscopes and Electromyography (EMG) sensing modules to design our proposed wearable devices. Through computer and smartphone, the analysis and real-time assessment of the weight training in barbell free bench press can be performed to avoid injury in weight training and improve the quality of training performance.In this study, 21 subjects are recruited to use our proposed wearable devices for weight training in barbell free bench press. In the training, the subject's physiological signals and videos are captured, and the subject's signals are extracted according to the 11 most common kinds of errors marked by the fitness instructor, including 7 posture errors and 4 kinds of muscle force errors. After the extracted signal is normalized, the data is fed for the Recurrent Neural Network (RNN) training through the Long Short Term Memory (LSTM) to classify the weight training errors. The experimental results show that the classification threshold used in the classification has the best classification result when set at 0.5, and the overall average accuracy, accuracy, recall rate, F1 Score, FPR and FNR are 91.84%, 89.25%, 88.17%, 88.18%, 6.50% and 11.83%, respectively. We found that in some categories, because the sensors are not powerful enough to capture the characteristics of the errors, the accuracy is low. While the overall accuracy of the other categories is higher than 85%.In order to accelerate the training speed of LSTM, we also try to use the common factor extraction analysis to reduce the data of accelerometers and gyroscopes from 24 to 18, 12 and 6 dimensions for training. When the total dimension including EMG is 30 dimensions, there is not much difference in the accuracy when the dimension is reduced to 24 or 18. However when it is reduced to 12 dimensions, the evaluation metrics are reduced to below 70%, and the False Negative Rate (FNR) has risen sharply to 30.21%. We therefore choose to reduce the training data from 30 dimensions to 18 dimensions to maintain recognition accuracy and to accelerate LSTM training.To verify the feasibility of our Virtual Fitness-Coach Information System, we have further recruited 5 subjects for user satisfaction survey of the instant voice feedback and our wearable devices. The users show relatively high satisfiaction about our instant feedback system in the following aspects: helpfulness, clearance, reliability, correctness, and performance. The users also feel relatively comfortable for our wearable devices and suggest further simplification of our wearable devices for ease of wearing.

A mobile node localization algorithm based on the Extended Kalman Filter (EKF) along with Radio Signal Strength Index (RSSI) information is proposed in this paper for mobile sensor networks. The localization process is two-fold: the initialization phase and subsequent localization phase. If a node receives the information broadcast from three anchor (or beacon) nodes, it will be localized initially and then enter the subsequent phase. Different from the localization methods using EKF with RSSI requiring three anchor nodes or more, the proposed method estimates the position of a localized node whether or not any anchor node is received. The simulation results indicate that the node localization rate of the proposed method outperforms. Also, more anchor nodes received, more accuracy localization results observed.

Video telephone systems are convenient media for people to communicate remotely in life. If the video can be analyzed in the process of video chat to extract the relevant physiological indicators of the communicators such as emotions or stress, it can help the communicators to make proper conversations. In this paper, we propose (1) Similar Brightness Area Selection Method (2) and Difference-driven Weight-Setting Method to measure the heart rate in the non-contact image mode that can be adapted to the video signal in the videophone. After the image is compressed, it is transmitted through the network. After being transmitted to the video receiver for image decompression, we also propose that the luminance (Y) instead of Green channel (G) of the image should be utilized based on Photoplethysmography (PPG) to infer the heart rate. In our experiment, we have adjusted the weight range of our Difference-driven Weight-Setting Method for four commonly used video compression codecs including H.264, H.265, VP8, and VP9 together with five different compression bit rates and the results have shown to be effective in improving the accuracy of the heart rate measurement. The root mean square error (RMSE) of the heart rate measurement has also been calculated as evaluation metrics in the experiment. The mean and standard deviation of the overall RMSE is 2.14±0.52 bpm (beats per minute) that have shown the feasibility of our proposed methods to effectively and accurately measure the heart rate in the video telephony system.

In this study, we used a microcontroller to develop the blood pressure evaluating devices of dual-PPG and recording two groups of photoplethysmography simultaneously. Cardiac systolic and diastolic change the blood volume and also affect the amount of light reflection. Thus, light sensor will receive the pulse wave changes. After using FIR filtering noise from the signal, the device detects PPG peak and then two groups of PPG can calculate pulse transit time. Multiple regression analysis models for evaluating blood pressure was established by using various parameters such as height, weight, age, and BMI. With this model, we can investigate the feasibility of dual-PPG. The result shows the systolic blood pressure, average deviation and standard deviation is 6.74 ± 8.03mmHg, which closes to the AAMI standard, 5mmHg ± 8mmHg. It means estimating blood pressure using physiological parameters is feasible.

In this study, correlation of heart rate variability (HRV) results in different postures between ECG-based and PPG-based cardiac measurement devices is explored. Electrocardiogram (ECG) is one of the best indicators for the assessment of physical health and heart function, while photoplethysmography (PPG) uses light sensor to detect the change of blood volume in the vessel, and is thus less susceptible to power supply noise and electromagnetic interference. Our wearable prototype Z1 Wristband and commercially available Mio Alpha HR watch are chosen as the target PPG-based devices, while the medical 12-lead Poly-Spectrum-12/E ECG monitor is chosen as the target (benchmark) ECG-based device. The experimental results shows that in different postures, the HRV results of our Z1 prototype are of significant positive correlation with the HRV results of the ECG device (r>0.861, p0.970, p<;0.01) which shows the feasibility of measurement with our Z1 via either right hand or left hand. The performance with the posture with hands in the same height of the heart is slightly better than that with hands drooping.

In this study, we have implemented an auscultation sphygmomanometer that can continuously estimate blood pressure via simultaneous measurement of phonocardiogram (PCG) and photoplethysmography (PPG) signals. In PCG, in the process of systole and diastole, the heart will produce "lub" "dub" sounds, called the first (S1) and the second heart sound (S2) respectively. In PPG, according to systole and diastole, the blood volume in the vessel will change, and thus affect the amount of light reflected from the vessel and received in the photosensitive element to detect the heart pulse wave. After using Finite Impulse Response (FIR) and Discrete Wavelet Transform (DWT) to filter out noise from the signal, the device can detect three features, namely S1, S2 and PPG peak, which can be used to calculate Vascular Transit Time (VTT), Ejection Time (ET), and Heart Rate (HR). Multiple regression analysis model for estimating blood pressure is established by using various cardiac parameters such as VTT, ET, HR, and individual's physiological parameters such as gender, age, height, and weight. With this model, auscultation blood pressure measurement can be designed. The results of this study show that for the range of normal blood pressure, the error in systolic blood pressure is 6.67 ± 8.47 mmHg, which is very close to the AAMI standard, 5 ± 8 mmHg. This shows the feasibility of estimating blood pressure using various cardiac and physiological parameters via PCG and PPG measurement.

Taiwan government has recently promoted “Plan for Building Island of Exercise” which has leaded the trend of exercises and increased the needs of various healthcare and fitness products. Prior researches have also pointed out that listening to music with different tempos while exercising apparently affects heart rate, oxygen consumption, breathing rate and other physiological parameters. Among the physiological parameters, heart rate (HR) best represents the current body status while heart rate recovery (HRR) can also represent the physical fitness condition since HRR can be used to predict death risk.

In this paper, we propose to integrate fingerprint and ECG authentification implemented on wearable devices for personal identify. The identification rate of fingerprint authentication is 96%, ECG authentication with fixed threshold method and variable threshold method are 92.6% and 95.9% respectively. We have combined fingerprint and ECG authentication as a novel multiple authentication method. Meanwhile, we have reduced the complexity of algorithms and used less ECG wave range in order to implement on wearable devices. The identification rate has been up to 92% to achieve high accuracy and high security goal using wearable devices.

With the evolution of technology and growth of economy, people's living style has gradually changed from Active-lifestyle into Sedentary-lifestyle. According to statistics, the ratio of students engaging in regular exercise has been declined with the increase in education. Under the long-term shortage of exercise, teenagers usually cannot develop regular exercise habits as soon as possible which has indirectly lead to the decrease in age of having chronic cardiovascular diseases. It is thus our research motivation and goal to build a system to allow users such as teachers or coaches to easily monitor a group's exercise condition, intensity and duration. Also, our system should be able to measure in a convenient way, to allow users to move around freely, to promote exercise efficiency, to improve exercise motivation and to even reduce the risk of exercise. Our research proposes an assisting system for teachers or coaches. Based on group measurement concept, using wearable chest strap textiles integrated with heart rate monitoring device, teachers or coaches can immediately receive and display all heart rate information on a notebook computer together with synchronous field projection. In the experiments the subjects are divided into "the varsity group" with regular exercise and "the sedentary group" without regular exercise habit. Subjects wearing chest strap were instructed to take five-minute constant intensity exercise test. The results show that "the varsity group" has lower resting heart rate, lower exercise heart rate and lower mean heart rate. Therefore, our wearable heart rate monitoring system is indeed effective in measurement of group heart rate and in assessment and comparison of cardiorespiratory fitness.

Elderly dementia patients often get lost due to lack of a sense of direction. This may put them at risk and cause their families much worry. The goal of this study is to use information technology to enhance the professional judgment of caregivers, strengthen internal safety monitoring at care organizations, and improve the quality of care for dementia patients. An eXtensible-Markup-Language-based dementia assessment system combining program code and assessment content is used to provide caregivers with better flexibility and real-time response ability. Beyond establishing long-term case files, the system can also perform data consistency analysis, strengthen caregivers' continuing education, improve caregivers' case judgment skills, and reduce the incidence of accidents due to neglect. This study also applies radio frequency identification (RFID) technology to the development of an indoor and outdoor active safety monitoring mechanism. The system can automatically remind caregivers whenever an elderly person approaches a dangerous area or strays too far. Apart from the use of different size tags, the realization of the system also employs the tame transformation signatures (TTS) algorithm to encrypt tag IDs and protect personal privacy. Clinical testing of the system showed that the indoor RFID reader has a response time of 0.5 s when sensing 40 tags, while the outdoor reader has a sensing time of approximately 5 s due to the need to save power. In the latter case, the system can ensure that elderly patients stay less than 15 m away from their caregivers. Patients were relatively willing to wear light tags. We also found that irritable patients with strong mobility were less compliant and often removed their own tags. Caregivers must provide active care and adopt various safety measures to protect the type of patients.

Hypertension and arrhythmia are chronic diseases, which can be effectively prevented and controlled only if the physiological parameters of the patient are constantly monitored, along with the full support of the health education and professional medical care. In this paper, a role-based intelligent mobile care system with alert mechanism in chronic care environment is proposed and implemented. The roles in our system include patients, physicians, nurses, and healthcare providers. Each of the roles represents a person that uses a mobile device such as a mobile phone to communicate with the server setup in the care center such that he or she can go around without restrictions. For commercial mobile phones with Bluetooth communication capability attached to chronic patients, we have developed physiological signal recognition algorithms that were implemented and built-in in the mobile phone without affecting its original communication functions. It is thus possible to integrate several front-end mobile care devices with Bluetooth communication capability to extract patients' various physiological parameters [such as blood pressure, pulse, saturation of haemoglobin (SpO2), and electrocardiogram (ECG)], to monitor multiple physiological signals without space limit, and to upload important or abnormal physiological information to healthcare center for storage and analysis or transmit the information to physicians and healthcare providers for further processing. Thus, the physiological signal extraction devices only have to deal with signal extraction and wireless transmission. Since they do not have to do signal processing, their form factor can be further reduced to reach the goal of microminiaturization and power saving. An alert management mechanism has been included in back-end healthcare center to initiate various strategies for automatic emergency alerts after receiving emergency messages or after automatically recognizing emergency messages. Within the time intervals in system setting, according to the medical history of a specific patient, our prototype system can inform various healthcare providers in sequence to provide healthcare service with their reply to ensure the accuracy of alert information and the completeness of early warning notification to further improve the healthcare quality. In the end, with the testing results and performance evaluation of our implemented system prototype, we conclude that it is possible to set up a complete intelligent healt care chain with mobile monitoring and healthcare service via the assistance of our system.

To assist in the inspection of sleep-related diagnosis and research, an adaptive method for processing long-term polysomnography (PSG) is proposed in this paper. The extracted features of segmented PSG based on wavelet analysis can be used for clustering the segments with similar pattern into a group. The adaptive fuzzy clustering was used to estimate the clusters within the PSG recordings, the optimal number of clusters and the optimal features of an individual subject. The novel method with the adaptive-to-subject concept exhibits four advantages in comparison with other approaches: 1) full automated, 2) adaptive to the diversity of physiological signals among subjects, 3) less sensitive to noise and artifacts, and 4) effective visualization of analysis results for clinicians. The simulation results show the superiority of the proposed method in long-term PSG analysis

The purpose of this paper is to integrate the technologies of radio frequency identification, global positioning system, global system for mobile communications, and geographic information system (GIS) to construct a stray prevention system for elderly persons suffering from dementia without interfering with their activities of daily livings. We also aim to improve the passive and manpowered way of searching the missing patient with the help of the information technology. Our system provides four monitoring schemes, including indoor residence monitoring, outdoor activity area monitoring, emergency rescue, and remote monitoring modes, and we have developed a service platform to implement these monitoring schemes. The platform consists of a web service server, a database server, a message controller server, and a health-GIS (H-GIS) server. Family members or volunteer workers can identify the real-time positions of missing elderly using mobile phone, PDA, Notebook PC, and various mobile devices through the service platform. System performance and reliability is analyzed. Experiments performed on four different time slots, from three locations, through three mobile telecommunication companies show that the overall transaction time is 34 s and the average deviation of the geographical location is about 8 m. A questionnaire surveyed by 11 users show that eight users are satisfied with the system stability and 10 users would like to carry the locating device themselves, or recommend it to their family members.

This paper introduces a novel scheduling problem called the active interval scheduling problem in hierarchical wireless sensor networks for long-term periodical monitoring applications. To improve the report sensitivity of the hierarchical wireless sensor networks, an efficient scheduling algorithm is desired. In this paper, we propose a compact genetic algorithm (CGA) to optimize the solution quality for sensor network maintenance. The experimental result shows that the proposed CGA brings better solutions in acceptable calculation time.

To solve the inconvenience of routine transportation of chronically ill and handicapped patients, this paper proposes a platform based on a hybrid fiber coaxial (HFC) network in Taiwan designed to make a home telecare system feasible. The aim of this home telecare system is to combine biomedical data, including three-channel electrocardiogram (ECG) and blood pressure (BP), video, and audio into a National Television Standard Committee (NTSC) channel for communication between the patient and healthcare provider. Digitized biomedical data and output from medical devices can be further modulated to a second audio program (SAP) subchannel which can be used for second-language audio in NTSC television signals. For long-distance transmission, we translate the digital biomedical data into the frequency domain using frequency shift key (FSK) technology and insert this signal into an SAP band. The whole system has been implemented and tested. The results obtained using this system clearly demonstrated that real-time video, audio, and biomedical data transmission are very clear with a carrier-to-noise ratio up to 43 dB.